text:
\section{Introduction}

Recent works have demonstrated an interest in unsupervised representation learning as a pretraining method to obtain good speech features for downstream tasks with little labelled data \cite{schneider2019wav2vec,kahn2020,riviere2020multi,kawakami2020robust,wang2020}. While Contrastive Predictive Coding (CPC) and derivatives appear to be versatile methods for unsupervised representation learning \cite{oord2018representation,chung2019,chung2019generative}, they do not yet reach the state-of-the-art (SOTA) results on purely unsupervised learning metrics \cite{kahn2020,oord2018representation,versteegh2016,dunbar2017}.

Data augmentation is useful for supervised training, and is also a key component in unsupervised setups in the image domain~\cite{dosovitskiy2014discriminative,chen2020simple}. It is not well established in unsupervised learning for speech, where the sequential nature of the signal may introduce specificities.

Our first objective is to explore several types of time-domain data augmentation (additive noise, masking, reverberation) and several methods for augmenting in the contrastive framework (in the past, future, or both) in English (LibriSpeech).
In a second stage, we extend the results to other languages (French and Mandarin) in the zero-resource 2017 benchmark~\cite{dunbar2017}.
Lastly, we show that data augmentation benefits semi-supervised training, using the Libri-light benchmark~\cite{kahn2020}.

\section{Related work}

{\bf CPC.}
Van den Oord et al.~\cite{oord2018representation} introduced Contrastive Predictive Coding, a method for unsupervised representation learning.
Applied to speech, CPC trains a convolutional encoder and a predictor for future embeddings of the encoder.
To prevent mode collapsing, the loss is contrastive: an embedding should be close to positive future embeddings and distant from negative future embeddings.
CPC was used as pretraining for ASR~\cite{schneider2019wav2vec} and speaker identification~\cite{lowe2019,lai2019}.
Non-contrastive versions of predictive coding with fixed embeddings can learn generic multi-task representations~\cite{chung2019,chung2019generative}.
Here we use a deeper and optimized version of the CPC implementation of ~\cite{kahn2020, riviere2020multi}.

\noindent
{\bf Data augmentation for ASR.}
Basic time-domain augmentations modify the sampling rate of the input by a small factor ($\pm 10\%$), which changes both the duration and pitch~\cite{ko2015audio}.
Another one consists of adding noise, convolved with a room impulse response function to simulate point sources spread in space~\cite{ko2017rev}.
SpecAugment~\cite{park2019specaugment} is a spectral-domain augmentation whose effect is to mask bands of frequency and/or time.
We introduce WavAugment, that implements these augmentations in the time domain and is optimized for applying augmentations on-the-fly as part of data loading.

Our work is close to~\cite{ravanelli2020multi}, which applies data augmentation techniques to representation learning (autoencoders).
However, they evaluated them in terms of pretraining for a downstream task not in terms of the learned representation

\vspace{-0.7em}
\section{Method}

Our method is based on a state-of-the-art CPC architecture~\cite{riviere2020multi}. We explore how to perform data augmentation and introduce the WavAugment package.

\vspace{-0.5em}
\subsection{The CPC2 architecture}
\label{ss:cpc_arch}

The architecture is summarized in Figure~\ref{fig:CPCaug}.
A convolutional encoder network produces a representation $z_t$ of the raw audio waveform.
The sequence $(z_t)$ is then passed to a recurrent context network to build our final representation $c_t$.
At each step, we apply $c_t$ to a predictor neural network $Pred$ with several outputs $Pred^k$ each one reconstructing future representations $z_{t+k}$ ($0< k \leq K$, $K=12$). The loss is contrastive and tries to minimize the dot product between the predicted and correct future representation while maximizing the dot product with a sample of 128 negative examples $\mathcal{N}_{t,k}$ taken from the batch.
This gives the following loss:
\vspace{-0.3em}
\[\mathcal{L} = \frac{1}{K} \sum_{k=1}^K \log \frac{\exp ( Pred^k(c_t)^T z_{t+k})}{\sum_{n\in \mathcal{N}_{t,k}} \exp (Pred^k (c_t)^T z_{n})}\]
\vspace{-0.3em}

CPC2 is a modified version of the CPC architecture in~\cite{kahn2020,riviere2020multi}. The encoder
architecture is unchanged (5 convolutional layers with kernel sizes [10,8,4,4,4], strides [5,4,2,2,2] and hidden dimension 256). We increase the depth of the auto-regressive network, which improves accuracy (see Supplementary Table \ref{tab:archi_ablation})
For the recurrent context nextwork, we use a 2-layer LSTM, as a tradeoff between feature quality and training speed.
In the prediction network, we replace the $k$ independent transformers in \cite{kahn2020,riviere2020multi}, each one predicting a specific time-step ahead, to a single multi-head transformer layer with $k$ classifiers at its heads.
This has a limited impact on accuracy but dramatically decreases training time.

\subsection{Data augmentation and CPC}
\label{ss:past_future}

As discussed in Section~\ref{ss:cpc_arch}, the encoded representations $z_t$ are used in two ways:
(a) to calculate the contextual representation $c_t
, and
(b) as target predictions (positive or negative candidates)
 We refer to the representation $z_t$ as \textit{past} and the targets $z^+, z^-$ as \textit{future}.
The model predicts \textit{future} representations  based on its \textit{past}, by learning to discriminate it from a samples of negative candidates (Figure~\ref{fig:CPCaug}).

We can apply two different augmentations on the same speech sequence and use them to calculate \textit{past} and \textit{future} representations.
This separation opens a plethora of possibilities for data augmentation:
applying the same augmentation on all sequences in the batch (on query sequence and all positive and negative candidates);
augmenting each sequence independently (\textit{past} and \textit{future} have identical augmentations, but negatives have independent augmentations);
augmenting only \textit{past};
augmenting only \textit{future};
augmenting both \textit{past} and \textit{future} independently (\textit{past+future} setting).
Preliminary experiments demonstrated that the most promising approaches are either augmenting only the \textit{past} representation or applying two independent augmentations on both \textit{past} and \textit{future} (\textit{past+future}).
In this work, we therefore focus on these two options.

\subsection{WavAugment}

\begin{figure}[t]
  \centering
  \includegraphics[width=7.0cm]{CPCaug.pdf}
  \vspace{-1.3mm}
\caption{\textbf{CPC-based data augmentation}. Each speech sequence is encoded twice, one for \textit{past} one for \textit{future}, with potentially different augmentations for each. The CPC loss tries to contrastively predict \textit{future} embeddings $z_{t+i}$ based on \textit{past} ones, ignoring the noise of the augmentation. Positive and negative sequences may have different augmentations.}
\label{fig:CPCaug}
\vspace{-1em}

\end{figure}

Our experimental setup requires to apply independent data augmentations on short  audio sequences ($\approx 1$ s). For this, we developed WavAugment, a library that implements time-domain augmentations. WavAugment is publicly available at~{\color{blue}\href{https://github.com/facebookresearch/WavAugment}{github.com/facebookresearch/WavAugment}}.
WavAugment builds upon a C++ API to \textit{libsox}\footnote{http://sox.sourceforge.net/sox.html} that implements dozens of audio processing transformations. WavAugment has a Pytorch~\cite{Pytorch2019} interface and Pytorch- and libsox-based effects can be interleaved transparently.

\subsection{Datasets and evaluation measures}\label{sec:dataset}

In the experiments reported below, unless reported otherwise, we trained our CPC model on Librispeech-100h~\cite{librispeech}, which is a set of short sentences in good quality (clean) read speech, from a balanced set of speakers. We directly used all of the files, without filtering or modification. In Experiment~2, we introduce two similar datasets in French and Mandarin, respectively. The French dataset was created by selecting the French data from the Librivox website\footnote{\url{https://librivox.org/}}, and Mandarin from the MagicData dataset~\cite{magicdata}. The recordings were cut into ``utterance''-like segments using pyannote's  Voice Activity Detector \cite{Bredin2020}. Both datasets had a similar number of speakers and total duration as Libri-Speech (250 speakers, 76h and 80h respectively).

We tested the learned representation using the Libri-light~\cite{kahn2020} ABX metric for unsupervised representation learning. This distance-based metric estimates the probability that a speech segment $X$ is closer to a segment $A$ with the same transcription than to a segment $B$ with a different transcription. The distance is the DTW-realigned average angle (arc-cosine of the normalized dot product) between each frame
. The test uses minimal pairs of triphones that only change in the central phoneme ('bet' vs 'bit'), and is conducted within-speaker ($A$, $B$ and $X$ are from the same speaker) and across-speaker ($A$ and $B$ are from one speaker, $X$ from another one). This metric has been shown to be useful to analyse the linguistic content of speech features without having to train a classifier \cite{schatz2016abx}, and has been used in the Zero Resource Challenge series \cite{versteegh2016,dunbar2017,dunbar2019}.
\section{Experiments}

\subsection{Preliminary Experiment: Tuning data augmentation}

We focus on 5 augmentations that were either proposed earlier~\cite{park2019specaugment} or that can potentially inject useful invariances in the speech representations.
We selected: pitch modification (\dan{pitch}), additive noise (\dan{add}), reverberation (\dan{reverb}), band reject filtering (\dan{bandrej}), and time masking (\dan{tdrop}). The last two augmentations are similar to those used in SpecAugment~\cite{park2019specaugment}.
The \dan{pitch} can be attributed to the \textit{source} (how the speaker talks), \dan{add} and \dan{reverb} to the \textit{communication channel}, and \dan{bandrej} \& \dan{tdrop} to noise in the \textit{neural representation} of the speech.
When we compose augmentations (indicated by '+'), they are applied in that order.

