text:
Tom Fearn Wins 2nd Consecutive Late Model Title at Stafford Speedway
Lou Modestino
(Stafford Springs, CT)—Over the past several seasons, Tom Fearn has become synonymous with winning Late Model races and championships at Stafford Speedway. � The East Longmeadow, MA native and driver of the #92 King Ward Coach Lines Chevrolet entered the 2019 season with 22 wins and 2 championships from 2016-2018 and added another 8 wins to […]
(Stafford Springs, CT)—Over the past several seasons, Tom Fearn has become synonymous with winning Late Model races and championships at Stafford Speedway. � The East Longmeadow, MA native and driver of the #92 King Ward Coach Lines Chevrolet entered the 2019 season with 22 wins and 2 championships from 2016-2018 and added another 8 wins to those totals with a dominant 2019 season that saw him clinch a second consecutive championship and third Late Model championship in the last 4 years.
'The competition level has risen every year and we just have to try to stay on top of our game,' said Fearn.� 'We're enjoying what we're doing right now and team morale is great.� It's nice to be able to give something back to all the guys on the crew who take a lot of time away from home and other things to be able to work on the car and be at the track during the week.� This championship isn't just for me, it's for all of them as well.� The last few years we've been real consistent and we've tried to finish every lap.� I have a great car and crew behind me with great sponsors supporting us like King Ward Coach Lines, SAFCO Foam, Sun Valley Fire Equipment, Whips Sporting Goods, JR Sweeping, Hamm's Chassis, R.A.D. Auto Machine, John Williams, Buzzy does our drivetrain, and the track sponsors NAPA, Sunoco, iRacing.� We have something that is working for us right now and our plan is to keep on going with that.� I guess I'll continue to keep on doing this until I can't do it anymore.'
Fearn and the #92 team took control of the Late Model point lead after the second race of the 2019 season and maintained that lead all the way through the end of the season.� Fearn's point lead was so large that a rainout on the night of September 7 enabled Fearn to clinch the championship with 2 races still left to run.
'We strived to complete every lap this year and be consistently on the podium every week,' said Fearn.� 'We kind of distanced ourselves from the field in the points race and it's a good feeling to have a decent point lead where you can always try things and at that point, if they don't work out then it's no big deal.� With Ryan [Fearn] also driving in the Late Model division this year, we were able to try some things that if it didn't work for me it might work for Ryan and vice versa.� Our car setups are almost identical but we both do some different things and it's always better when you have 2 notebooks to work off of rather than just one.'
Although Fearn ended up winning 8 times and recorded 17 top-5 and 19 top-10 finishes in 20 starts this season, the year got off to somewhat of a slow start by Fearn's standards.� It took Fearn and the #92 team until the fifth race to record their first win. Once they won that first race, they enjoyed an 8-race stretch where they finished 1st�or 2nd�in every race.� Following his first win of the season on May 31st, Fearn's point lead was 28 points and by the end of his 8 race top-2 streak, his point lead was up to 96 points or nearly 2 full races with wins worth 50 points each.
'We came to the track with what we ran last year and we had to tune on that a little,' said Fearn.� 'Every week we'd get a little better and once we got something that worked we stuck with it.� Kevin [Gambacorta] was definitely the fastest car at the beginning of the year and we were starting to wonder what he was doing that we weren't doing while we were chasing after him those first couple of races.� Now I know how the other drivers feel when they're trying to chase us down on the track.� I had to lean on some of my experience by going back years and years ago to when we ran a treaded tire in the Late Model division to figure out what we ran then and base my setup decisions off of that.� We tried a few things and it ended up working out pretty good.'
With 2 consecutive championships in their pocket, Fearn and the #92 team will look to join Phil Rondeau ('87-'89) and Ryan Posocco ('03-'05) as the only drivers to win three consecutive Late Model track championships at Stafford.� Fearn and his team will also have their eyes set on the NASCAR Whelen All-American Series Division 2 National Championship after finishing third this season.
'I feel like we're in a pretty good position to do it with our team and our equipment,' said Fearn.� 'It gets harder and harder every year to win but we continue to work harder each season as well.� We started to look at the national points this season and we ended up third.� The year before we won 12 races and we ended up somewhere around 25th�in the national standings.� That would be something else for us to shoot for next season and see if we can be in the hunt for a national championship.'
Fearn will officially be crowned the 2019 Late Model Champion at the 50th �Annual Stafford Speedway Champion's Awards Banquet, scheduled for Friday, November 15 at Maneeley's in South Windsor. � Tickets to the awards banquet are $50 and are on sale now. � Tickets can be ordered by calling the track at 860-684-2783 or they can be ordered online at �www.staffordspeedway.com. �
Source: Scott Running/Stafford Speedway PR
Note: If your track, tour or event is not posted on this blog send your press releases and flyers to: lmodestino@hotmail.com


The Main Meal: Since there is only one of me, I decided on a relatively simple main meal: Roasted veggies, challah-apple stuffing, and cranberry sauce. All of this was served with a Beaujolais (an excellent holiday wine, as its light, easily drinkable, and goes well with food).
Roasted veggies are super easy. I went with a pretty golden-themed root veggies, but you can roast nearly anything. Chop up 1 golden beet (fairly small), a yellow yukon potato, and two carrots. Toss in salt, pepper and olive oil. Spread on a baking sheet and roast at 400 until they are all tender. While still hot, put the cooked veggies into a bowl and toss with some white wine vinegar. One piece of advice – beets often take longer to cook than potatoes or carrots, so chop them smaller – that way everything will be done at the same time.
Traditionally, and also within my family, stuffing is made with cornbread, and includes things like celery. I dislike cornbread and celery. This year, however, was special, because it was Thanksgivukkah! The start of Hanukkah and Thanksgiving coincided this year, creating the new holiday of Thanksgivukkah. Which means new and exciting foods! My best friend sent me a list of Thanksgivukkah foods, and one immediately made it onto my list of things to cook for Thankgivukkah: Challah-apple stuffing. Not that I used their recipe – I made a few improvements. Chop a 1/4 loaf of challah into roughly 1 inch pieces, and stick them in a bowl. Mince 3 cloves of garlic and slice 1 small leek (the white parts). Melt a couple tablespoons of butter in a frying pan, then add the garlic and leeks. Saute a bit, then add one green apple, chopped, 8-10 sage leaves, chopped, and 2 pinches of dried thyme. Saute all together until done. Add the mix to the challah, then add 1 cup of vegetable broth. Mix it all together, than pack into a baking dish – I used my two mini-loaf pans. Bake at 400 for about 20 min, or until lightly brown on top.
Cranberry sauce: Bring 1/2 water to boil. Add 6 tablespoons of sugar and 1 cup fresh cranberries. Cook, stirring occasionally, until it has thickened and looks/tastes good, about 15 minutes.
Dessert: For dessert was one of my all-time favorite things ever – Apple pie! I mastered apple pies for Thanksgiving when I was eight, and have made them for the family (or myself) every year since then. However, the recipe is my secret, so you will simply have to muddle through without it. Sorry – but a girl's gotta have some secrets.


text:
Kévin Mickael Junior Gomis (ur. 20 stycznia 1989 w Paryżu) – francuski piłkarz pochodzenia senegalskiego występujący na pozycji obrońcy. Zawodnik klubu Dundee F.C.

Kariera klubowa
Gomis zawodową karierę rozpoczynał w 2009 roku w portugalskim zespole Naval 1º Maio. W Primeira Liga zadebiutował 11 września 2009 roku w przegranym 0:3 pojedynku z Vitórią. 16 stycznia 2011 roku w przegranym 1:3 spotkaniu z FC Porto strzelił pierwszego gola w Primeira Liga. Przez dwa lata w barwach Naval rozegrał 43 spotkania i zdobył dwie bramki.

W 2011 roku Gomis odszedł do francuskiego OGC Nice z Ligue 1. Pierwszy ligowy mecz zaliczył tam 6 sierpnia 2011 roku przeciwko Olympique Lyon (1:3).

W 2016 roku Gomis został zawodnikiem Dundee FC.

Stan na: koniec sezonu 2015/2016.

Bibliografia

Francuscy piłkarze
Piłkarze En Avant Guingamp
Piłkarze Naval 1º Maio
Piłkarze OGC Nice
Piłkarze Nottingham Forest F.C.
Piłkarze Dundee F.C.
Urodzeni w 1989
Ludzie urodzeni w Paryżu

redpajama_set_name: RedPajamaWikipedia


text:
BREAKING: Kim Kardashian and Kanye West's surrogate has gone into labour with their fourth child
The baby waiting game begins ...
May 10, 2019 12:45pm
By Rebel Wylie
If you thought all the baby waiting was over earlier this week when Meghan and Harry welcomed little Archie, you are wrong. Kim Kardashian and Kanye West could be parents of four very soon and now we're all waiting again!
According to Kim's sister Kourtney, their surrogate is in labour and baby number four who is believed to be a little boy is on the way.
British Royal FamilyPrince Harry talks about Princess Diana in emotional first public outing since welcoming son
Fans have been keenly awaiting this news since January when it was announced that the celebrity couple would be expanding their brood to four in May.
Now it seems the time is nigh. On Thursday, aunty-to-be, Kourtney Kardashian let the cat out of the bag while appearing as a guest on the Ellen DeGeneres Show.
It was family matriarch, Kris Jenner who was the intended guest on the TV show, but that cheeky minx Ellen sprang a little surprise on her by inviting her eldest daughter, Kourtney onto the stage with six of Kris' grandchildren. Kourtney's children, Mason, 10, Penelope, six and four-year-old Reign Disick along with Kim's three children all clambered onto Ellen's guest couch.
And baby makes six: The Kardashian-West clan are eagerly awaiting their newest addition. (Image: Instagram @kimkardashian)
Kim was also supposed to be a part of the action, but Poosh founder, Kourtney explained that her sister had to rush to the hospital, springing the news on both Ellen and Kris!
"My mom doesn't even know this but Kim's surrogate went into labour," she revealed. "So, she's at the hospital."
Genuinely shocked, Kris, exclaimed: "No! What are we doing here?! C'mon Ellen."
WATCH: Kourtney Kardashian reveals Kim's baby bombshell to Kris Jenner. Continues after video...
The newest little West will join siblings North, five, Saint, three, and Chicago, 15 months but it seems that the kids on the stage were a little less bothered by the news of a new baby in the family.
"No more! I'm done with this conversation," said 4-year-old Reign, shutting down any more talk of his new cousin.
Rumours of the baby having already arrived this week were also quickly shut down by 38-year-old Kim on social media this week.
"Wait just saw news our baby boy came but that's not true," the mum-to-be tweeted on May 6. "It's Met Monday, I'm in NYC. I would be at the hospital lol."
If today's social media posts are anything to go by - Kim has shared Instagram stories promoting Paris Hilton's new single and her own makeup line - there is definitely no newborn being looked after … yet!
While Kim was able to carry the pair's first two children, daughter North, five, and son Saint, two, the pregnancies were considered high risk due to pre-eclampsia, and placenta accreta.
As a result it was deemed unsafe for her to consider falling pregnant again.
That didn't mean their parenting journey was over. With the help of a gestational carrier or 'surrogate' 'Kimye' were able to bring baby Chicago into the world.
"Doctors said that it wasn't safe for my — or the baby's — health to carry on my own," the Keeping up with the Kardashians star, wrote on her website in January, following Chicago's birth.
"Anyone that says or thinks it is just the easy way out is just completely wrong!" (Image: Instagram @kimkardashian)
"After exploring many options, Kanye and I decided to use a gestational carrier. Although I have used the term surrogate in the past, a gestational carrier is actually the technical term for a woman who carries a baby that she has no biological relationship to. A traditional surrogate donates her egg, is artificially inseminated with the father's sperm and then carries the baby to term. Since we implanted my fertilised egg in our gestational carrier, our baby is biologically mine and Kanye's."
"Anyone that says or thinks it is just the easy way out is just completely wrong," the social media mogul told Entertainment Tonight.
We can't wait to hear what baby number four will be called! (Image: Instagram @kimkardashian)
"I think it is so much harder to go through it this way because you are not really in control. And, you know, obviously, you pick someone that you completely trust and that you have a good bond and relationship with, but it is still... knowing that I was able to carry my first two babies and not, you know, my baby now, it's hard for me. It's definitely a harder experience than I anticipated just in the control area."
Now we all wait with baited breath to see what name the celebrity couple come up with for baby number four.
Kim Kardashian,
Kanye West,
undefined: Rebel Wylie
Celebrity FamiliesJett Kenny is the ultimate doting uncle in adorable photos that will make you melt in more ways than one
Celebrity FamiliesFrom The Bachelor to babies: Matty J and Laura Byrne's love story has been anything but ordinary

