CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算与图形学
Note onset detection based on sparse decomposition
Shao, Xi1; Gui, Wenming2; Xu, Changsheng3
AbstractMusic 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.
KeywordNote Onset Detection Sparse Decomposition Matching Pursuit
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
Funding OrganizationNational Nature Science Foundation of China(60902065 ; Beijing Natural Science Foundation(4152053) ; 61401227)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000372027000013
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Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Affiliation1.Nanjing Univ Posts & Telecommun, Coll Commun & Informat Engn, 172,66 Xinmofan Rd, Nanjing 210003, Jiangsu, Peoples R China
2.Jinling Inst Technol, Nanjing 210003, Jiangsu, Peoples R China
3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Shao, Xi,Gui, Wenming,Xu, Changsheng. Note onset detection based on sparse decomposition[J]. MULTIMEDIA TOOLS AND APPLICATIONS,2016,75(5):2613-2631.
APA Shao, Xi,Gui, Wenming,&Xu, Changsheng.(2016).Note onset detection based on sparse decomposition.MULTIMEDIA TOOLS AND APPLICATIONS,75(5),2613-2631.
MLA Shao, Xi,et al."Note onset detection based on sparse decomposition".MULTIMEDIA TOOLS AND APPLICATIONS 75.5(2016):2613-2631.
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