\label{ss:design}
In pilot experiments we calibrated the strength of the augmentations looking at the overall ABX results (within and across on the dev clean and other set of Libri-light \cite{kahn2020}.
For \dan{pitch}, the applied change in the pitch is an integer sampled uniformly between +300 and -300 (the change value is measured by 1/100 of a tone).
In \dan{reverb}, we uniformly sample \textit{room-scale} between 0 and 100, fixing other parameters to defaults.
\dan{tdrop} zeroes out one random subsequence of length 50ms.
We found that \dan{bandrej} performs best when we set the maximal width of the rejected spectrum to $150$ Hz.

We discovered accidentally
that for additive noise, low frequencies are more effective than high frequencies.
We therefore explored systematically the effect of the spectral characteristics of noise by filtering sounds from the MUSAN dataset~\cite{musan2015}
in successive frequency bands. We selected 5 broad bands, defined by 4 cutoff points by the tripling of the frequency: 80Hz, 240Hz, 720Hz, 2160Hz).
We found that the optimal additive noise was obtained by bandpass filtering MUSAN sounds in the $[80,240]$ Hz range, which corresponds roughly
the human F0 (see Supplementary Table~\ref{tab:add})
\begin{table}[]
\begin{center}
\begin{tabular}{l c @{\hspace{0.8\tabcolsep}} c @{\hspace{0.8\tabcolsep}} c@{\hspace{0.5\tabcolsep}}c @{\hspace{0.8\tabcolsep}} c @{\hspace{0.8\tabcolsep}}  }
\toprule
  & \multicolumn{2}{c}{{Within spk.}}  && \multicolumn{2}{c}{{Across spk.}} \\
\cline{2-3}\cline{5-6}
                          &dev       &dev   && dev  &dev\\
System                    &clean     &other && clean&other\\
\midrule
MFCC Baseline           &   10.95 &  13.55 &&  20.94 & 29.41 \\
CPC LL-60k  \cite{kahn2020} &    6.11 &   8.17 &&   8.05 & 12.83 \\
\midrule
\midrule
\multicolumn{6}{l}{\emph{Single augmentations (CPC2 on LibriSpeech clean 100h)}}\\
no augmentation     &    6.06 &   8.18 &&    7.59 &    12.84 \\
\midrule
pitch-past          &    4.90 &   6.28 &&	 6.84 &	   11.04 \\
pitch-past+future   &    5.03 &	  6.35 &&	 7.11 &	   11.30 \\
\midrule
add-past            &    5.47 &   7.58 &&    6.97 &    12.17 \\
add-past+future     &    5.16 &   7.33 &&    6.77 &    11.71 \\
\midrule
reverb-past         &    5.55 &   7.61 &&    7.16 &    12.19 \\
reverb-past+future  &    5.58 &   7.91 &&    7.77 &    13.07 \\
\midrule
bandrej-past        &    5.83 &   7.88 &&    7.07 &    12.21 \\
bandrej-past+future &    5.92 &   7.81 &&    7.19 &    12.24 \\
\midrule
tdrop-past         &    5.78 &   7.92 &&    7.18 &    12.56 \\
\midrule
\midrule
\multicolumn{6}{l}{\emph{2-way combinations, past only (same model and train set)}}\\
pitch+add          &  4.81 & 6.03 && 6.79 & 10.90 \\
pitch+reverb       &  4.74 & 6.75 && \textbf{6.06} & 10.99 \\
pitch+tdrop        &  4.83 & 6.15 && 6.90 & 11.08  \\
add+reverb         &  5.41 & 6.87 && 7.41 & 11.97 \\
add+tdrop          &  5.38 & 6.97 && 7.70 & 12.22 \\
reverb+tdrop       &  5.41 & 6.93 && 7.32 &	12.05  \\
\midrule
\multicolumn{6}{l}{\emph{3-way combinations, past only (same model and train set)}}\\
pitch + add + reverb    &\bf 4.66 &\bf 5.81 &&   6.62 &\bf 10.60 \\
pitch + add  + tdrop    &    4.86 &    6.09 &&   6.70 &    10.78 \\
pitch + reverb + tdrop  &    4.72 &    6.02 &&   6.53 &    10.70 \\
add + reverb + tdrop    &    5.40 &    6.87 &&   7.47 &    11.98 \\
\midrule
\multicolumn{6}{l}{\emph{4-way Combinations, past only (same model and train set)}}\\
pitch+add+reverb+tdrop  &    4.87 &    6.08 &&   6.79 &    10.76 \\
\bottomrule
\end{tabular}
\caption{\textbf{ABX errors on data-augmented CPC features (Libri-light dev set).} Within- and across-speaker phoneme discriminability scores (lower is better) on the Libri-light clean and other dev sets for CPC training as a function of types of data augmentation, in isolation or combination (see Section~\ref{ss:design}).
\label{tab:effects}
}
\end{center}\vspace{-20pt}
\end{table}

\vspace{-5pt}
\subsection{Experiment 1: Data augmentation combinations}
\label{sec:daeffects}
\vspace{-1pt}

We first tested these five augmentations alone, either applying them to the \textit{past} of the sequence or independently to \textit{past} and \textit{future} (\textit{past+future}) (see Section~\ref{ss:past_future}).

On analyzing single augmentations in Table~\ref{tab:effects}, we first observed that in many cases applying augmentations on \textit{past} performs as well as, or even better, than \textit{past+future} (\dan{pitch}, \dan{reverb}, \dan{bandrej}). The only augmentation performing better on \textit{past+future} is \dan{add}.\footnote{We did not experiment with \dan{tdrop} applied on \textit{past+future} as this will zero out the predicted sequences.}
According to their average performance, the individual augmentations can be sorted, from most to least useful: \dan{pitch}, \dan{add}, \dan{reverb}, \dan{tdrop}, and  \dan{bandrej}.

Next, we study the performance of combinations of augmentations. We decided to drop \texttt{bandrej} from consideration due to its poor results. We only consider augmenting \textit{past}, as this gives roughly the same quality of representations, but requires less computation.
As a result, we have 6 possible two-way, 4 three-way, and 1 four-way combination of effects.
The results are in the lower part of Table~\ref{tab:effects}, and they show that \texttt{pitch+add+reverb} performs best in 3 out of 4 metrics.

We chose this combination and evaluated the corresponding model on the Libri-light test set. The results are reported in Table~\ref{tab:best} and show that, across all metrics, data augmentation yields relative improvements of 18-22\% over no augmentation, and ends up with better results than the original CPC algorithm run on the much larger 60k hours dataset.

\begin{table}[]
\begin{center}
\begin{tabular}{l c @{\hspace{0.8\tabcolsep}} c @{\hspace{0.8\tabcolsep}} c@{\hspace{0.5\tabcolsep}}c @{\hspace{0.8\tabcolsep}} c @{\hspace{0.8\tabcolsep}}  }
\toprule
  & \multicolumn{2}{c}{{Within spk.}}  && \multicolumn{2}{c}{{Across spk.}} \\
\cline{2-3}\cline{5-6}
                          &test       &test  && test&test\\
System                    &clean     &other && clean&other\\
\midrule
MFCC Baseline       &  10.58    & 13.60    && 20.45 &  28.5  \\
CPC LL-60k   \cite{kahn2020}& 5.83  & 8.14  && 7.56&  13.42 \\
\midrule
\multicolumn{4}{l}{\emph{Trained on Librispeech-100h}}\\
CPC2        &  5.69     &   8.42   &&   7.26   &   13.42 \\
CPC2+WavAug        &    \bf 4.46   &  \bf 6.56   && \bf 5.90 & \bf 10.95 \\
\bottomrule
\end{tabular}
\caption{\textbf{ABX errors on data-augmented CPC features (Libri-light test sets).} Within- and across-speaker phoneme discriminability scores (lower is better) on the Libri-light test sets for our best augmentation (\texttt{pitch}+\texttt{add}+\texttt{reverb}-\texttt{past}).}\label{tab:best}
\end{center}\vspace{-20pt}
\end{table}

\vspace{-5pt}
\subsection{Experiment 2: Extending to other languages}
\vspace{-1pt}

In this experiment, we tested whether our data augmentation technique could be extended to other languages. We selected the three dev datasets of the ZeroSpeech Challenge 2017, covering English, French, and Mandarin.
As in the previous experiment, the metrics are the within- and across- ABX test provided by the Challenge.
For training, we used both the small in-domain training sets provided by the Challenge (45h, 24h, and 2h30, respectively), and our own, larger, out-of-domain training sets. For English, we used Librispeech-100 (100h), for French, the 76h of French-librivox, and Mandarin, the 80h of MagicData described in Section \ref{sec:dataset}. We also observed, training on Librispeech-100 and testing on Libri-light dev, that using larger datasets in combination with data augmentation allowed to benefit from increasing the number of LSTM layers to 3 (see Supplementary). We included this modification in the experiments.