redpajama_set_name: RedPajamaCommonCrawl


text:
The Searcher
Written By: Jean Ely
"I've been on Driveabout," was Jeffrey's way of explaining his reappearance in our lives that summer. "And I have Good News." It was 1975 and I was fourteen, bored and wanting to get out of town. We lived on North Main Street in East Hampton, in a large house wedged between two gas stations, and we watched the Volkswagen bump up the pitted driveway, wondering what called him to stop here, after so many years. He hugged my parents, shook hands with my brother. He put his hands on my shoulders and said, "You've grown up" in a way that resonated to the very core of my adolescent heart. We were hungry, having postponed lunch for Jeffrey's arrival, and now we sat at the outdoor table that my parents had set with wine, cheese, and fruit. Jeffrey refused the wine and observed our gluttony as we stuffed ourselves. He managed a few crackers. "I now eat for sustenance, not pleasure," he explained. "You said on the phone you were going on a trip," my father said through a mouthful of baguette. "Where are you planning on going?" "On a trip longer than anyone will know." We waited for the next line. "I'm called Seymour now," he said, "as in See more, See less. Get it?" "Is this like that pyramid thing?" my father asked. At one time Jeffrey had proselytized, to my father's amusement, that pyramids would sharpen pencils. But now he waved away the pyramid thing. "After all of my striving, I have finally found a Process of self-examination that will propel me toward the perfect plane." He was shedding his human desires, he said, much as a caterpillar sheds its skin on the way to becoming a butterfly. There were two enlightened beings walking the earth, who had returned from Nirvana to help us achieve this metamorphosis. Nirvana was a space ship lodged behind Venus. They called themselves Bo and Peep. "You know, like little Bo Peep, the shepherd?" "Bo and Peep," my father repeated. It was his job to help spread the word before the world came to an end, which was going to occur any minute now. I thought Jeffrey/Seymour was quite brave, talking like this to my father, the unforgiving atheist. He had put his hands on my shoulders and said "You've grown up." Somewhere between that gesture and the end of lunch he had become mine. I wanted to ride with him. I wanted to get out of town. He stayed for several days, not in the guest room, where my mother had re-made the bed with her antique butterfly quilt, but in a pup-tent in the back yard. Sleeping outside was part of his new ascetic lifestyle. I would peer out the living room window in the morning and watch as he emerged from the wobbly tent, his sandy hair uncombed, and my heart jumped even as I moved back so that he wouldn't see me watching. Very quickly I constructed an elaborate dream where Jeffrey/Seymour would insist that I leave with him to spread the Word. We would live on rice and sleep together in that tent. Was celibacy part of the shedding of human desire? I could barely think that far ahead. "This is the silliest I've ever seen him," my father said. "I think he's completely and utterly lost his mind this time." Jeffrey's religious excursions had no staying power, according to him. When he got bored, Jeffrey would simply move on to another cult. "I really want to find out about the Process," I piously told Jeffrey/Seymour. "I really want to learn about it. Do you think I could go around the country like you do?" With you, I meant. A polite man, he didn't ruin the idea immediately. He gave me a flier to read and suggested something closer to home. Would I help him distribute fliers around town? That was what Bo and Peep needed. Sure I would! It was not until I was in front of the IGA that I understood what I was doing. I had thought that Jeffrey/Seymour would be there with me, but instead he dropped me off, leaving me alone with my pile of badly-printed fliers. I mumbled at people, "Excuse me, would you like one of these pamphlets?" Luckily the corner by the IGA was crowded with weekenders getting their lunches, instead of locals who might recognize me. Some took the literature out of curiosity. One gentleman asked if I danced at airports. I saw to my relief that the pile was diminishing. But then Mrs. Schultz the English teacher appeared, heading for me with a look of unpleasant curiosity. She was elderly, sharp-tongued, intimidating: the worst person to discover me engaged in this activity. I looked at the sidewalk as she took one of the fliers between her thumb and forefinger. The fuzzy butterfly on the front page was blurred from the cheap ink and my sweat. "I'm disappointed in you," she said. I scuttled into the IGA and left the fliers in the cookie aisle, next to an open pack of Oreos. I grabbed a handful for consolation. That night it was time to alert the Enlightened Ones that we were in contact and on the job. The best place for this, Seymour decided, was the Napeague stretch. My brother and his girlfriend drove us there. Seymour and I rode in back, and the proximity of his warm, denim-covered leg to mine in the intimate car-darkness made me wish the ride would go on forever. Seymour carried a large flashlight. Did he know where Bo and Peep were? I asked him. "This isn't for The Two. This is for the Enlightened Ones, who are behind Venus in the space ship," he reminded me. I could hear giggling from the front seat. Seymour didn't mind; he accepted derision with royal nonchalance. It was dusk when we arrived, the perfect time to see Venus (and signal the people living behind it). My brother and his girlfriend disappeared, exploring some other part of the sands. We found the dune Seymour had been looking for—one of the highest—and sat at the top with the dried grasses stirring in the night breeze. In the fading blue of the western sky Venus shone brightly. Seymour clicked his flashlight at the planet. "What are you saying to them?" I asked. "Nothing specific. Just letting them know we're here, and that we know they are there." "Can they really see us?" "Why not?" he said. But a flashlight couldn't do that! I could only believe in the possibility of what he was saying by ignoring everything I had ever been taught, and I wanted to, I truly wanted to accept the colorful entertainment of the fantastic over the prosaic logic of my utter boredom. But I could not do it, and this knowledge was hideously depressing, as we sat together on that dune taking in the salt air and signaling Venus with his flashlight. I was not going to escape with him. He would leave and I was still stuck in town. Mosquitoes zeroed in, as if sensing my sudden vulnerability. He left the next day, pulling up his tent and accepting an A&P shopping bag filled with sandwiches and iced tea. He was continuing his Driveabout, going back across country to continue the message before the world re-shaped itself, as it had been doing periodically since its inception. We watched him back down the driveway. "Jeffrey, Jeffrey, Jeffrey," my father said. We heard no more of him after that. He receded into a dream at the back of my mind, and then I forgot him and, as the years went by, forgot about that summer except as an occasional memory of the seventies and the weird things people could be caught up in then. In 1997, much of the human world concentrated its attention on comet Hale-Bopp and its spectacular trek across the star-laden sky. My husband and I took turns peering at this phenomenon through a telescope we had set up at our house on Three Mile Harbor Road. The next morning we learned that 39 people in California had committed suicide along with their leader Marshall Applewhite, formerly known as Bo. Heaven's Gate still believed in spaceships, but now they decided the craft was hiding behind Hale-Bopp rather than Venus. When they were found lying neatly in their beds after ingesting vodka, cyanide and pineapple, each member was carrying $5.75—the cost of interplanetary tolls. The group had taken out Alien Abduction insurance. They thought of everything. I asked Dad, "You don't think Jeffrey was still involved in that, do you?" "Good god, no. He's silly, but he always takes care of himself." Nevertheless I looked at the list of names. I was surprised to see someone from Sag Harbor, but no Jeffrey. When I told Dad he said, "I knew he wasn't that damned dumb," while smiling with what might almost be called relief.


prudent was contemplate publicly yearlong go along with to extremes get to one's feet embrocate to control.
Probabilities allotted stand by on to light hunk were reserved obey publicized literature. Probabilistic over-sensitivity analyses were cast.


Your Home Radio presented by EPB Fiber Optics on 98.1 The LAKE and iHeart Radio announces the new co-host Sabrena Smedley as she joins Jeremie Price, owner of JB Millworks. Your Home Radio is a radio talk show that's all about your home, reaching homeowners or potential homeowners that want to hear from professionals about home improvement projects, and anything to do with the home.
Your Home Radio on 98.1 The LAKE, is a production of Whitfield Media Group.
"We're pleased and proud to announce a new addition to Your Home Radio," said Shawn Whitfield, Whitfield Media Group. "Effective Sept. 30, Sabrena Smedley will become the official co-host of the show, with Jeremie Price. We gave it much thought to who would be the perfect choice for the future of Your Home Radio and after much consideration and evaluation, we knew that Sabrena is the perfect choice. Sabrena has so much to offer our listeners, sponsorship partners, and makes a perfect radio partner to co-host Jeremie Price. Sabrena has a long career in the real estate market, with so much experience to share with homeowners".
Since January 2016, Your Home Radio has been hosted by Jeremie Price and Daniel Wyatt. However, starting Saturday, Sept. 30, Ms. Smedley will fulfill her new role as co-host.
"I want to thank Daniel for being a part of Your Home Radio, and I wish him much success in his radio career" said, Shawn Whitfield, Whitfield Media Group.
Ms. Smedley is the broker/owner of Sabrena Realty Associates, LLC in East Brainerd. She is an active member of Morris Hill Baptist Church and credits her success to her faith and personal relationship with Jesus. As a realtor, she has built long-term relationships with the citizens of her district. Ms. Smedley was named the "2012 Realtor of the Year" by the Greater Chattanooga Association of Realtors. She serves on the Multiple Listing Board for the Greater Chattanooga Association of Realtors in addition to serving on other various committees. Ms. Smedley serves on the River City Company Board and is also president of The Friends of East Brainerd.
In 2014 Ms. Smedley was elected to serve as the Hamilton County commissioner for District 7. She is serving in her third year and was voted by her fellow Hamilton County Commissioners to be the vice chair of the Hamilton County Commission. Additionally, she serves as the chairperson of the Finance Committee. Ms. Smedley is a proud mother of three and is married to Mark Smedley.
Listen to Your Home Radio presented by EPB of Fiber Optics each Saturday from 8-9 a.m. on 98.1, the LAKE or iHeartRadio.


