Open AccessMusic Signal Separation Using Supervised Robust Non-Negative Matrix Factorization with β-divergence
Author(s) -
Feng Li,
Hao Chang
Publication year - 2021
Publication title -
international journal of circuits, systems and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.156
H-Index - 13
ISSN - 1998-4464
DOI - 10.46300/9106.2021.15.16
Subject(s) - robustness (evolution) , source separation , matrix decomposition , computer science , non negative matrix factorization , speech recognition , divergence (linguistics) , artificial intelligence , pattern recognition (psychology) , separation (statistics) , matrix (chemical analysis) , instrumental music , machine learning , chromatography , art , musical , biochemistry , chemistry , eigenvalues and eigenvectors , physics , linguistics , philosophy , visual arts , quantum mechanics , gene
We propose a supervised method based on robust non-negative matrix factorization (RNMF) for music signal separation with β-divergence called supervised robust non-negative matrix factorization (SRNMF). Although RNMF method is an effective method for separating music signals, its separation performance degrades due to has no prior knowledge. To address this problem, in this paper, we develop SRNMF that unifying the robustness of RNMF and the prior knowledge to improve such separation performance on instrumental sound signals (e.g., piano, oboe and trombone). Application to the observed instrumental sound signals is an effective strategy by extracting the spectral bases of training sequences by using RNMF. In addition, β-divergence based on SRNMF be extended. The results obtained from our experiments on instrumental sound signals are promising for music signal separation. The proposed method achieves better separation performance than the conventional methods.