The results are shown in Table \ref{tab:zr}. As can be seen, while noise augmentation improves the score on all three languages, we cannot reach the SOTA with the small training datasets provided from the challenge. We can however, be on par with or improve  over best performing baseline with our out-of-domain train sets (same languages, larger datasets), in particular with the larger model. This shows that while our technique scales with dataset size, it is still less data efficient than the techniques described in Heck et al.~\cite{heck2017} and Choroskwi~et~al.~\cite{chor2019}. Note however, that both studies used speaker adaptation which are outside the scope of what can be done with standard time domain data augmentation techniques.

\begin{table}[h]
\centering
\begin{tabular}{l@{\hspace{0.7\tabcolsep}} c@{\hspace{0.7\tabcolsep}}c@{\hspace{0.7\tabcolsep}} c@{\hspace{0.3\tabcolsep}} c@{\hspace{0.7\tabcolsep}}c@{\hspace{0.7\tabcolsep}} c@{\hspace{0.3\tabcolsep}} c@{\hspace{0.7\tabcolsep}}c@{\hspace{0.7\tabcolsep}}c}
\toprule
& \multicolumn{2}{c}{English} && \multicolumn{2}{c}{French} && \multicolumn{2}{c}{Mandarin} \\
\cmidrule{2-3}\cmidrule{5-6}\cmidrule{8-9}
                                    & W. & A.     && W.    & A.    && W. & A.  &AVG\\
\midrule
\multicolumn{9}{l}{\emph{Trained on ZeroSpeech2017 (45h, 24h, 2h30, resp.)}}\\
Superv. topline~\cite{dunbar2017}   &   5.3 &    6.9 &&   6.8 &   9.1 &&    4.2 &   5.7 &6.33\\%6.33
Heck et al.~\cite{heck2017}         &   6.2 &    8.7 &&   8.7 &  11.7 &&  7.9 &\bf 7.4&8.43\\%8.43
Chorow. et al.~\cite{chor2019}    &   5.5 &    8.0 &&\bf 7.5&\bf 10.8&&   10.7 &  11.2 &8.95\\
\midrule
CPC2                                &   8.6 &   12.0 &&  12.2 &   16.4 &&  12.0 &  14.0 &12.53 \\
CPC2+WavAug                         &   6.6 &    9.3 &&   9.3 &   14.1 &&  11.2 &  11.9 &10.4\\
\midrule
\midrule
\multicolumn{9}{l}{\emph{Trained on out-of-domain (100h, 76h, 80h, resp.)}}\\
CPC2                                &   6.1 &    8.7 &&  10.3 &   12.9 &&   9.3 &   9.6 &9.48\\
CPC2+WavAug                         & 4.7& 6.5 &&   8.6 &   11.1 &&  7.9&   7.8 &  7.77\\
CPC2-3L+WavAug                   &\bf 4.6 &\bf 5.8 &&   7.6 &   10.9 && \bf 7.8 &   8.0 &\bf 7.45 \\
\bottomrule
\end{tabular}
\caption{
\textbf{ABX errors on the ZeroResource Speech Challenge 2017 (120s).}
Within- (``W.'') and across-speaker (``A.'') phoneme discriminability scores on English, French and Mandarin speech for CPC features with and without data augmentation. For comparison, the best systems plus supervised topline of the ZeroSpeech leaderboard trained on the provided datasets.}
\label{tab:zr}
\vspace{-10pt}
\end{table}

\vspace{-5pt}
\subsection{Experiment 3: Pretraining and limited supervision}
\vspace{-1pt}

In this experiment, we test whether our data augmentation technique can build better speech features that can be used for downstream tasks.
Here, we use the Libri-light limited supervision phone classification task \cite{kahn2020}, which contains intentionally small training sets (10 min, 1h or 10 hours of labelled data).
We fine-tune a linear phone classifier built on top of the CPC features with a CTC loss (frozen features). On 10 hours of data, we also fine-tune the entire network. Again, we additionally experiment with an architecture that has a 3-layer LSTM (CPC2-L3) (See Supplementary Table \ref{tab:archi_search}).

The results are in Table \ref{tab:semi} and show an effect of signal-based data augmentation, both for pretraining and for fine tuning.
For the supervised fine-tuning phase, we found out that we got the best results by using only pitch augmentation.
 Other methods having low or negative effects in this case. The combined effects of data augmentation on pretraining and fine-tuning adds up to 12-15\% relative improvement across the different training sets.
Interestingly, we find that with data augmentation we can beat the reference baseline (pretraining on 60k hours plus fine tuning on 10 hours) on frozen features with substantially less data (pretraining on 100 hours, plus fine tuning on 1 hour).
Another point worth mentioning is that with data augmentation, 10 minutes of data on frozen features is sufficient to outperform the no-pretraining reference
with 10 hours of labels.

\begin{table}[h]
\begin{tabular}{l cc @{\hspace{0.8\tabcolsep}} c @{\hspace{0.8\tabcolsep}} c @{\hspace{0.8\tabcolsep}} c }
\toprule
                     &Augmented& dev-      & dev-    & test-     & test-\\
System               &fine-tuning& clean     & other   & clean     & other\\
\midrule
\multicolumn{5}{l}{\emph{Reference}}\\
\multicolumn{2}{l}{CPC unlab-60k+train-10h-full} & 28.4& 41.4 & 27.9 & 43.6 \\
\multicolumn{2}{l}{CPC no pretraining - 10h-full}  & 45.9& 55.7 & 43.7 & 58.6 \\
\multicolumn{2}{l}{CPC2 no pretraining - 10h-full} & 41.3& 52.3 & 39.3 & 56.1 \\
\midrule
\midrule
\multicolumn{5}{l}{\emph{Frozen features - classifier trained on 10min}}\\
\multirow{2}{*}{CPC2}       &No & 47.8 & 60.9 & 47.0 & 60.1\\
                            &Yes& 49.4 & 57.9 & 49.4 & 59.2\\
\multirow{2}{*}{CPC2+WavAug}&No & 39.5 & 51.3 & 39.1 & 52.4\\
                            &Yes& 41.6 & 51.7 & 41.7 & 52.9\\
\midrule
\multicolumn{5}{l}{\emph{Frozen features - classifier trained on 1h}}\\
\multirow{2}{*}{CPC2}       &No & 34.6 & 47.5 & 32.9 & 50.0\\
                            &Yes& 33.5 & 46.9 & 32.7 & 49.4\\
\multirow{2}{*}{CPC2+WavAug}&No & 29.1 & 42.4 & 28.8 & 44.3\\
                            &Yes& 28.0 & 41.3 & 27.8 & 43.3\\
\midrule
\multicolumn{5}{l}{\emph{Frozen features - classifier trained on 10h}}\\
\multirow{2}{*}{CPC2}       &No & 29.3 & 43.7& 29.0 & 47.1\\
                            &Yes& 31.1 & 44.9& 30.6 & 48.3\\
\multirow{2}{*}{CPC2+WavAug}&No & 26.1 & 39.9& 25.7 & 41.6\\
                            &Yes& 25.7 & 39.3& 25.3 & 41.2\\
\midrule
\multicolumn{5}{l}{\emph{Full fine-tuning, 10h of data}}\\
\multirow{2}{*}{CPC2}       &No & 27.8 & 42.6 & 26.5 & 45.0\\
                            &Yes& 26.3 & 39.9 & 25.4 & 43.9\\
\multirow{2}{*}{CPC2+WavAug}&No & 24.5 & 39.0 & 24.1 & 40.8\\
                            &Yes& 23.5& 37.6& 23.1&41.0\\
\multirow{2}{*}{CPC2-L3+WavAug}&No & 22.9 & 37.3 & 22.8 & \bf{39.9}\\
                            &Yes&\bf 22.5 &\bf 36.8 &\bf 22.2& \bf{39.9}\\
\bottomrule
\end{tabular}
\caption{\textbf{Phone Error Rate (PER) in the semi-supervised setting.} A linear classifier is added on top of Librispeech-100 pretrained CPC2 models and fine tuned with either 10min, 1h or 10h of Libri-light labelled data with a CTC loss.  For comparison, reference Libri-light results plus the untrained CPC2 architecture fully fined-tuned with 10 h.
}
\label{tab:semi}
\vspace{-10pt}
\end{table}

\section{Discussion}
We have introduced WavAugment, a library for time-domain data augmentation and illustrated its use in the context of unsupervised contrastive representation learning, and in the context of learning with limited supervision. We found that pitch and additive noise are the most powerful data augmentation techniques for our implementation of contrastive predictive coding, yielding very good results in unsupervised representation learning in English, Mandarin and French. We further showed that these gains extend to fine tuning on very limited data yielding gains in PER. Interestingly, the two most popular data augmentation techniques that are typically done in the spectral domain (as in SpecAugment)  do not work very well for CPC training. Furthermore, pitch and additive noise are techniques that can only be applied in the time domain. Further work will allow to determine whether the superiority of time domain noise augmentation over spectral ones is specific to the CPC loss or to the fact that our architecture starts directly from the waveform as opposed to using spectral features like Mel Filterbanks or MFCCs. Note that~\cite{ravanelli2020multi} also combines several data augmentation techniques for unsupervised learning in an autoencoder architecture. Among data augmentation technique they use the most are two time-domain ones (reverberation and additive noise) and one spectral (band reject). It remains to be seen how pitch would fare in such a pretraining setup.

