Abstract
This paper presents an algorithm for nonnegative matrix factorization 2D (NMF-2D) with the flexible β-Divergence. The β-Divergence is a group of cost functions parametrized by a single parameter β. The Least Squares divergence, Kullback-Leibler divergence and the Itakura-Saito divergence are special cases (β=2,1,0). This paper presents a more complete algorithm which uses a flexible range of β, instead of be limited to just special cases. We describe a maximization-minimization (MM) algorithm lead to multiplicative updates. The proposed factorization decomposes an information-bearing matrix into two-dimensional convolution of factor matrices that represent the spectral dictionary and temporal codes with enhanced performance. The method is demonstrated on the separation of audio mixtures recorded from a single channel. Experimental tests and comparisons with other factorization methods have been conducted to verify the efficacy of the proposed method.
Original language | English |
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Title of host publication | Electronic Proceedings of the 2015 IEEE International Workshop on Signal Processing Systems, SiPS 2015 |
Publisher | IEEE |
ISBN (Electronic) | 9781467396042 |
DOIs | |
Publication status | Published - 3 Dec 2015 |
Event | IEEE International Workshop on Signal Processing Systems, SiPS 2015 - Hangzhou, China Duration: 14 Oct 2015 → 16 Oct 2015 |
Conference
Conference | IEEE International Workshop on Signal Processing Systems, SiPS 2015 |
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Country/Territory | China |
City | Hangzhou |
Period | 14/10/15 → 16/10/15 |
Keywords
- audio processing
- maximization-minimization
- non-negative matrix factorization
- Single channel source separation
- β-Divergence