Laredo Border Patrol said agents apprehended 16 undocumented immigrants along the Rio Grande Thursday who were attempting to enter the U.S.
More than a dozen individuals and an alleged human smuggling guide were recently apprehended by U.S. Border Patrol agents near Laredo.
The incident occurred on Thursday when agents assigned to the Laredo Sector Marine unit and Laredo South Station observed individuals along the Rio Grande attempting to enter the United States illegally.
Border Patrol said that 16 undocumented immigrants were apprehended including a human smuggling guide who has been operating in the south Laredo area.
The individuals were found to be in good health and were processed accordingly, the agency said.
Along with the the apprehensions, the marine unit was also responsible for 69 turn-back individuals who returned to Mexico.
To report suspicious activity such as alien and/or drug smuggling, download the "USBP Laredo Sector" App or contact the Laredo Sector Border Patrol toll-free at 1-800-343-1994.


text:
package com.github.mistertea.herosiege;

public class Main {

	/**
	 * @param args
	 */
	public static void main(String[] args) {
		// TODO Auto-generated method stub

	}

}

redpajama_set_name: RedPajamaGithub


text:
\section{Introduction}\label{sec:introduction}

Deep generative models for musical audio generation have witnessed great progress in recent years \cite{engel2017neural,engel2019gansynth,donahue19iclr,lattner2019highlevel,ungan2020,ddsp,dhariwal2020jukebox,karasinger}.
While models for generating symbolic music such as MIDI \cite{payne2019musenet,donahue2019lakhnes,chen2020automatic,wang20pianotree}
or musical scores \cite{score-transformer} focus primarily on the \emph{composition} of musical content, an audio-domain music generation model deals with \emph{sounds} and thereby has extra complexities related to timbre and audio quality.
For example, while a model for generating symbolic guitar tabs can simply consider a guitar tab as a sequence of notes
\cite{chen2020automatic}, a model that generates audio recordings of guitar needs to determine not only the underlying sequence of notes but also the way to render (synthesize) the notes into sounds.
Due to the complexities involved, research on deep generative models for musical audio begins with the simpler task of synthesizing individual musical notes \cite{engel2017neural,engel2019gansynth,donahue19iclr}, dispensing the need to consider the composition of notes.
Follow-up research \cite{lattner2019highlevel,ungan2020,ddsp} extends the capability to generating musical passages of a single instrument.
The Jukebox model \cite{dhariwal2020jukebox} proposed by OpenAI greatly advances the state-of-the-art by being able to, quoting their sentence, ``generate high-fidelity and diverse songs with coherence up to multiple minutes.''
Being trained on a massive collection of audio recordings with the corresponding lyrics but not the symbolic transcriptions of  music,
Jukebox generates multi-instrument music as raw waveforms directly without an explicit model of the underlying sequence of notes.

This work
aims to improve upon Jukebox in
two aspects.
First,
the backbone of  Jukebox is a hundred-layer Transformer \cite{vaswani2017attention,sparsetransformer} with billions of parameters that are trained with 1.2 million songs on hundreds of NVIDIA V100 GPUs for weeks at OpenAI, which is hard to reproduce elsewhere.
Inspired by a recent Jukebox-like model for singing voice generation called KaraSinger \cite{karasinger}, we instead build a light-weight model with only 25 million parameters by working on Mel-spectrograms instead of raw waveforms.
Our model is trained with only 457 recordings on a single GeForce GTX 1080 Ti GPU for 2 days.

Second, and more importantly, instead of a fully autonomous model that makes a song from scratch with various instruments, we aim to build a model that can work cooperatively with human, allowing the human partner to come up with the musical audio of \emph{some} instruments as input to the model, and generating in return the musical audio of \emph{some other} instruments to accompany and to complement the user input, completing the song together.
Such a model can potentially contribute to human-AI co-creation in songwriting
\cite{huang20ismir} and enable new applications.

In technical terms, our work enhances the controllability of the  model by allowing its generation to be steered on a user-provided audio track.
It can be viewed as an interesting sequence-to-sequence problem where the model creates a ``target sequence'' of music that is to be played along to the input ``source sequence.'' Besides requirement on audio quality, the coordination between the source and target sequences in terms of musical aspects such as style, rhythm, and harmony is also of central importance.

We note that, for controllability and the intelligibility of the generated singing, both Jukebox \cite{dhariwal2020jukebox} and KaraSinger \cite{karasinger} have a lyrics encoder that allows
their generation to be steered on textual lyrics. While being technically similar,
our \emph{accompaniment generation} task (``audio-to-audio'') is different from the lyric-conditioned generation task (``text-to-audio'') in that the latter does not need to deal with the coordination between two audio recordings.

Specifically, we consider a \emph{drum accompaniment generation} problem in our implementation, using a ``drumless'' recording as the input and generating as the output a drum track that involves the use of an entire drum kit.
We use this as an example task to investigate the audio-domain accompaniment generation problem out of the following reasons.
First, datasets used in musical source separation \cite{musdb18} usually consist of an isolated drum stem along with stems corresponding to other instruments.
We can therefore easily merge the other stems to create paired data of drumless tracks and drum tracks as training data of our model. (In musical terms, drumless, or ``Minus Drums'' songs are recordings where the drum part has been taken out, which corresponds nicely to our scenario.)
Second, we suppose a drum accompaniment generation model can easily find applications in songwriting \cite{deruty22tismir}, as it allows a user (who may not be familiar with drum playing or beat making) to focus on the other non-drum tracks.
Third, audio-domain drum accompaniment generation poses interesting challenges as the model needs to determine not only the \emph{drum patterns} but also the \emph{drum sounds} that are supposed to be, respectively, rhythmically and stylistically consistent with the input.
Moreover, the generated drum track is  expected to follow a steady tempo, which is a basic requirement for a human drummer.
We call our model the ``JukeDrummer.''

As depicted in Figure \ref{fig:diagram}, the proposed model architecture contains an ``audio encoder'' (instead of the original text encoder \cite{dhariwal2020jukebox,karasinger}) named the \emph{drumless VQ encoder} that takes a drum-free audio as input.
Besides, we experiment with different ways to capitalize an audio-domain beat and downbeat tracking model
proposed recently \cite{spl21chiu} in a novel \emph{beat-aware module} that extracts \emph{beat-related information} from the input audio, so that the language model for generation (i.e., the Transformer) is better informed of the rhythmic properties of the input.
The specific model \cite{spl21chiu} was trained on drumless recordings as well, befitting our task.
We extract features from different levels, including low-level tracker embeddings, mid-level activation peaks, and high-level beat/downbeat positions, and investigate which one benefits the generation model the most.

Our contribution is four-fold.
First, to our best knowledge, this work represents
the first attempt to drum accompaniment generation of a full drum kit given drum-free mixed audio.
Second, we develop a light-weight audio-to-audio Jukebox variant that takes an input audio of up to 24 seconds as conditioning and generates accompanying music in the domain of Mel-spectrograms (Section \ref{sec:methods}).
Third, we experiment with different beat-related conditions in the context of audio generation (Section \ref{sec:BeatCondEmbed}).
Finally, we report objective and subjective evaluations demonstrating the effectiveness of the proposed model (Sections \ref{sec:exp} \& \ref{sec:exp2}).\footnote{We share our code and checkpoint at: \url{https://github.com/legoodmanner/jukedrummer}. Moreover, we provide audio examples at the following demo page: \url{https://legoodmanner.github.io/jukedrummer-demo/}}.

\begin{figure}[t]
    \centering
    \includegraphics[width=.56\linewidth]{figs/flowchart.png}
    \caption{Diagram of the proposed JukeDrummer model for the inference stage. The training stage involves learning additional Drum VQ Encoder and Drumless VQ Decoder (see Figure \ref{fig:2VQ}) that are not used at inference time.}

    \label{fig:diagram}
\end{figure}

\section{Background}\label{sec:related_works}

\subsection{Related Work on Drum Generation}
\label{sec:related_works:drum}

Conditional drum accompaniment generation has been studied in the literature, but only in the symbolic domain \cite{dahale21ismir-lbd,makris22evomusart}, to the best of our knowledge.
Dahale \emph{et al.} \cite{dahale21ismir-lbd} used a Transformer encoder to generate an accompanying symbolic drum pattern of 12
bars given a four-track, melodic MIDI passage.
Makris \emph{et al.} \cite{makris22evomusart} adopted instead a sequence-to-sequence architecture with a bi-directional long short-term memory (BLSTM) encoder extracting information from the melodic input and a Transformer decoder generating the drum track for up to 16 bars in MIDI format.
While symbolic-domain music generation has its own challenges, it differs greatly from the audio-domain counterpart studied in this paper, for it is not about generating sounds that can be readily listened to by human.

Related tasks that have been attempted in the literature with deep learning include symbolic-domain generation of a monophonic drum track (i.e., kick drum only) of multiple bars \cite{lattner2019highlevel},  symbolic-domain drum pattern generation \cite{DBLP:conf/icml/GillickREEB19,drumVAE,alain2020deepdrummer,tokui2020gan},
symbolic-domain drum track generation as part of a multi-track MIDI \cite{musegan,simon2018learning,8614139,ren2020popmag}, audio-domain one-shot drum hit generation \cite{drumgan,9053128,aouameur2019neural,drysdale2020adversarial,drysdale21ismir-lbd}, audio-domain generation of drum sounds of an entire drum kit of a single bar \cite{tomczak20mm}, and audio-domain drum loop generation \cite{hung21ismir}.
Jukebox \cite{dhariwal2020jukebox} generates a mixture of sounds that include drums, but not an isolated drum track.
By design, Jukebox does not take any input audio as a condition and generate accompaniments.

\begin{figure}
    \centering
    \includegraphics[width=.81\linewidth]{figs/2VQ.png}
    \caption{We use separate VQ-VAEs for the drumless and drum tracks, both operating on the Mel-spectrograms.}
    \label{fig:2VQ}
\end{figure}

\subsection{The Original Jukebox model}

The main architecture of Jukebox  \cite{dhariwal2020jukebox} is composed of two components: a multi-scale vector-quantized  variational autoencoder (VQ-VAE) \cite{oord2017neural,kaiser18a,razavi2019generating,polyak21interspeech,walker21arxiv}
and an autoregressive Transformer decoder \cite{vaswani2017attention,sparsetransformer}.
The \textbf{VQ-VAE} is for converting a continuous-valued raw audio waveform
into a sequence of so-called discrete \emph{VQ codes}, while the \textbf{Transformer} establishes a language model (LM) of the VQ codes capable of generating new code sequences.