\section{Conclusion}
With data augmentation, CPC can take good advantage of relatively short (around 100 hours) clean and well segmented speech, although it is currently insufficient to learn competitively with very small amounts of data (between 2.5 and 50 hours). More research is needed to extend such techniques in both directions: with small amounts of data, and with very large, and potentially more noisy datasets.  In addition, the differences that we observe between data-augmentation effects open the issue of more systematic exploration of data augmentation as a function of tasks and architectures.

\let\oldbibliography\thebibliography
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  \oldbibliography{#1}%
  \setlength{\itemsep}{0pt}%
}
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redpajama_set_name: RedPajamaArXiv


text:
Home Article THE POPULAR VOTE.
THE POPULAR VOTE.
"Tomorrow is the very last day Americans will have the chance to vote in this hard-fought and historic race for the Democratic nomination," reads the latest e-mail from the Clinton campaign. "Every vote we receive in South Dakota and Montana will help us add to our popular vote total." It's a little peculiar to even have to say this, but the Democratic presidential nomination is decided by delegates, not the popular vote. There's a good argument to be made that it should be decided by the popular vote, but for now, it isn't. And so both candidates pursued strategies meant to attain the necessary number of delegates. If the "votes" in the non-election in Michigan and the no-campaign election in Florida were going to matter, the two candidates would have campaigned in both places. If the popular vote was the key, the Obama camp would have ignored small state caucuses and spent that money running up their totals in larger states like Illinois. Indeed, to get a sense for how contingent it all is, head over to Poblano's place where he's got an unbelievably cool little gadget that lets you measure the popular vote while changing different assumptions and variables (like how to count Michigan, Puerto Rico, etc). All these questions and uncertainties exist because the primary process is not set up to measure the popular vote. It's set up to measure delegates. At the end of the day, you have to judge the game based on the rules set down at the beginning. Trying to decide the election based on the popular vote is like demanding that the NBA finals be decided based on which team brought more of its supporters to the arena. You can argue that that's the more relevant achievement if you want, but if that had been the metric from the beginning, then the two teams would have been out recruiting supporters and not on the court shooting free throws. The Clinton campaign is trying to change the game. But that's something you do before the season starts. Not 30 seconds before the final buzzer.

redpajama_set_name: RedPajamaCommonCrawl


text:
The Choir With No Name: London homeless choir records debut album and supports Coldplay
Filed Under: Education, Fashion, Humanity, News, People
They're called the Choir with No Name – a modest name certainly for a group of people whose achievements are anything but small.
The members of this London-based choir are people who have experienced homelessness – and now they're launching their debut album and will support Coldplay at two gigs this month.
Founded in 2008 by Marie Benton, The Choir's success in London means plans are beginning for UK-wide expansion into other UK cities, starting with Birmingham in 2011.
Marie explained: "I founded the choir because singing makes you feel good, about yourself and the world. I was determined that the members wouldn't be short-changed on the quality of the music; they've been short-changed on so many other things in life. Our album's amazing, and Chris's video captures the members' beautiful and quirky personalities in all their glory. We're really excited about sharing them with the world.
"Our London choir has proved to us time and again that the choir works. We'd love to be able to expand that to help homeless people across the UK, and so our plan is to pilot another choir in Birmingham from Spring/Summer 2011, and prepare for a wider roll out across the UK in 2013. In Birmingham what we need first is to recruit the right Choir Director – someone with real passion, musical talent, drive and ambition, and above all a good sense of humour!"
The choir's debut, The Album with No Name, was recorded at Livingston Studios in North London in September – home to The Magic Numbers and Bjork, among others. It features tracks such as Radiohead's Street Spirit, and seasonal favourite The Pogues and Kirsty MacColl's "Fairytale of New York".
Read the full story on the Optemistworld.com website


Six decades of experience constitute our foundation for innovative ideas and high quality products.
Signs: a simple term, yet associated with a wide array of possibilities. While we tend to initially think of traffic and informational signs, the spectrum is nearly endless. Instructional signs, doorbell signs, practice signs, illuminated advertising, restaurant signs, door signs, company signs, routing signs in buildings, number signs, advertising stickers, escape route signs... the list goes on and on. And we cover this diverse spectrum in its entirety. We work with all of this diversity. We have solutions for this entire spectrum of diversity. And throughout this entire spectrum of diversity, we develop individualised solutions tailored to your needs, regardless of whether you need merely a small sign for the door to your home, or are looking to post signs throughout an entire hotel. For more than sixty years now, we've been providing customers with specially tailored solutions for all their needs. Our know-how, the joy we find in innovation, our creativity and quality have turned us into a leading manufacturer of these fascinating products on a regional, national and international level.
In screen printing, we particularly produce high print runs of standard signs. Safety signs, including signs with afterglow, as well as instructional and prohibition signs in line with the applicable DIN standards. Moreover, the screen printing process is great for materials that are otherwise difficult to print, such as ceramics and metals.
Digital printing facilitates affordable printing on a variety of materials in small and extremely small print runs. That means realistic, photo-like print quality is made possible. In special cases, digital printing facilitates special effects, such as embossed lettering to make it possible for blind people to feel them. Digital printing is a young, yet highly innovative printing process whose possibilities are currently sky-rocketing.
As part of its internationalisation strategy, Marahrens Group has opened a branch in Turku, Finland. The branch will be able to serve Nordic customers – especially from the maritime industry – more flexible and offers a local contact.
At the site in Turku, own production and warehouse facilities will be available to handle orders in an optimal and efficient manner. The team of experienced sign manufacturers will be supported by the mother entity from Bremen.
The product portfolio includes standard and customized signs for the maritime industry as well as solutions for the onshore segment.
"We are very excited to extend our European coverage with the branch. Since we have successfully executed projects in Finland in the past, an own branch was the next and logical step to intensify our activities in Finland", stated Janina Marahrens-Hashagen, Group CEO.
In March 2017, H. Marahrens Group has established a US company headquartered in Miami, Florida, which commenced regular business operations in September.
The US maritime and land-based market will be supplied with the proven product portfolio of the Marahrens Group covering standard and customized signs as well as IMO-signage.
Own operational staff on site will be able to be close to customers and enable efficient support to find the optimal signage solution in all matters. Professional installation services are part of the offering.
We are very excited about the new chapter in the history of H. Marahrens Group that the internationalization across the Atlantic is opening for us and are in eagerly looking forward to new opportunities.
Who are the people behind the scenes at Marahrens? Today I want you to meet Friederike. She works as a Business Development Manager in the USA and tells us what her tasks are, why she works for us and what you can find on her desk.


text:
Q: Start setInterval again after clicking on button i  started js a few days ago and made a slider that changes the images when the button is clicked and a timer that changes images automatically every 5 sec. But when i clicked the buttons the timer didnt reset. I used clearInterval to stop the timer but i dont know how to call it to restart it. Here is my Js code.
//progression bar
var width = 1;
//the images
const images = [
    "https://www.travelercar.com/wp-content/uploads/2016/04/4a36e314016aa914f203ea6b7d579dc6_large.jpeg",
    "https://lemag.nikonclub.fr/wp-content/uploads/2017/07/08.jpg",
    "https://www.yourvalleynews.co.uk/wp-content/uploads/2018/03/pic-outside-1080x675.jpg",

  ];

//the buttons
const suivant = document.getElementById('button1');
const precedent = document.getElementById('button2');

//change image every 5 sec
var counter = 0;
var imageChange;
imageChange = setInterval (function () {
    if (counter >= 2 ){
        counter -= 2;
        document.getElementById("currentImage").src = images[counter];
    }
    else if (images[0]){
        document.getElementById("currentImage").src = images[++counter];
    };
}, 5000);

//make buttons work
  precedent.onclick = function() {
    width = 0;
    clearInterval(imageChange);
    //I know i have to put something on this line with v imageChange but i dont know what
    if (counter <= 0){
        counter += 2;
        document.getElementById("currentImage").src = images[counter];
    }
    else{
        document.getElementById("currentImage").src = images[--counter];
    };
  }

  suivant.onclick = function() {
    width = 0;
    clearInterval(imageChange);
    //I know i have to put something on this line with imageChange but i dont know what
    if (counter >= 2 ){
        counter -= 2;
        document.getElementById("currentImage").src = images[counter];
    }
    else{
        document.getElementById("currentImage").src = images[++counter];
    };
  }

//progression bar
function progression(){
    var progres = document.getElementById('progression');
    var temps = setInterval(frame, 50);
    function frame() {
        if (width >= 100){
            width = 0;
        }
        else{
            width++;
            progres.style.width = width + '%';
        }
    }
}
window.onload = progression;

Feel free to say if have to change some of my ways in the code to make it cleaner. And thanks for taking to help me :)

A: The main thing is that you have to call setInterval again after you click, in order to restart the interval. Calling clearInterval stops the interval from running.
Here's a way to clean this up a bit:
let counter = 0;
let intervalId;

const getNextImage = (direction) => {
    const increment = direction === 'forward' ? 1 : -1;
    counter += increment;
    if (counter > images.length - 1) {
      counter = 0;
    }
    if (counter < 0) {
      counter = images.length - 1;
    }
    document.getElementById("currentImage").src = images[counter];
};

const startImageLoop = (direction) => {
  width = 0;
  clearInterval(intervalId);
  getNextImage(direction);
  intervalId = setInterval(() => {
    getNextImage(direction);
  }, 5000);
}

precedent.onClick = () => startImageLoop('backward');
suivant.onClick = () => startImageLoop('forward');

redpajama_set_name: RedPajamaStackExchange


Boost beginning number skills by asking the child to count the segments with you. Repeat the activity until the child is familiar with the numbers one through ten.
Help the child identify the numerals and place the segments in order.
Ask the child to name all the colors of the segments.
Once the child is more familiar with numbers, present a simple math problem, like 1+2=3, counting out the dots on top of the segments together.
Lay the segments flat and encourage the child to stack them as high as he or she can.
Place the caterpillar on its side and try to make segments "walk" across it like a balance beam.


