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Deep learning-based automatic downbeat tracking: a brief review

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Abstract

As an important format of multimedia, music has filled almost everyone’s life. Automatic analyzing of music is a significant step to satisfy people’s need for music retrieval and music recommendation in an effortless way. Thereinto, downbeat tracking has been a fundamental and continuous problem in Music Information Retrieval (MIR) area. Despite significant research efforts, downbeat tracking still remains a challenge. Previous researches either focus on feature engineering (extracting certain features by signal processing, which are semi-automatic solutions); or have some limitations: they can only model music audio recordings within limited time signatures and tempo ranges. Recently, deep learning has surpassed traditional machine learning methods and has become the primary algorithm in feature learning; the combination of traditional and deep learning methods also has made better performance. In this paper, we begin with a background introduction of downbeat tracking problem. Then, we give detailed discussions of the following topics: system architecture, feature extraction, deep neural network algorithms, data sets, and evaluation strategy. In addition, we take a look at the results from the annual benchmark evaluation—Music Information Retrieval Evaluation eXchange—as well as the developments in software implementations. Although much has been achieved in the area of automatic downbeat tracking, some problems still remain. We point out these problems and conclude with possible directions and challenges for future research.

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Notes

  1. For a more comprehensive survey of MIR, containing background, history, fundamentals, tasks and applications, we refer readers to the overview by  [14, 18,19,20].

  2. A more complete list of data sets for MIR research is at: http://www.audiocontentanalysis.org/data-sets/.

  3. There are 13 duplicates which are pointed out by Bob Sturm: http://media.aau.dk/null_space_pursuits/2014/01/ballroom-dataset.html.

  4. http://www.sonicvisualiser.org/.

  5. http://www.music-ir.org/mirex/wiki/MIREX_HOME.

  6. Here we only talk about the six data sets used since 2014 because there are no comparisons for RWC classical and GTZAN.

  7. https://github.com/CPJKU/madmom.

  8. https://www.jyu.fi/hytk/fi/laitokset/mutku/en/research/materials/mirtoolbox.

  9. http://essentia.upf.edu/documentation/.

  10. https://github.com/librosa/librosa.

  11. Note that almost all researchers of automatic downbeat tracking have participated in MIREX Automatic Downbeat Estimation task and systems they proposed in their papers are similar to ones in MIREX. So results in Table 2 are quite representative and sufficient enough to analysis

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Acknowledgements

This work is supported by National Natural Science Fund for Distinguished Young Scholar (Grant No. 61625204) and partially supported by the State Key Program of National Science Foundation of China (Grant Nos. 61836006 and 61432014).

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  1. College of Computer Science, Sichuan University, Chengdu, People’s Republic of China

    Bijue Jia, Jiancheng Lv & Dayiheng Liu

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Jia, B., Lv, J. & Liu, D. Deep learning-based automatic downbeat tracking: a brief review. Multimedia Systems 25, 617–638 (2019). https://doi.org/10.1007/s00530-019-00607-x

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