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Note onset detection based on sparse decomposition

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Abstract

Music onset detection is significant and essential for obtaining the high-level music features such as rhythm, beat, music paragraph and structure. The traditional methods for onset detection which employ Short Time Fourier Transform (STFT)-based or Wavelet Transform (WT)-based features to characterize music signal generally lack adaptiveness for representing the stationary and non-stationary part of the music signal. This will lead to the degraded performance for music note onset detection. To solve this problem, a new algorithm for note onset detection based on sparse decomposition is proposed. Firstly, the musical signals are sparsely decomposed with Matching Pursuit (MP), and then the hybrid detection algorithm which combines namely the Degree of Explanation (DE) and the Change of Partials (CP) is applied to the sparse representation of the music signal. Finally, a modified peak-picking algorithm is employed to generate onset vectors. The experiments on the dataset with 2050 onsets show that our results are superior to those of MIREX 2013. For the polyphonic music which is the most widely used form in our real life, the proposed algorithm has better performance than the other algorithms.

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Acknowledgments

This work is supported by the National Nature Science Foundation of China under Grant No. 60902065, No. 61401227, and by Beijing Natural Science Foundation(No.4152053).

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Authors and Affiliations

  1. College of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, No.172, 66 Xinmofan Road, Nanjing, Jiangsu Province, China, 210003

    Xi Shao

  2. Jinling Institute of Technology, Nanjing, China, 210003

    Wenming Gui

  3. National Laboratory of Pattern Recognition, The Chinese Academy of Sciences Institute of Automation, Beijing, China, 100190

    Changsheng Xu

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  1. Xi Shao
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  2. Wenming Gui
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  3. Changsheng Xu
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Correspondence to Xi Shao.

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Shao, X., Gui, W. & Xu, C. Note onset detection based on sparse decomposition. Multimed Tools Appl 75, 2613–2631 (2016). https://doi.org/10.1007/s11042-015-2656-8

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  • DOI: https://doi.org/10.1007/s11042-015-2656-8

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