HomeNewsTales of Hearts Update: Shing's and Kohak's Full Hi-Ougis.
Tales of Hearts Update: Shing's and Kohak's Full Hi-Ougis.
The Tales of Hearts official site has been updated with two battle videos of Shing and Kohak, both showing their full hi-ougis/mystic artes.
I can't determine Shing's, but if I'm right, Kohak's is Final Fury. Click the thumbnails above to view them in our YouTube channel.


text:
Taylor Swift to bring The Red Tour to UK, February 2014
Seven-time Grammy winner Taylor Swift, who has spent her summer headlining sold-out stadiums and arenas in North America on The Red Tour, announcestoday that she will be painting London RED in February 2014 with 3 dates at London's The O2.
London's The O2 will host Taylor Swift's blockbuster The Red Tour on 1st, 2nd and 4thFebruary 2014. Tickets will be priced from £37.50. Tickets will be available to purchase from 9am on Friday 4th October via AXS.com,ticketmaster.co.uk, gigsandtours.com and by phone on 08448 24 48 24. Exclusive VIP Experiences and packages are also available.
Taylor, dubbed 'a true arena-rock goddess' by Rolling Stone, recently wrapped up the US leg of The Red Tour with three consecutive sold-out nights at the Bridgestone Arena in her hometown of Nashville.
The North American portion of The Red Tour saw Taylor play to more than 1.36 million fans over 66 shows (including 13 stadium stops) in 47 cities in 29 states and 3 provinces. On a number of these nights Taylor was notably joined on stage by some very special guests for an array of show-stopping collaborations – from Jennifer Lopez and Ellie Goulding at Staples Center in LA through to Carly Simon at Gillette Stadium in Foxborough, Mass.
Taylor's The Red Tour performance features two multi-level stages, elaborate costumes, dancers, aerialists and numerous changing sets, giving everyone in the venue an excellent seat. Taylor plays electric guitar, banjo, piano and acoustic guitar during the show, and her set features numerous songs from her record breaking album, RED, in addition to new takes on several fan favourites.
This November will see Taylor take The Red Tour to Australia and New Zealand, where she will become the first solo female artist in 20 years to undertake a national stadium tour of Australia, with the last being Madonna's Girlie Show tour in 1993.
Taylor's fourth studio album, RED, reached no 1 at iTunes in over 50 countries around the globe. RED has sold over half a million copies and spawned 4 top 10 singles in the UK alone. In the US, RED is certified 4 x Platinum, while worldwide it has sold in excess of 5.8 million. With RED Taylor becomes the first artist since the Beatles (and the only female artist in history) to log six or more weeks at no. 1 in the US charts with three consecutive studio albums.
Described by The Times as a 'rare talent', Taylor Swift is one of the top 5 selling digital music artists worldwide, and is the top-selling digital artist in country music history. At only 23 years old, she has already had career record sales in excess of 26 million albums and 75 million song downloads worldwide.
Each of Swift's four studio albums has sold in excess of 5 million copies worldwide while her two most recent albums are two of only 18 albums in the entire history of music to sell more than 1 million copies in a single week.
Taylor recently received two new entries into the 2014 book of Guinness World Records, earning the title of fastest selling digital single for 'We Are Never Ever Getting Back Together' and for being the first solo female with two one-million selling weeks on the US album chart.
Previous articleINTERVIEW: Ebony Day
Next articleWATCH: Kamaliya reveals video for new single 'Love Me Like'


Vladimir Vasilenko was born in the far north of Siberia, where he still lives. An economist by trade, he has never pursued that occupation. Instead of financial reports and spreadsheets, his main calling became the written word, and on top of being a writer, he also worked several years as a journalist.
He began writing stories at the tender age of 7. At 13, Vladimir started working on two novels, and a few years later, they were picked up by the largest publishing house in Russia. Those novels that came about in the early stages of his writer's career weren't particularly remarkable: your usual sci-fi space action, but in the writer's own words, "they did give me the opportunity to hold my own books in my hands and believe that one day, I could become a successful writer".
However, after an almost ten-year gap since his first childish attempts at writing, Vladimir learned about the LitRPG genre that was gaining momentum in Russia and gave it another go in 2016. An avid gamer himself, he started contemplating the possible significance of video games in a futuristic setting with the invention of technologies allowing total immersion. What was at first just a passing fancy quickly turned into a genuine fascination and he returned to writing.
Ironically, as a reader, Vladimir isn't a big fan the LitRPG genre. His biggest pet peeves are Mary Sue protagonists, the excessive use of game slang, and the abundant overviews of character statistics. "I just cannot stand it when fights are described as someone using a fireball and dealing 180 damage to the enemy," says Vladimir.
What distinguishes his works from other LitRPGs is their closeness to classic fantasy fiction. Instead of being generic accounts of someone's power-leveling, Vladimir's stories describe how the game becomes a second life to some people, opening up new opportunities, allowing them to discover new qualities, and sometimes putting them before difficult moral dilemmas. None of this, of course, takes away from battles, exciting quests, rare artifacts and other attractive things, irresistible to many gamers.


The Chandler Center for the Arts is the Valley's best kept secret for outstanding entertainment in a welcoming environment where customer service is our priority. The Center is located in the heart of Downtown Chandler and is the gem of the city's entertainment district with performances every week, and rotating art exhibitions on display in its gallery. A wide variety of restaurants and retailers to fit any taste are within walking distance, making the Center the perfect part of an afternoon or evening in Downtown. To find restaurants and bars in Downtown Chandler and the surrounding Chandler area, go to visitchandler.com.
Parking is free at our venue, with additional free public parking located in the nearby Downtown area. The Center is located on the northwest corner of Chandler Blvd and Arizona Ave. Accessible seating, hearing assistance devices and other accommodations are made available for our patrons.
Chandler Center for the Arts has some of the most unique theatre and visual arts spaces in the Valley. With three theatres and two galleries, we have spaces created for performers and artists to provide patrons with memorable experiences.
Find all of the information you need to make your journey here easier.
The Chandler Center for the Arts strives to make its performances and facilities accessible to all our patrons by having accommodations available.
Interested in renting the CCA or want a unique school group trip? We give tours.
Our Restaurant Partners show their dedication to the arts in the community through their donations to the Chandler Center for the Arts.
Enjoy some of our offerings at our concessions counter to complete your arts experience.
Here are some Frequently Asked Questions that may provide you with additional helpful information.


