Exploiting Nonnegative Matrix Factorization with Mixed Group Sparsity Constraint to Separate Speech Signal from Single-channel Mixture with Unknown Ambient Noise
Journal Title: EAI Endorsed Transactions on Context-aware Systems and Applications - Year 2017, Vol 4, Issue 13
Abstract
This paper focuses on solving a challenging speech enhancement problem: improving the desired speech from a single-channel audio signal containing high-level unspecified noise (possibly environmental noise, music, other sounds, etc.). Using source separation technique, we investigate a solution combining nonnegative matrix factorization (NMF) with mixed group sparsity constraint that allows exploiting generic noise spectral model to guide the separation process. The experiment performed on a set of benchmarked audio signals with different types of real-world noise shows that the proposed algorithm yields better quantitative results in term of the signal-to-distortion ratio than the previously published algorithms.
Authors and Affiliations
Thanh Thi Hien Duong, Phuong Cong Nguyen, Cuong Quoc Nguye
Keywords
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- EP ID EP45802
- DOI http://dx.doi.org/10.4108/eai.14-3-2018.154342
- Views 288
- Downloads 0
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