Blind Audio Source Separation With Minimum-Volume Beta-Divergence NMF

被引:38
|
作者
Leplat, Valentin [1 ]
Gillis, Nicolas [1 ]
Ang, Andersen M. S. [1 ]
机构
[1] Univ Mons, Dept Math & Operat Res, Fac Polytech, B-7000 Mons, Belgium
基金
欧洲研究理事会;
关键词
Nonnegative matrix factorization; beta-divergences; minimum-volume regularization; identifiability; blind audio source separation; model order selection; NONNEGATIVE MATRIX FACTORIZATION; IDENTIFIABILITY;
D O I
10.1109/TSP.2020.2991801
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Considering a mixed signal composed of various audio sources and recorded with a single microphone, we consider in this paper the blind audio source separation problem which consists in isolating and extracting each of the sources. To perform this task, nonnegative matrix factorization (NMF) based on the Kullback-Leibler and Itakura-Saito beta-divergences is a standard and state-of-the-art technique that uses the time-frequency representation of the signal. We present a new NMF model better suited for this task. It is based on the minimization of beta-divergences along with a penalty term that promotes the columns of the dictionary matrix to have a small volume. Under some mild assumptions and in noiseless conditions, we prove that this model is provably able to identify the sources. In order to solve this problem, we propose multiplicative updates whose derivations are based on the standard majorization-minimization framework. We show on several numerical experiments that our new model is able to obtain more interpretable results than standard NMF models. Moreover, we show that it is able to recover the sources even when the number of sources present into the mixed signal is overestimated. In fact, our model automatically sets sources to zero in this situation, hence performs model order selection automatically.
引用
收藏
页码:3400 / 3410
页数:11
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