UNIVERSITY OF DELAWARE, et al. Defendants.
Plaintiff filed this action against Defendants claiming violations of the Due Process Clause of the Fourteenth Amendment (Counts I and II), the Age Discrimination in Employment Act ("ADEA") pursuant to 29 U.S.C. § 623 (Count III), the Delaware Discrimination in Employment Act pursuant to 19 Del. C. § 711(a) (Counts III, IV, and V), and the Equal Protection Clause of the Fourteenth Amendment (Count VI). (D.I. 1 at ¶ 1). Presently before the Court is Defendant's Motion to Dismiss all counts of Plaintiff s Complaint pursuant to Fed.R.Civ.P. 12(b)(6). (D.I. 6). The issues have been fully briefed. (D.I. 7, 9, 11). For the reasons set forth below, Defendants' Motion is GRANTED as to Counts I and II and DENIED as to Counts III, IV, V, and VI.
On October 3, 2016, UD and Kenny received a complaint from a parent alleging Kenny mistreated a player. (Id. at ¶ 18). Two days later, Kenny met with Defendants Chrissi Rawak, UD Athletic Director, and Thomas LaPenta, UD Human Resources Director, to discuss the complaint. (Id. at ¶¶ 20-21). At that meeting, Rawak and LaPenta stated they must investigate the complaint while placing Kenny on paid suspension, but also offered Kenny the option of resigning with one year's salary and benefits. (Id. at ¶¶ 21-22). On October 11, 2016, Kenny informed UD that she wished to go forward with the investigation. (Id. at ¶ 27). A UD athletics spokesperson confirmed to the News Journal that Kenny was placed on leave and refused any other comment. (Id. at ¶ 28).
UD terminated Kenny without cause, as permitted by her employment contract, on October 13, 2016. (Id. at ¶ 33). UD did not conduct an investigation into the complaint filed ten days earlier, despite UD Policy 4-29 setting forth the procedure UD would adhere to when investigating a complaint. (Id. at ¶¶ 26, 33). UD claimed its decision to terminate Kenny was due to three previous losing seasons, although UD did not make public statements regarding Kenny's termination. (Id. at ¶¶ 32, 34). Approximately seven months later, an article appeared in the UD student newspaper with opinions from former UD women's volleyball players. (Id. at ¶ 35). These opinions provided a mostly negative picture of Kenny and her coaching. (Id. at ¶ 36).
Kenny's termination occurred in the middle of the regular volleyball season which, she asserts, damaged her reputation. (Id. at ¶¶ 33-34). Kenny also alleges that UD's silence to the press and the student newspaper article damaged her reputation further. (Id. at ¶ 39). Thus, Kenny asserts she was entitled to the investigation described in UD Policy 4-29, as well as a name-clearing hearing to protect her damaged reputation. (Id. at ¶¶ 33, 62).
Kenny alleges that her termination was motivated by discrimination based on her age, sexual orientation, and marital status. (Id. at ¶¶ 73, 79, 85). After UD terminated Kenny's employment, it replaced her with a 38 year old head coach. (Id. at ¶ 51). Over Kenny's tenure, UD terminated five other female coaches over the age of 40. (Id. at ¶ 49). Additionally, four of those five coaches were homosexual. (Id. at ¶ 50). Since Defendant Rawak's appointment as UD Athletic Director in May 2016, she has only hired coaches who are heterosexual, are married or divorced, and have children. (Id. at ¶ 52). Additionally, after Kenny's termination, her employment agreement required UD to pay her base salary for three years. (Id. at ¶ 43). Her spouse, Ms. Gregory, also received UD health coverage. In January 2017, however, UD terminated Ms. Gregory's health coverage. (Id. at ¶ 47). UD continued to provide health coverage to the spouses of two heterosexual male coaches during the time they received their regular base pay after their terminations. (Id. at ¶ 48).
In March 2017, Kenny filed charges of discrimination with the Delaware Department of Labor (the "DDOL") and the Equal Employment Opportunity Commission (the "EEOC"). (Id. at ¶ 30). In June 2017, the DDOL and EEOC issued Kenny Right to Sue Notices. (D.I. 1-1 at 2, 5).
Rule 8 requires a complainant to provide "a short and plain statement of the claim showing that the pleader is entitled to relief" Fed.R.Civ.P. 8(a)(2). Rule 12(b)(6) allows the accused party to bring a motion to dismiss the claim for failing to meet this standard. A Rule 12(b)(6) motion may be granted only if, accepting the well-pleaded allegations in the complaint as true and viewing them in the light most favorable to the complainant, a court concludes that those allegations "could not raise a claim of entitlement to relief." Bell Atl. Corp. v. Twombly, 550 U.S. 544, 558 (2007).
A complainant must plead facts sufficient to show that a claim has "substantive plausibility." Id. at 347. That plausibility must be found on the face of the complaint. Ashcroft v. Iqbal, 556 U.S. 662, 678 (2009). "A claim has facial plausibility when the [complainant] pleads factual content that allows the court to draw the reasonable inference that the [accused] is liable for the misconduct alleged." Id. Deciding whether a claim is plausible will be a "context-specific task that requires the reviewing court to draw on its judicial experience and common sense." Id. at 679.


So anyone who knows me, knows that I am a recent convert to the "Divergent" fandom. I saw the film for the first time in early October, and began reading the books Halloween weekend. I spent all of November and most of December watching the movie at least twice a week. I started following Sheo and FourTris-related social media pages (that stands for Shailene/Theo and/or Four/Tris – for those of you uninitiated who are clueless about what a ship is), reading old interviews of Shai and Theo (yes, my burgeoning fandom love has progressed to first-name basis), and trying to figure out exactly what took so long for me to get into the series in the first place (oh, yeah – the fact that I could never find the books in the library and was trying to stick to my "don't buy another book until you read the almost 400 you've already got" rule). Whatever.
My obsession quickly died down as the holidays ended and I had to get back to real life – work, school, parenting, and all of that other stuff that interferes with me being able to fangirl full time 😦 However, today as I was sitting at my desk at work trying to take a five minute break from the mountain of paperwork I was drowning in, Insurgent began trending on my social media timelines and I figured something got released and it was my duty to check it out.


text:
Fra A til nÅ – 40 beste är ett samlingsalbum med Finn Kalvik. Albumet utgavs 1993 som en dubbel-CD av skivbolaget Polydor Records.

Låtlista
CD 1
"Finne meg sjæl" – 2:29
"Lilla vackra Anna" (Bengt Henrik Alstermark/Alf Prøysen) – 3:46
"Fyll mine seil" – 3:58
"Aldri i livet" – 4:08
"Mang en søvnløs natt" – 2:11
"Lillesøster" (Finn Kalvik/Haakon Graf) – 3:52
"En tur rundt i byen" (Ralph McTell/Finn Kalvik) – 4:28
"Rotløs" – 4:17
"Liten oppsummering" – 3:37
"To tunger" (Inger Hagerup/Finn Kalvik) – 1:54
"Kom ut kom fram" – 3:59
"Livets lyse side" – 3:37
"Elva" (John Martyn/Finn Kalvik) – 2:36
"I sommernattens stillhet" – 4:25
"Lille persille" (Inger Hagerup/Finn Kalvik) – 1:15
"Kom og ligg inntil meg" – 5:06
"Du er som vinden" (Donovan/Finn Kalvik) – 2:42
"Natt og dag" – 4:01
"Ved peisen" (J.R.R. Tolkien/Finn Kalvik) – 3:48
"Tenn dine vakre øyne" – 3:54

CD 2
"På flukt" – 3:55
"Fred og frihet" – 4:38
"Måken" (André Bjerke/Finn Kalvik) – 2:15
"Den svalande vind" (Trad. arr.: Finn Kalvik) – 2:43
"Bokseren" (Paul Simon/Finn Kalvik) – 4:39
"Livet og leken" – 3:30
"Mot ukjente farvann" – 4:46
"Vredens dag" – 3:28
"Samfunnshus blues" – 3:00
"Alle som blir igjen" – (Elton John/Finn Kalvik) – 5:02
"Berceuse" (André Bjerke/Finn Kalvik) – 2:55
"Sommerøya" (Inger Hagerup/Finn Kalvik) – 2:57
"Velkommen – farvel" (Finn Kalvik/Ted Gärdestad) – 4:00
"Normann Andersen" – 4:14
"Malene" – 3:25
"Feriebrev" – 2:46
"Fredløs" (Finn Kalvik/Jimmy Webb) – 4:24
"Ride ride ranke" (Harry Chapin/Finn Kalvik) – 3:40
"Barndommens jul" – 3:16
"Jeg har finni meg sjæl" (Øystein Sunde) – 2:46

Samtlia låtar skrivna av Finn Kalvik där inget annat anges.

Externa länkar
Fra A til nÅ – 40 beste på Discogs
Samlingsalbum 1990

redpajama_set_name: RedPajamaWikipedia


text:
#33 Vikram Lahkbir Singh
Aizawl FC
I-League League level: Second Tier Joined: Aug 30, 2022 Contract expires: May 31, 2023
Date of birth/Age: Jul 6, 1993 (29)
Place of birth: ---, Maharashtra
Citizenship: India
₹60 L
Stats of Vikram Lahkbir Singh
This page contains information about a player's detailed stats. In the info box, you can filter by period, club, type of league and competition. The "Detailed stats" tab shows a player's total appearances, goals, cards and cumulative minutes of play for each competition, and indicates the season in which it occurred.
Overall balance 22/23 21/22 19/20 18/19
Stats 22/23
Total 22/23: 7 - 2 - 1 9 1 630'
I-League 7 - 2 - 1 9 1 630'
1 11/15/22 H (6.) TRAU FC (7.) 1:1 GK 90'
2 11/18/22 H (6.) Gokulam FC (4.) 0:1 GK 90'
3 11/21/22 H (9.) Sudeva Delhi (11.) 2:1 GK 90'
4 11/28/22 A (6.) RoundGlass (2.) 2:1 GK 60' 90'
5 12/2/22 A (9.) Ch. Brothers (11.) 1:1 GK 90'
6 12/8/22 H (8.) NEROCA FC (7.) 1:0 GK 81' 90'
7 12/11/22 A (6.) Sreenidi (2.) 3:3 GK 90 + 4' 90'
8 12/17/22 H (7.) Kenkre FC (10.) 4:0 Not in squad
9 12/20/22 A (6.) Rajasthan Utd. (7.) 1:0 Not in squad
10 1/9/23 H (8.) Real Kashmir (3.) 0:0 Not in squad
11 1/13/23 A (7.) Mohammedan (9.) 2:2 Not in squad
12 1/16/23 A (7.) Sudeva Delhi (12.) 1:2 Not in squad
13 1/21/23 A (7.) NEROCA FC (10.) 2:1 Not in squad
14 1/26/23 H (8.) Mohammedan (9.) 1:0 on the bench
Squad: 8, Starting eleven: 7, Substituted in: 0, On the bench: 1, Suspended: 0, Injured: 0
Positions played
Played as...
Goalkeeper 7
Aizawl FC 7 9 1
Mumbai City FC II 4 7 1
Fateh Hyderabad AFC 3 1 2
Mumbai City FC - - -
Performance by competition
I-League 7 9 1
I-League 2nd Division 7 8 3
Indian Super League - - -