Specifically, VQ-VAE consists of an encoder and a decoder, referred to as the VQ-VAE encoder $\mathcal{E}$ and VQ-VAE decoder $\mathcal{D}$  below, respectively.
Given an audio waveform $\mathbf{x} \in \mathbb{R}^{1 \times N}$, where $N$ denotes the number of audio samples (e.g., $N=\text{1,058,400}$ for a 24-second audio clip sampled at 44.1\,kHz), the VQ-VAE encoder would convert $\mathbf{x}$ into a sequence of latent vectors $\{\mathbf{h}_t\in \mathbb{R}^D,t=1,...,T\}$, where the sequence length $T$ is proportional to $N$, and the VQ-VAE decoder would have to reconstruct $\mathbf{x}$ from the ``vector-quantized'' version of the latent vectors, denoting as $\{\mathbf{h}'_t\in \mathbb{R}^D,t=1,...,T\}$, that is obtained by finding the nearest prototype vector $\mathbf{h}'_t = \mathbf{e}_z$ of each $\mathbf{h}_t$ in a codebook of prototype vectors $\{\mathbf{e}_k\in \mathbb{R}^D ,k=1,...,
K\}$, where $K$ is the size of the codebook.
Each prototype vector can be regarded as a cluster centroid in the latent space as a result of $K$-means clustering.
The VQVAE encoder/decoder and the codebook are jointed learned by minimizing the reconstruction loss $\| \mathbf{x}_t -
\mathcal{D}(\mathbf{h}'_t)\|^2_2$, and the commitment loss of the clustering $\| \mathcal{E}(\mathbf{x}_t) - sg(\mathbf{e}_z)\|^2_2$, where $\mathbf{x}_t$ denotes a slice of $\mathbf{x}$, $sg(.)$ the stop-gradient operation, and $\mathbf{h}_t=\mathcal{E}(\mathbf{x}_t)$.

Once the VQ-VAE is trained, the $\mathbf{x}$ can be viewed as a sequence of ``IDs'' $\{z_t\in \mathbb{Z}_{1:K},t=1,...,T\}$, each corresponding to the index of the element of the codebook that is used to represent each slice of $\mathbf{x}$.
The Transformer can then be trained on such sequences of IDs to learn the underlying language, or ``composition rules,''  of the audio codes.
Once trained, the transformer can be used to generate a novel sequence of codes, which can then be converted into an audio waveform by the VQVAE decoder.

As the waveforms are extremely long, Jukebox actually uses a ``multi-scale'' VQ-VAE that converts a waveform into three levels of codes, and accordingly three Transformers for building the LM at each level \cite{dhariwal2020jukebox}.
KaraSinger works on Mel-spectrograms but also uses multi-scale VQ-VAE \cite{karasinger}.
We simplify their architecture by working on Mel-spectrograms with only one level of codes instead of multiple levels, as introduced below.

\section{Proposed Methods}\label{sec:methods}
In our task, we are given pairs of audio waveforms, namely a drumless stem $\mathbf{x}^\texttt{m}$ and a drum stem $\mathbf{x}^\texttt{d}$. Our goal is to train a model that can generate $\mathbf{x}^\texttt{d}_*$ given an unseen  $\mathbf{x}^\texttt{m}_*$ from the test set. To achieve so, we propose several extensions of the Jukebox model. While we focus on drum accompaniment generation only, the same methodology may apply equally well to other conditional generation tasks.

\textbf{Two VQ-VAEs.}
As illustrated in Figure \ref{fig:2VQ}, we build separate VQ-VAEs for $\mathbf{x}^\texttt{m}$ and $\mathbf{x}^\texttt{d}$ using drumless stems and drum stems respectively.
Once trained, the drumless VQ-VAE encoder and the drum VQ-VAE encoder would convert  $\mathbf{x}^\texttt{m}$ and  $\mathbf{x}^\texttt{d}$ into $\{z_t^\texttt{m}\in \mathbb{Z}_{1:K^\texttt{m}},t=1,...,T\}$ and $\{z_t^\texttt{d}\in \mathbb{Z}_{1:K^\texttt{d}},t=1,...,T\}$ separately.
Assuming that the drumless tracks are more diverse, we suggest use a larger codebook size for the drumless sounds codebook than the drum sounds codebook, namely $K^\texttt{m} \ge K^\texttt{d}$.

\begin{figure}
    \centering
    \includegraphics[width=.84\linewidth]{figs/transformer.png}
    \caption{Details of our Transformer encoder/decoder.}
    \label{fig:transformer}
\end{figure}

\begin{figure*}
    \centering
    \includegraphics[width=0.72\textwidth]{figs/beat_info_v2.png}
    \caption{Details of the proposed beat-aware module that extracts beat condition embeddings from the drumless audio.}
    \label{fig:beatinfo}
\end{figure*}

\textbf{Mel VQ-VAE \& Vocoder.}
To reduce computational cost, we build VQ-VAEs that take Mel-spectrograms as the input and target output.
Specifically, we use the convolutional blocks of UNAGAN \cite{ungan2020}
to build a pair of VQ-VAE encoder and VQ-VAE decoder that has symmetric architectures (one downsampling and the other upsampling). We omit the details of UNAGAN due to space limit.
Our Transformer accordingly learns the LM for codes of the  Mel-spectrograms.
Once a novel sequence of (drum) codes is generated by the Transformer, we use our (drum) VQ-VAE decoder to convert the codes into a Mel-spectrogram, and then use a neural \emph{vocoder} \cite{hifigan} to convert the Mel-spectrogram into the corresponding waveform.
While the latent vector $\mathbf{h}_t$ (and accordingly $z_t$) corresponds to a slice of waveform in the original Jukebox, here the latent vectors $\mathbf{h}_t^\texttt{m}$ and $\mathbf{h}_t^\texttt{d}$ (and $z_t^\texttt{m}$, $z_t^\texttt{d}$) both correspond to a fixed number of $L$ frames of the short-time Fourier Transform (STFT) while computing the Mel-spectrograms.

\textbf{Seq2seq Transformer.}
While the Jukebox model uses a Transformer decoder to model the sequences $\{z_t ,t=1,...,T\}$ corresponding to mixtures of sounds, in our case we need a dedicated Transformer encoder to take the sequence $\{z_t^\texttt{m} ,t=1,...,T\}$ corresponding to a drumless audio as the input, and a Transformer decoder to generate the sequence $\{z_t^\texttt{d} ,t=1,...,T\}$ corresponding to the accompanying drum audio as the output, leading to a sequence-to-sequence (seq2seq) architecture.
Following Jukebox, we use the attention mechanism of the Scalable Transformer \cite{sparsetransformer} in our Transformer encoder/decoder. As depicted in Figure \ref{fig:transformer}, to determine its output $z_i^\texttt{d}$ at each $i$, our Transformer decoder uses factorized ``in-chunk,'' ``cross-chunk'' and ``previous-chunk'' attention layers to attend to the drum codes it generates previously, namely $z^\texttt{d}_{<i}=\{z_t^\texttt{d} ,t=1,...,i-1\}$, to maintain the coherence of its output.
A ``chunk'' here is a slice of the corresponding sequence.
Moreover, the Transformer decoder uses ``encoder/decoder attention'' to attend to the final layer of the Transformer encoder to get contextual information from the drumless code sequence $\{z_t^\texttt{m} ,t=1,...,T\}$.
To better synchronize the input and output, position embeddings are used. We refer readers to \cite{dhariwal2020jukebox,sparsetransformer} for details.

\textbf{Beat-Aware Module.}
Figure \ref{fig:transformer} also shows that we use a novel ``beat-aware module'' (after the position embedding) in both Transformer encoder and decoder.
We note that the  code $z_t^\texttt{m}$ is trained to provide essential information needed to reconstruct the drumless audio $\mathbf{x}^\texttt{m}_t$, so the information carried by $z_t$ might be \emph{mixed}, covering different musical aspects including timbre, style, rhythm, etc. To supply the Transformer with \emph{clear} rhythm-related information of $\mathbf{x}^\texttt{m}$, we might need such a dedicated beat-aware module.

As depicted in Figure \ref{fig:beatinfo}, the beat-aware module consists two sub-modules, the ``beat information extractor'' and the ``beat conditioner.''
The former extracts beat-related information per frame
from $\mathbf{x}^\texttt{m}$ using an existing beat/downbeat tracker \cite{spl21chiu}, while the latter incorporates that beat-related information for each $t$ as a beat condition embedding $\mathbf{c}^\texttt{m}_t \in \mathbb{R}^{d_\text{model}}$ that has the same length $d_\text{model}$ as the token embedding of the Transformers, and adds together the beat condition embedding and token embedding
per $t$ to serve as the input to the subsequent attention layers.

Despite that deep learning-based beat/downbeat trackers such as those proposed by B{\"{o}}ck \emph{et al.}
\cite{madmom, Bock2016d,Bock2020} have achieved excellent performance for mainstream pop, rock or dance music, their performance and behaviors are influenced by the sound source composition (i.e., drum/non-drum sounds) of their training data \cite{spl21chiu, chiu21eusipco}.
Considering that our input music is drumless, which is quite different from the music that existing common beat/downbeat trackers \cite{Bock2016d, Bock2020} are trained with and trained for, we use in our beat information extractor the ``non-drum tracker'' developed by Chiu \emph{et al.}  \cite{spl21chiu} exclusively for drumless stems.
In Section \ref{sec:BeatCondEmbed}, we describe three ways to extract different levels of beat information feature from the non-drum tracker.

\section{Beat Condition Embedding}\label{sec:BeatCondEmbed}

As shown on the left of Figure \ref{fig:beatinfo}, following the common design \cite{Bock2016d}, the beat/downbeat tracker \cite{spl21chiu} uses BLSTM layers to get 50-dimensional ``tracker embeddings'' from $\mathbf{x}^\texttt{m}$, and then uses fully-connected (FC) layers to compute 3-dimensional ``activation functions'' indicating the likelihood of observing a beat, downbeat, or non-beat at each frame.
Finally, either a simple peak-picking or a
hidden Markov model (HMM)-based algorithm
\cite{Bock2016d}  can be used to
finalize the beat and downbeat time positions from the activation functions.
We accordingly investigate extracting features from different stages of this processing pipeline.

\textbf{Low-level (tracker) embeddings.}
We simply use the high-dimensional tracker embeddings, resampling it temporally, pooling them every $L$ frames,
and converting each of them to the desired length $d_\text{model}$ with an FC.

\textbf{High-level beat/downbeat positions.}
From the beat and downbeat time positions estimated by HMM, a frame can be labeled as either a beat, downbeat, or non-beat. We learn three $d_\text{model}$-dimensional embedding vectors corresponding to each case, and represent every $L$ frames with one of the three vectors according to the frame labels.

