Note onset detection based on sparse decomposition

doi:10.1007/s11042-015-2656-8

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 多媒体计算与图形学团队

	Note onset detection based on sparse decomposition
	Shao, Xi 1; Gui, Wenming 2; Xu, Changsheng3
刊名	MULTIMEDIA TOOLS AND APPLICATIONS
	2016-03-01
卷号	75 期号:5 页码:2613-2631
关键词	Note Onset Detection Sparse Decomposition Matching Pursuit
DOI	10.1007/s11042-015-2656-8
文献子类	Article
英文摘要	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.
WOS关键词	MATCHING PURSUIT ; SIGNALS ; MUSIC
WOS研究方向	Computer Science ; Engineering
语种	英语
WOS记录号	WOS:000372027000013
资助机构	National Nature Science Foundation of China(60902065 ; Beijing Natural Science Foundation(4152053) ; 61401227)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/11366]
专题	自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队
作者单位	1.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
推荐引用方式 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.