Questions for Nursing Interviews « Maximus Capital S.A.
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Biographies, old texts and studies are often offered by public libraries.
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In summary, some matches may again be affected by some rainfall at some point in the game (including a chance of a storm on Saturday's matches, and this afternoon and evening in Canberra), however this is expected to have less of an impact than last week.
Friday Showers and thunderstorms are expected around the Canberra area this afternoon, including some possible rain areas. Heading into this evening, there is expected to be a general easing trend, however the chance of rain happening at any time during early evening game remains more likely than not.
Humidity levels are expected to be low, and should there be no rain during the game, a moderate to high dew rating is expected in the second half.
Friday The isolated heavy showers currently around Brisbane are expected to move inland and clear this afternoon, with fine weather this evening for the Fri night match. Game temperatures will be warm, along with moderate humidity levels, but these are unlikely to make an impact upon player comfort given these are two QLD teams. A moderate dew rating is also expected.
Saturday There may be one or two showers or storms moving across Sydney during Saturday afternoon, however the chance of one of these moving across Cronulla during the mid afternoon match is relatively low. Game temperatures will be fairly warm, in the mid to high 20's, along with low to moderate humidity levels, however the skies are expected to be fairly cloudy which will aid in the player comfort not being hugely impacted.
Saturday Similar to the earlier match at Cronulla, shower and storm activity developing to the west of Sydney will also move into the Newcastle region during the early evening match. Moderate humidity levels will cancel out the small decrease in temperature compared with the earlier match, with a small impact on player comfort levels expected during the first two Saturday matches.
Saturday Any isolated shower or storm activity is most likely to have moved north through the Central Coast/Newcastle area by the start of the match at Brookvale, however there is still a small chance of any rainfall during the match.
Moderate humidity levels are expected during the match, which will make a small impact on player comfort levels. A moderate northeasterly breeze will limit the amount of dew able to form on the playing surface, with a low to moderate rating.
Sunday Fine but fairly hot weather is expected at Homebush for the Sunday afternoon match. Fortunately this will be combined with low humidity levels, so player comfort levels are not hugely impacted.
Sunday There is a slight chance of a shower or storm during the evening, otherwise it will remain fine at Campbelltown for the Sunday evening match. A moderate dew rating is expected in the second half.
Just keep the Sydney radar open on the radar app on your phone just to make sure there's no storms inbound!


You'd think that already being on a beautiful island will suffice, but no. We are in the Cook Islands after all, and therefore we had to do a bit of inter island hopping whilst here.
On a perfect Monday morning it was a party of eight on a long narrow boat heading out of the lagoon into the deep blue ocean.
Our first mission was to spot and swim with turtles.
When we spotted one we'd ease out of the boat and slowly snorkel up to it. Do you see me out there looking for a turtle?
We actually spotted three in total, and Jef chased one with a camera. I know it is hazy, but you can just see it through the turquoise.
We then went to a lovely spot just out from the reef. There we had some serious snorkeling action; moving in and out with the waves while watching the fish do the same.
It is so satisfying to have such a wonderful underwater experience, mask lines and all. Now I remember why I loved scuba diving.
Mathias then anchored at a remote island called Maina.
What can I say - this place is ridiculously perfect.
Rediculous is also when you discover Red Tail (?) chicks nesting at your picnic area.
Kien found himself a play mate - Roko, son of Mathias' assistant, Nga.
Mathias prepared an amazing lunch for us; BBQ Tuna steaks, taro leaves in coconut cream, boiled taro, potato salad, papaya salad, and a tossed green salad. We washed our food down with fresh coconut juice.
After our feast Mathias and Nga cleared up while we went for a walk around the island.
We cooled off in the bath when it was too hot.
We found Kien and Roko playing in the rock pool on our return.
Our next stop was Honeymoon Island. They dropped us off on one side of the island and took the boat around to the other side to pick us up.
Who couldn't fall in love here? With perfect white sand and crystal waters stretching out into the turquoise sea it is like a place straight out of the movies.
We did our last bit of snorkeling at an amazing spot where the sand bank drops away sharply into the deep.
I took Kien for a swim here, first where he could touch the bottom, then we made our way out to the blue part.
Then I went and did some skin diving. It was so beautiful and clear, like we were diving in an aquarium. It was completely surreal.
We came back exhausted and happy. The entire day was like a dream.
Absolutely beautiful and I am very envious!!!


text:
Pygarctia grossbecki är en fjärilsart som beskrevs av Davis 1913. Pygarctia grossbecki ingår i släktet Pygarctia och familjen björnspinnare. Inga underarter finns listade.

Källor

Björnspinnare
grossbecki


text:
Podcasts – digital audio files with information valuable to subscribers – can be a great source of inspiration for startups, providing real-life stories of other startups, of their successes, failures and strategies. They are also a great tool for investors, who can find insider tips and knowledge on finance and business. We've collected the top 3 channels for both parties to listen to and gain insight from others experience.
Startups, you could be learning from these podcasts
1. The Tim Ferris Show
It's no surprise the Tim Ferris Show is often ranked as the #1 business podcast on Apple. An accomplished entrepreneur himself, Ferris is a best-selling author, an investor, an entrepreneur and host of the podcast show. He has interviewed the likes of Tony Robbins, Malcolm Gladwell and Jon Favreau, but the particularity of his modus operandi: he prides himself on deconstructing the minds of geniuses in all industries – from art to business and sports – to expose their processes, routines and keys to success.
If you haven't been following him, time to catch up: The Tim Ferris Show
2. This Week in Startups
Jason Calacanis, an entrepreneur-turned-investor often gets visited by the heavyweight champions of the startup ecosystem. He provides inspiration for founders on how to not fail as a startup by picking the minds of influencers like Steve Huffman or Travis Kalanick. His self-proclaimed mission: to provide "outrageous and illuminating stories from the world of entrepreneurship" so founders can gather advice on how to grow their startup.
Catch the bi-weekly podcast here: This Week in Startups
3. How To Build a Rocketship
How to Build a Rocketship examines all topics from raising funding, product development, sales tactics and much more. With over 200 interviews with successful entrepreneurs, the podcasts offer plenty of growth hacking tips and real-life stories of failure and overcoming limitations.
Get onboard the rocketship: How to Build a Rocketship
Investors, tune in to these experts
1. The Full Ratchet
When Nick Moran started The Full Ratchet in 2014 he had already been angel investing since early 2013. His struggle to find solid investing information and knowledge inspired him to start his own channel. In his episodes he dissects themes such as 'Venture Capital', 'Startup Ecosystem', 'Angel Investing' and more. After months of chatting with investors over coffee, it was clear to Moran he needed to spread the knowledge, so he did.
The full list of podcasts: The Full Ratchet
2. Angel Investing with Tatyana Gray
As the angel investing circle grows, so does the need for thorough insight into the startup investing world. Gray is on a mission: She wants to assemble a group of elite angel investors to share their background stories and investing knowledge. This podcast offers a window into the minds of venture capitalists and breaks down what you need to know like how to handle equity and shares. The lawyer-turned-angel investor aims to "attract, educate, and empower young accredited investors to become active angel investors."
Don't miss an episode: Angel Investing with Tatyana Gray
3. Harvard Business Review IdeaCast
The Harvard Business Review IdeaCast podcasts offer a range of business and finance-related topics. From micro and macromanagement theories to negotiation practices to leadership tips, the series looks into what makes people and businesses successful. Guests include authors, astrophysicists, journalists, economists, politicians, and Harvard professors so there is something for everyone to listen to and learn from.
Cast a line and reel in some knowledge: HBR IdeaCast
Sign up to Leapfunder to join our network of startups and investors:


RedTail Golf Center has four (4) different short game practice areas available for public use. You can establish a comprehensive practice routine that includes putting, chipping, pitching, full-swing and bunker play all at one facility.
Adjacent to the Eighteenth Hole is our comprehensive short game practice area. Here you can practice everything from green side bunker shots to an 80-yard approach to the green. The RedTail short game practice area allows you to improve your game in that critical scoring zone from 100 yards and in.
This putting green is conveniently located between the Clubhouse and the first tee. It is the ideal place to get ready for your round of golf or demo the newest putter from the RedTail Pro Shop. The North Putting Green has the least amount of undulation of all our short game areas. So practice your lag putts, and see the strokes start to fall off your game.
The smallest of our four (4) short game areas, this green is located just outside of Bunker's Bar & Grill. Grab a quick bite and a drink and practice with your flat stick as this green is restricted to putting only.
This putting green is located between Bunkers, the 10th tee and the 18th green. It is the largest of all our putting greens. Practice your chipping and putting and watch your up and down percentage go up and your handicap come down.