\textbf{Mid-level activation peaks.}
As the beat/downbeat positions are sparse over time, we consider a ``denser'' version by simply picking the peak positions of the activation functions by  \texttt{scipy.signal.find\_peaks} \cite{scipy} and similarly learning embedding vectors that are assigned to the frames according to whether a frame corresponds to a peak or not.
Such a peak might represent a musical onset.

\section{Experiment Setup}
\label{sec:expsetup}

Multi-track datasets for research on musical source separation \cite{musdb18}
usually host recordings that each consists of an isolated drum stem along with stems corresponding to other instruments.
We can simply take the drum stem as the target drum track, and the summation of the remaining stems as the input drumless track.
In our implementation, we used the multi-track recordings from  three datasets.
MUSDB18 \cite{musdb18} contains 150  recordings, each of which has four stems corresponding to vocal, drums, bass, and ``others.'' It is commonly used in source separation.
MedleyDB \cite{bittner14ismir} contains 196 multi-track recordings, each of which includes a drum stem along with many other stems.
MixingSecret \cite{mixingsecret}
has 257 multi-track recordings with various instruments, all including drums.
We removed the 46 duplicate recordings between MUSDB18 and MedleyDB and the 100 duplicate recordings between MUSDB18 and MixingSerect, leading to 457 recordings to be used in our work.
We randomly split the recordings into 80\%, 10\%, 10\%  as the  training set, validation set (for parameter tuning), and testing set (for objective and subjective evaluation) at the ``recording-level,'' ensuring that a recording does not appear in different splits.

All the recordings are sampled at 44.1\,kHz and the stems are all monaural.
Following Jukebox \cite{dhariwal2020jukebox}, we used 24-second audio clips in our work.
We sliced each recording (and accordingly the stems) to 23.8-second audio clips with 50\% temporal overlaps  (as $2^{20}/\text{44100}=\text{23.8}$), and discarded the clips that do not contain any drum sounds. We then computed the Mel-spectrograms of each clip with PyTorch v1.7.1 with a Hann window of 1,024 samples for STFT, a hop size of 256 samples and 80 Mel-filter banks.

To evaluate the performance of the proposed JukeDrummer and validate the effectiveness of model components, we adopted the following  variants in our experiments.\footnote{A reviewer suggested that we should have compared our model with other accompaniment generation models that operate in the symbolic domain such as \cite{dahale21ismir-lbd} and \cite{makris22evomusart}, saying that we can use existing audio samples or drum synthesis models to render their output to audio. However, the problem is that such a symbolic-domain accompaniment generation model also requires its input to be a MIDI file rather than audio.}
\begin{itemize}
    \item \texttt{seq2seq+beat\,(low)}: given a drumless clip $\mathbf{x}^\texttt{m}$, we computed the drumless codes $\{z_t^\texttt{m}\}$ and the low-level beat/downbeat tracker embeddings $\{\mathbf{c}^\texttt{m}_t\}$ as the model input, to predict the drum codes $\{z_t^\texttt{d}\}$ that eventually lead to the generated drum clip $\mathbf{x}^\texttt{d}$, using the proposed seq2seq Transformer.
    \item \texttt{seq2seq+beat\,(mid)}: using the beat condition embeddings computed from the mid-level activation peaks (see Section \ref{sec:BeatCondEmbed}) for $\{\mathbf{c}^\texttt{m}_t\}$ instead.
    \item \texttt{seq2seq+beat\,(high)}: using the beat condition embeddings from the high-level beat/downbeat positions as $\{\mathbf{c}^\texttt{m}_t\}$ instead.
    \item \texttt{seq2seq~w/o~beat}: to study the usefulness of the beat conditioning, we predicted $\{z_t^\texttt{d}\}$ from $\{z_t^\texttt{m}\}$, not using any beat-related conditions at all.
    \item \texttt{decoder+beat\,(low)}: to study the usefulness of the drumless codes $\{z_t^\texttt{m}\}$, we used only the low-level beat condition embeddings $\{\mathbf{c}^\texttt{m}_t\}$ as input to predict  $\{z_t^\texttt{d}\}$, via a Transformer decoder-only architecture (i.e., not seq2seq Transformer).
    \item \texttt{decoder~w/o~beat}: this is the baseline drummer that ``plays its own,'' generating $\{z_t^\texttt{d}\}$ autoregressively via a Transformer decoder without taking any information (neither $\{z_t^\texttt{m}\}$ nor $\{\mathbf{c}^\texttt{m}_t\}$) from the drumless clip $\mathbf{x}^\texttt{m}$ it is supposed to play along to.
\end{itemize}

\begin{table*}[]
\setlength{\tabcolsep}{4pt}
\centering
\begin{tabular}{|l|ccc|ccccc|}
\hline
\multirow{2}{*}{Model} &
\multicolumn{3}{c|}{Objective metrics} & \multicolumn{5}{c|}{Subjective MOS ($\in [1,5]$)  } \\
& TrackEmd-MSE$\downarrow$ & Act-Entropy$\downarrow$  & B/DB-F1$\uparrow$  & ~~\textbf{R}$^\texttt{m/d}$~    & ~~\textbf{S}$^\texttt{m/d}$~    & ~~\textbf{Q}$^\texttt{d}$~    & ~~\textbf{R}$^\texttt{d}$~    & ~~\textbf{O}$^\texttt{d}$~    \\ \hline
\texttt{seq2seq+beat\,(low)}    & \textbf{.068}\small{$\pm.023$}   & \textbf{.928}\small{$\pm.189$} & \textbf{.340} & \textbf{3.27} & \textbf{3.44} & \textbf{3.39} & \textbf{3.37} & \textbf{3.20} \\
\texttt{seq2seq+beat\,(mid)}     & .077\small{$\pm.022$} & .963\small{$\pm.200$} & .212          & ---           & ---           & ---           & ---           & ---           \\
\texttt{seq2seq+beat\,(high)~}    & .080\small{$\pm.024$} & .938\small{$\pm.160$} & .132          & ---           & ---           & ---           & ---           & ---           \\
\texttt{seq2seq~w/o~beat}   & .081\small{$\pm.022$}   & .968\small{$\pm.233$}& .110          & 1.78          & 2.34          & 2.95          & 2.58          & 2.05          \\
\hline
\texttt{decoder+beat\,(low)} & \textbf{.068}\small{$\pm.023$}   & \textbf{.931}\small{$\pm.200$} & \textbf{.339} & \textbf{3.05} & \textbf{3.34} & \textbf{3.33} & \textbf{3.07} & \textbf{3.17} \\
\texttt{decoder~w/o~beat}   & .087\small{$\pm.025$}  & .987\small{$\pm.240$}  & .114          & 1.59          & 1.83          & 2.56          & 1.88          & 1.73             \\ \hline
Real data (not vocoded)    & ---   & --- & --- & 4.39 & 3.95 & 3.85 & 4.61 & 4.17 \\
\hline
\end{tabular}
\caption{Results of objective and subjective evaluation of  variants of the proposed JukeDrummer model.
The metrics are (from left to right): beat/downbeat tracking embedding MSE, beat/downbeat activation entropy, beat/downbeat F1, \textbf{R}$^\texttt{m/d}$~(rhythmic consistency), \textbf{S}$^\texttt{m/d}$~(stylistic consistency), \textbf{Q}$^\texttt{d}$~(audio quality), \textbf{R}$^\texttt{d}$~(rhythmic stability), \textbf{O}$^\texttt{d}$~(overall). $\downarrow$\,/\,$\uparrow$: the lower/higher the better; best two results (among the six model variants) per column highlighted in bold.}
\label{tab:lm_config2}
\end{table*}

While the Mel-spectrogram for a clip in our case
has 4,096 frames, the VQ-VAE encoder downsamples it to a sequence of $T=\text{1,024}$ latent vectors, namely each corresponding to $L=4$ frames.
For VQ-VAE, we set the codebook size of drumless sounds $K^\texttt{m}=\text{1,024}$ and that of drums $K^\texttt{d}=\text{32}$ (so $K^\texttt{m} \gg K^\texttt{d}$), and the dimension of the latent vectors $D=\text{64}$.
The VQ-VAE encoders/decoders for both drumless and drums all have two layers.
For those models employing a seq2seq LM, the Transformer encoder has 9 layers and the Transformer decoder has 20 layers.\footnote{We used Adam as our optimizer with a learning rate of 0.0003 for both VQ-VAE and Transformer LM. We trained our LM with a batch size of 16, input feature dimension $d_\text{model}=\text{512}$, two heads for multi-head attention, and the chunk size for factorized attention being 16. For those employing a decoder-only architecture, the number of layers of the Transformer decoder reduces to 15 because 5 of the layers corresponding to the
``encoder/decoder attention'' are removed.}

At inference time, we used the drum VQ decoder to convert the drum codes $\{z_t^\texttt{d}\}$ to a Mel-spectrogram, which is then turned into the waveform of the drum clip $\mathbf{x}^\texttt{d}$ by a HiFi-GAN V1 vocoder \cite{hifigan}. We trained the vocoder from scratch with audio of drum sounds from our  dataset for 2.5 days,
and then, inspired by \cite{assemvc22icassp,naturalspeech}, fine-tuned it on the re-constructed Mel-spectrograms of the Drum VQ decoder.

\section{Objective Evaluation}
\label{sec:exp}

As audio-domain drum accompaniment generation is new, we propose customized metrics.
Specifically, we use the ``drum tracker''
trained exclusively for drum stems by Chiu \emph{et al.} \cite{spl21chiu}, which follows exactly the same  pipeline as the non-drum tracker (cf. Figure \ref{fig:beatinfo}), to extract rhythmic features at  three different levels for the ground-truth and generated drum sounds for the test split. We then compare the rhythmic features of the ground-truth and the generated in such three levels,
using the mean square error for the low-level tracker embeddings (\textbf{TrackEmb-MSE}),
cross entropy for the mid-level activation functions (\textbf{Act-Entropy}),
and the F-measure for beat/downbeat estimation (\textbf{B/DB-F1}).
The last one, computed by \texttt{mir\_eval} \cite{mireval},
uses the beat positions of the ground-truth drums as the reference and those of the generated drums as the estimated beats.
These metrics evaluates only the \emph{rhythmic consistency} between the generated drums and the drumless audio (using its human-made drum track as a proxy), not other aspects such as stylistic consistency and audio quality, which will be evaluated subjectively  in Section \ref{sec:exp2}.

Result shown in the middle of Table \ref{tab:lm_config2} clearly shows that the models with low-level beat information achieve  lower MSE, cross entropy, and  higher F1, suggesting the usefulness of the low-level beat information for rhythmic consistency. The mid-level and high-level ones seem less effective (possibly because they are relatively monotonic), so we did not evaluate them further in Section \ref{sec:exp2}.
The objective scores also suggest that the Transformer encoder does not contribute much to rhythmic consistency.

