An efficient approach combined with harmonic and shift invariance for piano music multi-pitch detection

Kai Deng; Gang Liu; Yuzhi Huang

doi:10.1117/12.2540410

31 July 2019 An efficient approach combined with harmonic and shift invariance for piano music multi-pitch detection

Kai Deng, Gang Liu, Yuzhi Huang

Proceedings Volume 11198, Fourth International Workshop on Pattern Recognition; 111980P (2019) https://doi.org/10.1117/12.2540410
Event: Fourth International Workshop on Pattern Recognition, 2019, Nanjing, China

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.

Citation Download Citation

Kai Deng, Gang Liu, and Yuzhi Huang "An efficient approach combined with harmonic and shift invariance for piano music multi-pitch detection", Proc. SPIE 11198, Fourth International Workshop on Pattern Recognition, 111980P (31 July 2019); https://doi.org/10.1117/12.2540410

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