Research Article
Exploiting Nonnegative Matrix Factorization with Mixed Group Sparsity Constraint to Separate Speech Signal from Single-channel Mixture with Unknown Ambient Noise
@ARTICLE{10.4108/eai.14-3-2018.154342, author={Thanh Thi Hien Duong and Phuong Cong Nguyen and Cuong Quoc Nguyen}, title={Exploiting Nonnegative Matrix Factorization with Mixed Group Sparsity Constraint to Separate Speech Signal from Single-channel Mixture with Unknown Ambient Noise}, journal={EAI Endorsed Transactions on Context-aware Systems and Applications}, volume={4}, number={13}, publisher={EAI}, journal_a={CASA}, year={2018}, month={3}, keywords={Speech enhancement, source separation, nonnegative matrix factorizarion (NMF), sparsity constraint, generic source spectral model}, doi={10.4108/eai.14-3-2018.154342} }- Thanh Thi Hien Duong
Phuong Cong Nguyen
Cuong Quoc Nguyen
Year: 2018
Exploiting Nonnegative Matrix Factorization with Mixed Group Sparsity Constraint to Separate Speech Signal from Single-channel Mixture with Unknown Ambient Noise
CASA
EAI
DOI: 10.4108/eai.14-3-2018.154342
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.
Copyright © 2018 Thanh Thi Hien Duong et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
