You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
In this paper, we present a novel state of the art system for automatic downbeat tracking from music signals. The audio signal is first segmented in frames which are synchronized at the tatum level of the music. We then extract different kind of features based on harmony, melody, rhythm and bass content to feed convolutional neural networks that are adapted to take advantage of each feature characteristics. This ensemble of neural networks is combined to obtain one downbeat likelihood per tatum. The downbeat sequence is finally decoded with a flexible and efficient temporal model which takes advantage of the metrical continuity of a song. We then perform an evaluation of our system on a large base of 9 datasets, compare its performance to 4 other published algorithms and obtain a significant increase of 16.8 percent points compared to the second best system, for altogether a moderate cost in test and training. The influence of each step of the method is studied to show its strengths and shortcomings.

Cited Abstract(s):
[1]: Rhythmic patterns are an important structural element in music. This paper investigates the use of rhythmic pattern modeling to infer metrical structure in musical audio recordings. We present a Hidden Markov Model (HMM) based system that simultaneously extracts beats, downbeats, tempo, meter, and rhythmic patterns. Our model builds upon the basic structure proposed by Whiteley et. al [20], which we further modified by introducing a new observation model: rhythmic patterns are learned directly from data, which makes the model adaptable to the rhythmical structure of any kind of music. For learning rhythmic patterns and evaluating beat and downbeat tracking, 697 ballroom dance pieces were annotated with beat and measure information. The results showed that explicitly modeling rhythmic patterns of dance styles drastically reduces octave errors (detection of half or double tempo) and substantially improves downbeat tracking.

[2]: Hardcore, jungle, and drum and bass (HJDB) are fastpaced electronic dance music genres that often employ resequenced breakbeats or drum samples from jazz and funk percussionist solos. We present a style-specific method for downbeat detection specifically designed for HJDB. The presented method combines three forms of metrical information in the prediction of downbeats: lowlevel onset event information; periodicity information from beat tracking; and high-level information from a regression model trained with classic breakbeats. In an evaluation using 206 HJDB pieces, we demonstrate superior accuracy of our style specific method over four general downbeat detection algorithms. We present this result to motivate the need for style-specific knowledge and techniques for improved downbeat detection.

[3]: 

[4]: This dissertation covers machine listening techniques for the automated real-time analysis of live drum performances. Onset detection, drum detection, beat tracking, and drum pattern analysis are combined into a system that provides rhythmic information useful in performance analysis, synchronization, and retrieval. The techniques are designed with real-time use in mind but can easily be adapted for offline batch use for large scale rhythm analysis. At the front end of the system, onset and drum detection provide the locations, types, and amplitudes of percussive events. The onset detector uses an adaptive, causal threshold in order to remain robust to large dynamic swings. For drum detection, a gamma mixture model is used to compute multiple spectral templates per drum onto which onset events can be decomposed using a technique based on non-negative matrix factorization. Unlike classification-based approaches to drum detection, this approach provides amplitude information which is invaluable in the analysis of rhythm. In addition, the decay of drum events are modeled using "tail" templates , which when used with multiple spectral templates per drum, reduce detection errors by 42 . The beat tracking component uses multiple period hypotheses and an ambiguity measure in order to choose a reliable pulse estimate. Results show that using multiple hypotheses significantly improves tracking accuracy compared to a single period model. The drum pattern analysis component uses the amplitudes of the detected drum onsets and the metric grid defined by the beat tracker as inputs to a generatively pre-trained deep neural network in order to estimate high-level rhythmic information. The network is tested with beat alignment tasks, including downbeat detection, and reduces alignment errors compared to a simple template correlation approach by up to 59 .

[5]: In this paper, we approach the tasks of beat tracking, downbeat recognition and rhythmic style classification in nonWestern music. Our approach is based on a Bayesian model, which infers tempo, dow ...

[6]: The perception of downbeat is not yet very well understood, although it generally "feels" natural and intuitive to music listeners. We propose an unbiased and predictive modeling strategy of downbeat by combining psychoacoustic models of music listening with time-lag embedded segment learning. The model is causal, tempo independent, and could inform in a top-down manner a bottom-up beat tracker. Results with particularly complex music examples are presented
Machine learning systems can also be considered. Generic approaches such as Support Vector Machines (SVM) with an auditory spectrogram [4]1 [2] have been used at the risk of not being tailored for this particular problem. Other systems focus on recognizing rhythm patterns in the data to find the downbeats. It can be done with a Conditional Deep Belief Network [4] (CDBN) or a Gaussian Mixture Model (GMM) coupled to a k-means clustering [1] [5] . These rhythm related methods are more adapted to the problem but they often make strong style-specific assumptions and require music with a very distinctive rhythm style to work well [1] [5] [3] .