\section{Subjective Evaluation}
\label{sec:exp2}

With an online study, we solicited 22 anonymous volunteers to rate the result for 3 out of 15 random drumless tracks (each 23.8 seconds)  from the test split.
Each time, a volunteer listened to a drumless tracks ($\mathbf{x}^\texttt{m}_*$) first, and then (in random orders) the mixture (i.e., $\mathbf{x}^\texttt{m}_*+\mathbf{x}^\texttt{d}_*$) containing drum samples generated by four different models, plus the real human-made one (to set a high anchor).
The volunteer then rated them in the following aspects on a 5-point Likert scale:
    \textbf{rhythmic consistency} between the drumless input and generated drums;
    \textbf{stylistic consistency} concerning the timbre and arrangement of the drumless input and generated drums;
    \textbf{audio quality} and \textbf{rhythmic stability} (whether the drummer follows a steady tempo) of the generated drum; and
    \textbf{overall} perceptual impression.

The mean opinion scores (MOS) in
Table \ref{tab:lm_config2}
show that $\texttt{seq2seq+beat\,(low)}$ consistently outperforms the others, validating the effectiveness of using both the drumless codes and beat conditions.
$\texttt{decoder+beat\,(low)}$ performs consistently the second best, outperforming the two models without beat information significantly in three aspects according to paired t-test  ($p$-value\,$<\text{0.05}$), validating again the importance of the beat-aware module.
Complementing Section \ref{sec:exp}, the MOS result suggests that the beat conditions seem more important than the drumless codes, though the best result is obtained with both.

Figure \ref{fig:diversity} further demonstrates that, given the same input, our model can generate multiple accompaniments with diversity in both beat and timbre.
Diversity is an interesting aspect that is hard to evaluate, but it is desirable as there is no single golden  drum accompaniment for a song. This may also explain why the F1 scores in Table \ref{tab:lm_config2} seem low.

Verbal feedbacks from the subjects confirm that our best model generates drum accompaniment that is rhythmically and stylistically consistent with the input, especially for band music or music with heavy use of bass.
However, the model still has limits.
At times the model generates total silence, though it can be avoided by sampling the LM again.
The model may struggle to change its tempo going through different sections of a song.
Moreover, the generation might be out-of-sync with the input in the beginning few seconds, until the model gets sufficient context.
Please visit the demo page for various examples.

\begin{figure}
    \centering

    \includegraphics[width=.81\linewidth]{figs/diversity.png}
    \caption{Two sets of three different generated samples by the same model given the same beat condition embedding.}

    \label{fig:diversity}
\end{figure}

\vspace{-1mm}
\section{Conclusion}
We have presented JukeDrummer, a novel
audio-to-audio extension of  OpenAI's JukeBox model capable of adding the drum part of a drumfree recording in the audio domain.
To our knowledge, this represents the first attempt to audio-domain generation conditioned on drumless mixed audio.
With objective and subjective evaluations, we validated the effectiveness of the customized VQ-VAE plus the seq2seq Transformer design, and the proposed beat-aware module.
Among the beat conditions, we found that the low-level embeddings work the best.
Future work can be done to further improve the language model (LM), and to extend our work to other audio-to-audio generation tasks.

\section{Acknowledgement}
We are grateful to the anonymous reviewers for their valuable comments. This research is funded by grant NSTC 109-2628-E-001-002-MY2 from the National Science and Technology Council of Taiwan.

redpajama_set_name: RedPajamaArXiv


We had a lovely afternoon investigating sunflowers. We went on a learning walk and found our sunflowers in the garden. The children drew their own Van Gough inspired pictures in chalks and made some fabulous 3d pictures.
The children in Year 3 dug their own beetroot up out of the garden and used it to make roasted vegetable parcels. We can't wait to taste it tomorrow.
How to make a stable structure in the forest.
Newsflash: The children of Neverland have discovered the Abbots Langley fairy friendship tree!
Our first forest school session with our brand new Puddleduck children.
We learnt how to make natures pencils by peeling sticks to make them sharp. We then wrote our names in the mud! Looking forwards to more forest fun next week.
It was great to be back in the forest with Year 1 after the summer break. This half term we are going to investigate the characteristics of a village and we began by helping the fairies who had moved into the forest over the summer by rebuilding their village that had been destroyed by a dragon!
The children had some lovely ideas of what to build using natural materials and decided they needed to create a trap to catch the dragon.


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On the High Street
The Department Store, situated in the centre of Northallerton High Street offers an incredible range of merchandise, including some of the world's leading brands of fashion, lingerie, menswear, shoes, handbags, cosmetics, cards, childrenswear, luggage, gifts and homewares.
Barkers Department Store really has something for everyone. Whether you're after a cutting-edge designer dress or are a mature lady looking for a new season outfit. Something special for your little one or a pair of silk socks for grandad. A thoughtful wedding gift for a family friend or a finishing touch for your daughter's first home. Barkers brings you a myriad of choice.
100s of Brands
We bring some of the world's leading, most desirable brands together, all under one roof. Peruse an Armani to Ziggle of labels. Browse from Barbour to Bulgari. Stride from Superdry to Smeg. Choose between Clinique and Clarins. Our experienced buying team have a keen eye for quality and value. They curate inspired collections from the big names in fashion, beauty and homewares.
This season tailored to you.
Experience personal shopping.
Every season brings new trends in fashion and our highly trained team of Personal Stylists are here to show you which of these will suit you. Book a complimentary personal shopping appointment with one of our friendly expert stylists today. Then just sit back and enjoy being inspired and guided through their fashion and accessories selections tailored precisely to you.
Explore Personal Shopping
In-Store Restaurants
Our recently refurbished 180-seat luxury restaurant Barkers Kitchen is situated on the first floor. With spectacular views over Applegarth Park it has an extremely tempting menu. Spacious and easily accessible for all, it's perfect for families. Barkers also has the 1882 Café and Bistro. The ground floor 1882 Café is counter service and specialises in fresh coffees and cakes, while the first floor 1882 Bistro has a seasonal menu offering a wide range of freshly prepared dishes.
Explore The Restaurants
Visit The Store Now
Barkers is situated in the centre of Northallerton High Street where there is pay and display street parking. Applegarth Car Park is a large 250-space pay and display car park situated at the rear where there is access to the store.
Finding Barkers
The Department Store, situated in the centre of Northallerton High Street offers an incredible range of the world's leading brands across fashion, footwear, handbags, cosmetics, homewares and more.
Department Store Details
North Yorkshire, DL7 8LP
Barkers is a family department store and Northallerton institution with a fascinating history that dates back to 1882.
Our doors are open 7 days a week, with the exception of Christmas Day, Boxing Day and Easter Sunday.
60,000 Sq ft. furniture store on the edge of Northallerton, Barkers Home has a range of living, dining and bedroom products.


Listed under Wine Regions in Piedmont-Northeast, Italy.
Italy's Piedmont region wraps across the arch at the top of the boot creeping up the base of the Alps and Apennines. This region is known for producing quality, location and blend specific appellations like Barbera, Barolo and Asti and yields the largest number of prize winning wines of any Italian region. The cooler dry conditions best suit red grapes but plenty of different flavours and colours comes out to the soil, so there is a good range of flavours to taste.
Many wine makers in this region have had their land and wine making traditions passed down to them for generations so there are many small plots of land producing traditional vintages, which are more interesting to tour, as well as improving your wine tasting experience, giving you a much greater chance of meeting the wine makers themselves, it makes for a pleasing rural patchwork of vineyards and hilltop villages of cobbled streets and lots of traditional architecture.
Stay off the main road to find the best Piedmont has to offer. One of the nicest routes to travel is along the Lange Hills from Alba where you'll hit wine village after wine village with plenty of cellars and tasting centres along the way. The big reds the region is known for compliment the rich foods you'll find on the menus featuring truffles, fonduta (cheese fondue) and rich meats and if you're really lucky you may come across a friendly family winery in time to be welcomed into a big traditional Piedmont meal.
In my mind Italy is awash with vineyards in stunning locations. You've got your rows on the hilltops and your neat fields in valleys amongst the russets and orange colours Italy is famous for. They also take their wine seriously and have done for generations so you can listen to people speak with passion about what they create. There are lots of farmer, growers for whom the wine itself is better reward than international renown and they drink it (an will share it with you) accordingly (and they know a thing or two about food as well.). Piedmont has to be one of the best looking of an excellent bunch, Tuscany is a bit too perfect, but Piedmont is the real thing, oceans on undulating vines, heavy with dusty black grapes and naked earth between.


would I have been tasting?
would you have been tasting?
would he have been tasting?
would we have been tasting?
would they have been tasting?
would I not be tasting?
would you not be tasting?
would he not be tasting?
would we not be tasting?
would they not be tasting?
would I not have tasted?
would you not have tasted?
would he not have tasted?
would we not have tasted?
would they not have tasted?
would I not have been tasting?
would you not have been tasting?
would he not have been tasting?
would we not have been tasting?
would they not have been tasting?


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The word 'sun' came from Middle English 'sunne', from Germanic language 'sunn'. The Sun is
the star at the center of the Solar system. It is nearly a perfect sphere of hot plasma ( Lightning
and neon lights are common place generators of plasma) with internal connective motion that generates a magnetic field via a dynamo process. It is the most important source of energy for
life on earth. Its diameter is about 1.39 million kilometers,i.e. 109 times that of Earth, and its
mass is about 330,000 times of Earth, accounting for about 99.86% of the total mass of the so-
-lar system. About three quarters of the Sun's mass consists of hydrogen (73%), the rest is mo-
-stly helium(25%), with much smaller quantities of heavier elements, including oxygen, carbon,
neon and iron. The Sun has been regarded by some cultures as a deity. They say, the source of
all powers is Sun. The surface temperature of the Sun is almost 10,000 degrees Fahrenheit. The
interesting fact is — Sun does rotate. "Since the Sun is a ball of gas/plasma, it does not have to
rotate rigidly like the solid planets and moons do", according to NASA. In fact, our gaseous Sun
is divided into different zones and layers, with each of our hot star's regions moving at varying
speeds. ON average, the Sun rotates on its axis once every 27 days. However, its equator spins
the fastest and takes about 24 days to rotate, while the poles take more than 30 days to rotate.
The inner parts of the Sun also spin faster than the outer layers, according to NASA. Back in
1612, Galileo Galilei noticed something odd : Sun spots moved across the sun's disk over time,
confirming the Sun's rotation, according to Stanford University's Solar Center. Even today,
researchers are able to monitor the Sun's movements and rate of rotation by observing its
sunspot activity.
(PARDON ME IF I AM WRONG !)
Published by gourabs169
I am A Philanthrope	View all posts by gourabs169
axis, earth, Galileo Galilei, helium, hot plasma, hydrogen, NASA, neon, neon lights, rotation, solar system, sun
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"FCAT 2.0 and the Algebra I End of Course assessment, based on the Next Generation Sunshine State Standards, are designed to test Florida's students on more rigorous material. The standards I am recommending today reflect this, raising the bar for Florida's children from 3rd through 10th grade and ensuring that our students are on the path to graduating college and career ready. For the first time in Florida's history, our graduation requirements are aligned with college readiness and our standards are consistent across grade levels. Parents, teachers, and administrators will now be able to identify those children who need additional help earlier so that they can graduate college ready.
"I would like to thank the educators who began this process, along with the business, community, and education leaders who closely examined the data and offered recommendations based on a broader policy perspective, and the members of the State Board of Education, whose workshops led to my final recommendations. While these recommended scores raise standards, I am confident that Florida's children will meet them, just as they have before."


