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A music cognition-guided framework for multi-pitch estimation.

Li, Xiaoquan; Yan, Yijun; Soraghan, John; Wang, Zheng; Ren, Jinchang

Authors

Xiaoquan Li

John Soraghan

Zheng Wang

Jinchang Ren



Abstract

As one of the most important subtasks of automatic music transcription (AMT), multi-pitch estimation (MPE) has been studied extensively for predicting the fundamental frequencies in the frames of audio recordings during the past decade. However, how to use music perception and cognition for MPE has not yet been thoroughly investigated. Motivated by this, this demonstrates how to effectively detect the fundamental frequency and the harmonic structure of polyphonic music using a cognitive framework. Inspired by cognitive neuroscience, an integration of the constant Q transform and a state-of-the-art matrix factorization method called shift-invariant probabilistic latent component analysis (SI-PLCA) are proposed to resolve the polyphonic short-time magnitude log-spectra for multiple pitch estimation and source-specific feature extraction. The cognitions of rhythm, harmonic periodicity and instrument timbre are used to guide the analysis of characterizing contiguous notes and the relationship between fundamental frequency and harmonic frequencies for detecting the pitches from the outcomes of SI-PLCA. In the experiment, we compare the performance of proposed MPE system to a number of existing state-of-the-art approaches (seven weak learning methods and four deep learning methods) on three widely used datasets (i.e. MAPS, BACH10 and TRIOS) in terms of F-measure (F1) values. The experimental results show that the proposed MPE method provides the best overall performance against other existing methods.

Citation

LI, X., YAN, Y., SORAGHAN, J., WANG, Z. and REN, J. [2022]. A music cognition-guided framework for multi-pitch estimation. Cognitive computation [online], Latest articles. Available from: https://doi.org/10.1007/s12559-022-10031-5

Journal Article Type Article
Acceptance Date May 26, 2022
Online Publication Date Jun 14, 2022
Deposit Date Jun 16, 2022
Publicly Available Date Jun 16, 2022
Journal Cognitive computation
Print ISSN 1866-9956
Electronic ISSN 1866-9964
Publisher Springer
Peer Reviewed Peer Reviewed
DOI https://doi.org/10.1007/s12559-022-10031-5
Keywords Pitch estimation; Cognition; Artificial intelligence; Machine learning; Music cognition; Automatic music transcription; Multi-pitch estimation; Harmonic structure detection (HSD); Polyphonic music detection
Public URL https://rgu-repository.worktribe.com/output/1687960

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