For centuries, the hilltop on Montpelier Hill, south-southwest of Dublin and overlooking the city, was occupied by a Neolithic passage grave (burial chambers covered in earth or stone, usually with a single, narrow entrance) whose entrance was marked by a large cairn (a pile or stack of stones made by humans). But the vantage point and the view were too powerful for one 18th-century man to resist, so he built his home on the hill…using stones from the cairn he destroyed. Many of the locals were terrified that the man had offended the old pagan gods, especially after a powerful storm blew the roof off his new home shortly after construction.
And some say he offended the devil himself.
In the early 18th century, William Conolly, then the Speaker of the Irish House of Commons and one of the richest men in Ireland, was casting about for a suitably impressive location for an expansive hunting lodge he wanted to build. South of the city he found the northernmost mountain that forms the ridge of the Glensamole Valley; no one remembers the original Irish name for the mountain, though the names Suide Uí Ceallaig or Suidi Celi have been suggested. The site and the views of Dublin soon convinced Conolly that this was where he wanted to build his hunting lodge, which he named Mont Pelier (Cnoc Montpelier).
During construction, workers found a large cairn and stones aplenty (that later turned out to be part of a Neolithic passage grave) and quickly "recycled" the stones, using them to build the hunting lodge. Reportedly, the magnificent lintel over the fireplace was a large standing stone (of the type used to build henges, like Stonehenge) found on site. Both standing stones and cairns were known as ancient grave markers, but Conolly forged ahead anyway.
And shortly after construction was completed in 1725, a powerful storm blew the roof clean off his new hunting lodge.
The locals claimed it was retribution for desecrating the burial site; some said by the pagan gods of old, some said by Satan himself. Neither possibility seemed to disturb Conolly in the slightest. He promptly built an arched stone roof for his lodge, again using stones from the cairn and around the site. That stone roof still exists today.
Conolly died in 1729, and for several years, Montpelier was largely abandoned. Which only added to the appeal for the Hellfire Club (Club Thine Ifrinn), who began renting the hunting lodge from the Conolly family in 1735.
The Hellfire Club originated in London in 1719, though King George I quickly outlawed it. Royal edict, however, was no impediment to the idle, titled rich congregating and engaging in immoral acts. Dublin's Hellfire Club was a place for wealthy young gentlemen to drink, gamble, hire prostitutes, even, allegedly, engage in more degenerate activities like animal torture and Satan worship. Their motto was "Fais ce que tu voudras" ("Do what thou wilt"). One of the Dublin Club founders was Richard Parsons, 1st Earl of Rosse, and Grandmaster of the Freemasons of Dublin. Sheriff of Dublin Simon Luttrell was also a member.
The combination of reputedly cursed location and allegedly depraved doings quickly led to ever-wilder stories about the Club's activities. The remoteness of the location meant that few, if any, of the activities could be witnessed or verified, which only added fuel to the fire. And a great many stories about the Hellfire Club involved fire.
One story states that late one night a priest called on the house to see what was really going on. In one version of the story, when he entered the house, the center of attention was a huge black cat with ears so pointed they resembled horns…sitting in the chair reserved for the devil himself. The priest immediately sensed evil when the cat started snarling at him and threw holy water on the cat – which turned into a devil-like figure and ran outside, burning down the roof as it left. In another version of the story, the priest finds a black cat that had been sacrificed and exorcised the cat's soul. In a third version of the story, the cat was doused in whiskey and set on fire.
An oft-repeated story about the Hellfire Club is the story of "Burn-Chapel" Whaley. In 1740 one of the principal members of the Club was Richard Chappell Whaley. He was at Montpelier enjoying one of the Hellfire Club's drunken parties (some say a black Mass performed by a defrocked priest) when a servant accidently spilled a drink on him. In retaliation, Whaley doused the man in brandy and set him on fire. As the servant ran through the house he grabbed a tapestry to try and smother the flames; eventually the blaze burned the entire lodge.
There are tales of black masses and human sacrifices, one of which led to the lodge catching on fire and killing several members. There is even a story of the Club members kidnapping, killing and eating a farmer's daughter.
But the most famous story involves a mysterious stranger who wandered in one dark and stormy night. The stranger joined a card game already in progress. At one point a player dropped his card and bent down to look under table for it. As he looked, he glimpsed at the stranger's legs and realized that the man had a cloven foot. When the stranger was confronted, he burst into flames and vanished.
Eventually, the Hellfire Club managed to burn Montpelier so badly that it was unusable. They stayed in the area, though, just moving down the road to the Stewards House, also known as Killakee House, and also owned by the Conolly family. Their level of activity waxed and waned throughout the 18th century, and when Thomas "Buck" Whaley died in 1800, the Hellfire Club (then known as "The Holy Fathers") died with him.
Today, many visitors report strange paranormal activities and a feeling of pure evil at Montpelier, which is now popularly known as the Hellfire Club. Visitors walking through the ruins have reported an entity pulling on necklaces and bracelets, hard enough to snap them in some cases. People have reported seeing a ghostly black cat.
And those brave enough to visit the site at night have heard the screams of a woman who apparently died when she was set on fire and rolled down the hill in a burning barrel during a Satanic ritual.
Want to pay a visit to Ireland's notorious Hellfire Club? Authentic Ireland offers tailor-made travel packages where the itinerary revolves around any must-see destinations. Reach out to us and get started on customizing a spooktacular haunted tour of Ireland.


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When it comes to new products and services in the 21st century, Apple Inc. has a tendency to hit the target far more often than it misses. Run down the list of memorable products and you'll see what I mean.
Since the turn of the century Apple has introduced many new Mac models. The Mac Pro seems like the loser in the bunch. iPhones? iPhone 5c was the loser. iPad? Only the original iPad seems like a problem child. AirPods, Watch, Apple Pay, Apple TV, and even HomePod have done well. What's the key to Apple's new strategy of growing Services?
Every technology gadget company and every services company have some product marketing techniques in common. One of them is more important than others yet often overlooked among members of the technorati elite politburo and the nattering nabobs of negativism which criticize anything and everything Apple does.
Differentiation is a key component to successful product marketing. Apple has always had Services but with a one billion customer base to service, the company is wise to push more products that integrate with the company's hardware and software.
Hardware among premium brands of PCs, tablets, and smartphones remains much the same, so Apple uses software– iOS macOS, tvOS, watchOS et al– to differentiate the entire product line. So it is with Services. Apple has far more services with a revenue stream that filter into the customer base than Samsung or Google or Amazon, let alone the likes of HP, Dell, Motorola, Nokia, Huawei, and other knockoff makers.
Apple's new strategy is to grow the Services revenue but to ensure that services– individually and as a collection– remain sufficiently different than competing platforms. That means Apple does not have to out Netflix Netflix for the new Apple TV+ and Apple TV Channels to succeed. This level of differentiation means more stickiness for the entire Apple ecosystem.
Apple Card is a good example. You get more cash back when you buy Apple products. I'm not so sure about the potential success of Apple Arcade because the company, and just as importantly, game app developers, makes a boatload of cash from being the most profitable game platform. Apple News+ fits, too. I ditched a number of news applications, including Flipboard, because so much content is available in the Apple News app. News+ simply adds to the differentiation.
What about putting more Apple Services on Android and Windows? After all, iTunes runs on Windows. Apple Music runs on Android. Could Apple get more Android customers to ante up with monthly payments to the growing list of Services products?
Just remember that differentiation is key, and Apple is a hardware company, too. Those one billion customers use almost 1.5-billion Apple products and a growing cadre of Services is a strong incentive for those on the dark side to cross into the light.


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text:
Тодор Иванов Найденов е български политик от БКП, герой на социалистическия труд.

Биография
Роден е на 7 февруари 1900 г. във Враца. От 1922 г. е член на БКМС, а от 1923 г. на БКП. По време на Септемврийското въстание е куриер на Окръжния комитет на БКП във Враца. След неуспеха му е арестуван и уволнен като учител. От 1924 г. е секретар на Окръжния комитет на БКМС. През април 1925 г. е осъден на смърт, но впоследствие присъдата му е заменена на доживотен затвор. Освободен е през 1939 г. и става секретар на Окръжния комитет на БКП във Враца. През 1941 г. отново е арестуван и интерниран. Прекарва времето си в лагер до 1942 г., когато е осъден на 15 години затвор. След 9 септември 1944 г. е избран за председател на Областния комитет на ОФ във Враца. Известно време работи в ЦК на БКП като член на Контролната ревизионна комисия. От 7 юни 1950 до 4 март 1954 г. е кандидат-член на ЦК на БКП. През 70-те години е член на Централната контролно-ревизионна комисия при ЦК на БКП. Умира на 26 декември 1976 г. в Берлин.

Бележки

Герои на социалистическия труд на България
Участници в Септемврийското въстание
Кандидат-членове на ЦК на БКП
Членове на БКП до 1944 година
Членове на ЦКРК на БКП
Родени във Враца