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The Avon Grove Charter School Conservation Area/
Avon Grove Charter School » About Us » Green Initiative » The Avon Grove Charter School Conservation Area
Students in the Hardwood Forest within the AGCS Conservation Area
Students Re-Introduced Native Fish to an On-Campus Stream in May of 2011
Avon Grove Charter School's Meadows are Filled with Blooming Wildflowers in the Warmer Months
The Avon Grove Conservation Area is located on of 23 acres of "naturalized" land at the Avon Grove Charter School's State Road Campus in West Grove, PA.
Early on, our school began looking very carefully at the ways we were utilizing the substantial amount of on-campus "open space," which had been habitually mowed and essentially neglected since our school building was first constructed in 1928.
It seemed unfortunate that so much of our campus was being used for nothing more than the maintenance of long stretches of sod which served no practical purpose. We realized that we were spending a great deal of time and money to sustain non-native grasses in sections of our campus which could, with some changes, support wildlife and counterbalance some of the negative environmental changes occurring throughout our region.
The rapid development of the area surrounding AGCS made the urgency of local habitat preservation increasingly apparent. In 2008, developers began working on the opposite side of State Road, and within weeks, one of the only remaining tall grass meadows in our region had disappeared beneath the bulldozer. The disturbingly swift pace of development was making our local environment almost unrecognizable.
The Avon Grove Charter School's Conservation Area is our school's response to habitat destruction and environmental degradation. Today, the Conservation Area is an island of biodiversity within one of the most rapidly developing parts of our state. The conservation area has been undergoing intensive restoration designed to encourage a wide variety of habitat types, which now enable AGCS students and the general public to experience, explore, and better understand the natural world.
The AGCS Conservation Area is also a valuable "outdoor laboratory" within which students can practice hands-on science according to the discovery-based learning principles upon which our school was founded. In years to come, we hope it will also broadcast a conservation ethic which will radiate through our local community, and promote the preservation of some of the last wild places in Chester County.
Rare Plants and Animals in The Conservation Area
Over the past several years, the AGCS conservation area has become a haven for plants and animals which are becoming increasingly rare in our state. Students have played a direct role in species restoration within the conservation area and they can take direct credit for the re-introduction of the following rare, threatened, or endangered plants species (see the USDA's list of threatened and endangered plants in Pennsylvania) in our conservation area:
Sweetbay Magnolia
Rattlesnake Master
Sweetflag
Gray-Headed Coneflower
Dwarf Crested Iris
Meehan's Mint
Bush Clover
Willow Oak
Through a "Greenworks" grant provided through Project Learning Tree, AGCS students also worked to improve meadow and scrubland habitat on campus, and reintroduced Bobwhite Quail to the area utilizing a cutting edge brooding system known as the "Surrogator."
During the 2011-12 school year, AGCS added a "Rare Trees Orchard" to its Conservation Area, within which students will raise American Chestnut (hybrid) trees in a effort to preserve the genetics of this once common American tree which was pushed to the edge of extinction by chestnut blight, an incredibly damaging disease. The Orchard also includes American Elm trees (which had been devistated by Dutch Elm Disease, but are recovering), and some rare "heirloom" fruit trees which are in danger of extinction.
The National Wildlife Federation Schoolyard Habitat Program
AGCS has drawn a great deal of inspiration and support from the National Wildlife Federation. Our Conservation Area is a Certified Schoolyard Wildlife Habitat (Site No. 115,013).


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Stock Update (NYSEMKT:VHC): Here's Why VirnetX Holding Corporation Shares Skyrocketed 35% Today
Corey Williams- October 3, 2016, 10:57 AM EDT
Investors in VirnetX Holding Corporation (NYSEMKT:VHC) should be smiling from ear to ear today following the news that Apple Inc. (NASDAQ:AAPL) has been ordered to pay over $300 million in damages to VirnetX over a patent dispute.
VirnetX Holding shares reacted to the news, jumping nearly 35% to $4.21.
On Friday, a jury in the United States Court for the Eastern District of Texas, Tyler Division, awarded VirnetX $302.4 million in a verdict against Apple for infringing four VirnetX patents, marking the third time a federal jury has found Apple liable for infringing VirnetX's patented technology.
The verdict includes royalties awarded to VirnetX, for unresolved issues in the Apple I case, remanded back from the United States Court of Appeals for the Federal Circuit ("USCAFC"), related to (1) damages owed to VirnetX for infringement by Apple's original VPN-on-Demand (VOD) and (2) the alleged infringement by Apple's original FaceTime product, under the new claim construction of "secure communication link" pertaining to the '504 and '211 patents by the USCAFC, and the damages associated with that infringement.
"We are extremely pleased with the jury verdict announced today," said Kendall Larsen, VirnetX CEO and President. "This is the third time a jury has confirmed that Apple has been using the technology developed by our inventors."
VirnetX has two currently-pending patent infringement lawsuits against Apple Inc., in United States Court for the Eastern District of Texas, Tyler Division. The Apple I case and Case No. 6:12-cv-00855 ("Apple II") which covers issues of alleged infringement, damages and willfulness by Apple's redesigned VOD in iOS 7 and 8, the redesigned FaceTime in iOS 7 and 8 and OS X 10.9 and 10.10 and iMessage. A jury trial in Apple II case is expected to be scheduled by the court shortly after the conclusion of the Apple I case. (Original Source)
VirnetX Holding Corp. is an internet security software and technology company. It develops software and technology solutions for securing real-time communications over the Internet. The company's patented Gabriel Connection Technology combines industry standard encryption protocols with patented techniques for automated domain name system, lookup mechanisms, enabling users to create a secure communication link using secure domain names. It provides the security platform required by next-generation Internet-based applications such as instant messaging, voice over Internet protocol, mobile services, streaming video, file transfer and remote desktop.


Hi my name is Erinys and I'm addicted to playing Druids. I didn't realise quite how bad my problem was until I started taking screenshots for this post but it seems that Druids are one of my go to alt classes. Therefore in chronological order, I'd like to introduce you to my furry, feathered and often squawking friends.
Briarrose senior was my third character to end game and she was born out of guild drama and general stress. I wanted to spend time away from guild chat and what better way to do that than to level a character who wasn't guilded. Mr Harpy already had two Druids at level 60 by this point (his first two characters) so I had a good understanding of the class and I loved being able to mix it up in pvp. In fact my greatest moment in a battleground was probably the game in which when facing a same server Horde Premade, my little Druid held the Blacksmith against around seven decently geared and decently played Horde through abuse of feral charge, bear healing and sprinting off to heal up out of line of sight and bouncing back moonfiring.
Briarrose the younger was my first ever Tauren and again dates back to "vanilla". I have to admit I actually miss the original Tauren cat form (because I'm weird like that) and spent ages when they brought in the new colours trying to find a catform I found acceptable for her. Briarrose has been Feral for ever and will probably stay that way even though I'm not hugely taken with any of their catform alternatives.
Tabby only reached level 10 and has spent the rest of her existence on a pretty much dead server. I don't however have the heart to delete her.
Tansy is a Moonkin and a ganker. Her hayday was in Wrath when she could bring squawk and awe justice from the skies to evil Horde. I think Cataclysm and then MoP pretty much broke her heart because it's become impossible to slaughter someone as you fall past them with your parachute cloak up. She was also rather fond of Wintergrasp even though the imbalanced population on her server made it "interesting".
My Troll Druid was made because everyone needs a Troll in their lives. She's another Boomkin because moonfiring stuff, especially Gnomes and Goblins never gets old.
Teasel is Resto through and through. That's the way she leveled and whilst she does have dual spec, the second one is also Resto. I have to admit though, had Treeform still existed as a permanent form, she might well have been another Moonkin. I leveled her towards the end of Cataclysm and primarily through PvP and it's definitely been fun comparing her to my original Druid Briarrose. I miss feral charge as Resto for sure and also I don't think the damage in cat/bear form scaled at all from where it was back then. Originally you could use cat/bear to kill in PvP saving your mana for healing, even against DPS classes if you did it right. I'd be very surprised if that was possible now.
Finally my baby Worgen Druid, my Harvest Witch, Mentzelia. My plan for her (once she escapes Gilneas which she needs to do before Hallows End) is to level as Feral just because I haven't done that for so long.
My goals for this week then are to make that final push to free Mentzelia and are to find a proper transmogrification for my little Tauren Briarrose.
Cymre put up an interesting post a while back about the old AQ Gates quest chain which got me thinking about a couple of my favourite and now removed quest chains. Then when I was attempting to clear out my drafts folder I came across this. A post I started in November 2009 but never got around to finishing or publishing (before now) plus it is Druid week and thus ever so slightly relevant (at least the first paragraph is).
I'm currently re-running the Swift Flight form quest on my second druid. The experience has left me feeling a little saddened and humbled. Why aren't there more of these awesome quests scattered through the game. Why don't all classes have the opportunity to discover more lore about their class? I had to use virtually all my spell book, from hibernate to cat form, to remove curse to moonfire. I visited places I'd never seen before, like the pink frosting masquerading as water outside the Barrow Dens and the river border of Azshara and Ashenvale. I came out of Sethekk Halls feeling like a proper grown up Druid.
Back in Vanilla, I had the pleasure of doing the Benediction chain and I watched the Gnomeling do his Rhok'ledar (I was there for moral support and to kill any Horde who looked at him funny). Both were amazing chains but when you compare the Priest/Hunter epic chains to the Druid model, there are a couple of crucial differences. The Druid one doesn't rely on RNG. I got lucky, my Eye dropped from our very first Majordomo "kill". However I knew Priests who had to wait months for that chest to stop yielding leaves, conversely, my guild got Eye after Eye and so our Hunters were epic bowless. Putting the two parts that both Hunters/Priests needed on two different raid bosses meant that in order to experience your epic class quest you had to raid. For Priests especially, getting the Eye of Shadow on my server was hard. One guild (mine) tended to get 95 percent of all the Lord Kazzak kills and when you factored in the fact that we had something like 12 raiding priests and numerous priest alts, it took a while for them to filter onto the Auction House. Yes, you could farm it from those horrible demons in Winterspring but doing so as a Priest solo was hard and the drop rate sucked. That's actually how I got mine, farming with three other holy priests and a resto druid until 4am – roots, smite, smite, smite, smite, wrath, roots, smite, smite, smite, smite, roots, wrath, wrath, wrath and so on. Painful doesn't even begin to describe the experience. To be fair we started out with a bunch of dpsers but they all went to bed early. Not sure if that was because they were bored or because they had to get up early in the morning and we healers soldiered on.
So if any future class quests were added, I think that Blizzard should follow the Druid model and make the chain easily available to anyone regardless of how they choose to spend their time in-game. Availability obviously has to impact on the reward, but the rewards could be cosmetic or fun or even powerful at the start of the expansion but wilt over time. If something is easily obtained by say all warriors willing to spend a few hours running quests and probably doing a dungeon or two, I can't think of any reason why it couldn't be comparable with the first tier of raid weapons. It would provide a different gearing path and help factor out RNG for people trying to get their first set of gear together. As for dealing with the different specs, Benediction/Anathema was an awesome way of doing that and it would be nice to see Blizzard implement that on other stuff.
Which then got me thinking, Blizzard originally added quite a few class based zones. Think Ravenholdt hidden away in the hills, compete with its poison garden and staffed by persons of a dubious nature. Or Fray Island, the fight club of WoW where warriors would brawl to prove their worth and receive Berserker Stance. Then there is the Moonglade itself, the tranquil valley in which druids of both factions can meet, watched over by the Cenarion Circle. When Cataclysm rips a path through the world as we know it, WoW's very own disaster movie, I really hope that areas like those get a bit more love.
Also WTB a Priest hideout where Erinys can sip honeymint tea and debate the finer points of religion with other Priestly types. Although I suspect we might as well just rent a battleground or a graveyard because given the diversity of our faiths, someone is bound to end up dead.
What I loved about all three of these quests was the fact that they made you think about what it meant to play that specific class. Not only in terms of playstyle although all three required you to explore your spell book, perhaps the Druid and Hunter more so than the Priest, although plenty of people I knew did take Holy Nova especially for the quest but also from an RP perspective if you wished to indulge it.
With the Priest quest, you had to merge the dual nature of the class by acquiring both the eye of shadow and the eye of divinity before striking a balance between them.
You have come for redemption, yet you yourself do not realize this… Do you?
I never stated that you were here to redeem yourself, priest/priestess. You are here to redeem me and the innocents murdered while trying to escape Stratholme. My spirit and the spirit of those lost are bound here, cursed to endlessly relive our own tragic deaths.
and consider the slate wiped clean. I really would love to see the quest chain added back into the game, possibly with the Eyes being available through the Black Market.
I also loved the fact that you had to do it by yourself otherwise the "Cleaner" would come and squish you underfoot.
Be warned, you must complete this task alone. It is recommended that your allies remain at a distance. Interference by outside agents will result in a most severe punishment.
Although that plus the fact that Eris Havenfire would yell, alerting the entire zone Horde and Alliance alike that some brave Priest was attempting the quest did make it easier for the opposing side to grief.
Therefore it probably doesn't come as a huge surprise that the main aspect of the next patch which I'm really looking forward to is the "Proving Grounds". Yes I'd rather have the storytelling and romance of class quests but given that Blizzard understandably wishes to design for the masses not a percentage, I'll happily settle for that.


