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An efficient approach combined with harmonic and shift invariance for piano music multi-pitch detection
Author(s): Kai Deng; Gang Liu; Yuzhi Huang
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Paper Abstract

We propose an efficiently discriminative method that using AdaBoost as binary classifiers combined with musical signal properties for polyphonic piano music multi-pitch detection. As features, we use spectral components of multiples and divisions of notes’ fundamental frequency, which can reduce note’s feature redundancy compared with full spectrum. For the frame-level multi-pitch detection, the features of notes have adjacent pitches are similar (we called it shift invariance), which inspires us to use one binary classifier to detect those notes’ pitch. In a certain extent, those adjacent notes improves the classifier’s generalizability. In the post-processing stage, to combine with time property, we concatenate each notes’ several continuously frame-level predictions as their new features for final pitch detection. In conclusion, the proposed method with fewer classifiers achieves better performance compared with other methods.

Paper Details

Date Published: 31 July 2019
PDF: 6 pages
Proc. SPIE 11198, Fourth International Workshop on Pattern Recognition, 111980P (31 July 2019); doi: 10.1117/12.2540410
Show Author Affiliations
Kai Deng, Beijing Univ. of Posts and Telecommunications (China)
Gang Liu, Beijing Univ. of Posts and Telecommunications (China)
Yuzhi Huang, Beijing Univ. of Posts and Telecommunications (China)


Published in SPIE Proceedings Vol. 11198:
Fourth International Workshop on Pattern Recognition
Xudong Jiang; Zhenxiang Chen; Guojian Chen, Editor(s)

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