DocumentCode :
38534
Title :
Musical Onset Detection Using Constrained Linear Reconstruction
Author :
Che-Yuan Liang ; Li Su ; Yi-Hsuan Yang
Author_Institution :
Res. Center for Inf. Technol. Innovation, Taipei, Taiwan
Volume :
22
Issue :
11
fYear :
2015
fDate :
Nov. 2015
Firstpage :
2142
Lastpage :
2146
Abstract :
This letter presents a multi-frame extension of the well-known spectral flux method for unsupervised musical onset detection. Instead of comparing only the spectral content of two frames, the proposed method takes into account a wider temporal context to evaluate the dissimilarity between a given frame and its previous frames. More specifically, the dissimilarity is measured by using the previous frames to obtain a linear reconstruction of the given frame, and then calculating the rectified, l2-norm reconstruction error. Evaluation on a dataset comprising 2,169 onset events of 12 instruments shows that this simple idea works fairly well. When a non-negativity constraint is imposed in the linear reconstruction, the proposed method can outperform the state-of-the-art unsupervised method SuperFlux by 2.9% in F-score. Moreover, the proposed method is particularly effective for instruments with soft onsets, such as violin, cello, and ney. The proposed method is efficient, easy to implement, and is applicable to scenarios of online onset detection.
Keywords :
music; signal reconstruction; unsupervised learning; SuperFlux; constrained linear reconstruction; l2-norm reconstruction error; linear reconstruction; unsupervised musical onset detection; Context; Indexes; Instruments; Measurement uncertainty; Multiple signal classification; Music information retrieval; Signal processing algorithms; Exemplar; linear reconstruction; musical onset;
fLanguage :
English
Journal_Title :
Signal Processing Letters, IEEE
Publisher :
ieee
ISSN :
1070-9908
Type :
jour
DOI :
10.1109/LSP.2015.2466447
Filename :
7185355
Link To Document :
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