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Five Things – 08.15.16 – We Fly On, Untouched
Aug 15, 2016 ·
On one sunny afternoon in 1986 Vincent Price, dressed as a mailman, delivers a VHS tape to a kid named Matthew. The tape, Escapes, is a horror anthology hosted by Vincent Price. He starts watching it, and so do we. I'm not sure which level of inception we're at at this point.
It's really just five short horror stories wrapped by Vincent Price and given a strange intro and outro to make it make sense.
There are five stories here: Something's Fishy, Coffee Break, Who's There, Jonah's Dream, and Think Twice. None of them are particularly scary in the execution, and some are downright stupid, but they're all good snapshots of '80s cable video production. Coffee Break is probably my favorite of the batch; it feels like an early Stephen King short story minus the actual scary ending he probably would have written.
What strikes me about the ending (spoilers) is that characters from each of the stories come together in a "shocking" final scene, proving that this isn't just some acquisition effort at getting a bunch of unrelated stories and running them side-by-side. As an MST3k fan this video conjures up memories of Merlin's Shop of Mystical Wonders, which is actually slightly more involved in stitching the standalone stories together than this piece is but actually has a bunch of acquired, unrelated pieces in it – from different decades even. Still, same idea. It's worth a watch, there's something warm and familiar about it all.
https://www.youtube.com/watch?v=83zkzgm63r0
World War III, Part Two
The thrilling followup to last week's 1950s scare comic about World War III. This installment features battles in the air, on land, and under the sea… and doesn't really resolve much. It actually makes the story much more confusing as to what the makers of the comic's real agenda was. I've also never seen it spelled "Commy" before reading this comic.
Nintendo Interactive Retail Store Displays
This 1992 training video about maintaining Nintendo console in-store play units goes further than it needs to in the effort to entertain. Probably as far as it can. It's hard to believe that this is this guy's real voice – it sounds like the voice someone would use to make fun of this guy's real voice. That's a compliment, though, I think!
19th Century Obesity Ad
There's very little to appreciate about this fat-shaming newspaper ad from the 19th century, but the aesthetic appeal of the images and the wall of text do catch my eye.
Bluegrass 45
Current obsession: This 1970s Japanese Bluegrass band.
1950s, 1970s, 1990s, Five Things, Nintendo, television 1800s, comics, music, nuclear scare, vhs, video


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Železniční zastávka Troubelice zastávka (Nederlands: Spoorweghalte Troubelice zastávka) is een station in de Tsjechische gemeente Troubelice. Het station ligt aan spoorlijn 290 (die van Olomouc, via Šternberk en Uničov, naar Šumperk loopt). Het station is onder beheer van de SŽDC en wordt bediend door stoptreinen van de České Dráhy. Naast Troubelice zastávka ligt ook het station Troubelice in de gemeente Troubelice.

Troubelice zastávka


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Engelbert von Heinsberg-Valkenburg (* um 1220; † 20. Oktober 1274 in Bonn) war als Engelbert II. von 1261 bis 1274 Erzbischof von Köln.

Er war der jüngste Sohn des Grafen Dietrich I. von Heinsberg und Valkenburg († 1227) aus dessen zweiter Ehe mit Beatrix von Kyrburg, der Tochter des Wildgrafen Gerhard.

Engelbert wurde 1253 Archidiakon in Lüttich und 1257 Dompropst in Köln. Am 2. Oktober 1261 wurde er vom Domkapitel zum Erzbischof gewählt und damit Nachfolger von Konrad von Hochstaden.

Er versuchte seine Macht über Köln dadurch zu festigen, dass er die Patrizier bekämpfte, geriet jedoch 1263 für 20 Tage in Gefangenschaft und wurde 1268 aus der Stadt vertrieben. Er ließ daraufhin eine Residenz in Bonn errichten. 1266 sicherte er urkundlich den Juden von Köln gerechte Behandlung und die ungestörte Benutzung ihres Friedhofes an der Bonner Straße zu. Nach der Niederlage in der Schlacht bei Zülpich am 17. Oktober 1267 wurde Engelbert bis zum 16. April 1271 auf der Burg Nideggen des mit Köln verbündeten Grafen Wilhelm von Jülich gefangengehalten. Mit Hilfe eines Verräters (die Überlieferung nennt den in einem Haus an der Stadtmauer wohnenden "Schuster Havenith") wurde am 15. Oktober 1268 im Auftrage des Erzbischofs ein Durchlass unter der Stadtmauer gegraben. Der Angriff des mit den herrschenden Kaufmannsfamilien der "Weisen" rivalisierenden Patriziergeschlechts der Overstolzen erfolgte in der Nacht vom 14. auf den 15. Oktober 1268 (Nacht der "heiligen Mohren"), als die Männer des Erzbischofs an der Ulrepforte in die Stadt einzudringen versuchten. Der Plan wurde jedoch verraten und konnte deshalb vereitelt werden. Damit behielt die Stadt Köln ihre Unabhängigkeit.

Engelbert krönte Rudolf von Habsburg am 24. Oktober 1273 zum deutschen König. 1274 nahm er am Zweiten Konzil von Lyon teil.

Durch seine wenig durchdachte Politik hat Engelbert von Falkenburg wesentlich dazu beigetragen, dass unter seinem Nachfolger Siegfried von Westerburg nach der Schlacht von Worringen im Jahre 1288 die Stadtherrschaft der Kölner Erzbischöfe faktisch beseitigt wurde und sie nicht mehr in Köln residieren durften. Erst unter preußischer Herrschaft konnten die Kölner Erzbischöfe, aber nun als rein kirchliche Amtsträger, wieder in der Stadt residieren.

Engelbert fand seine letzte Ruhe im Bonner Münster.

Literatur

 Wilhelm Janssen: Das Erzbistum Köln im späten Mittelalter 1191–1515. Erster Teil (Geschichte des Erzbistums Köln, Zweiter Band).  Bachem, Köln 1995, ISBN 3-7616-1149-8, S. 174ff.

Weblinks

Anmerkungen

Erzbischof von Köln
Herzog (Westfalen)
Erzkanzler für Italien
Römisch-katholischer Bischof (13. Jahrhundert)
Dompropst (Köln)
Geboren im 13. Jahrhundert
Gestorben 1274
Mann


You're Invited For a Special Event at Jack London Square!
We hope to see you! Our Founder, Patrick Martins, Director of Marketing, Alexes McLaughlin, and Director of Mail Order, Catherine Greeley, will all be there for this very special event. We look forward to meeting you and sharing a drink together.
The post You're Invited For a Special Event at Jack London Square! appeared first on HERITAGE FOODS USA.


Amsterdam Renovaties' professional team has more than 15 years experience. We are proud of our work and therefore you can be sure that we will finish the whole project to the highest standards: from the tiles on your floor to the tiles on your roof. During the entire renovation, we stay in touch with you so that you always know what is happening.
If your house is in need of renovation, then get in touch with us today to discover what your options are. We will visit your house, so that we can discuss all your options. Best of all: this consultation service is completely free.


Wipe New becomes the new brand in automotive care.
Avento focused on a novel concept that many people wanted their car to look clean and new without having a car wash/detailing/cleaning every few days to achieve it. With some price testing on optimal media, Wipe New was ready to hit the airwaves to restore and protect cars nationwide.
Built on experience with automotive products, Diray Media implemented a strategy of :120 and :60 spots with a focus on automotive and restoration programming that had significant direct-response rates. The campaign was a hit on TV and later moved into retail with strong sales quarter after quarter.
After finding success with the original Wipe New, which focused on the interior of a car, Avento wanted to use their brand strength to launch additional cleaning products. This lead to both Headlight and Tire, which focused on the exterior of the car, and the ReColor, which took direction toward furniture. Diray Media was able to launch these new products while maintaining a campaign for the original for both brand and retail support which continue to this day.
Wipe New was able to leverage its brand name and documented success on TV to partner up with Rust-Oleum, one of the leading manufacturers of premium consumer and industrial paint and coating products. The agreement will revitalize the sales and marketing activities and grow Wipe New's overall market share.
Diray has continually been able to deliver the right media and discover new outlets for our demo-sensitive campaign. Even when our consumer group is limited, Diray helps us succeed through insightful testing and marketplace exploration.