Exploiting Nonnegative Matrix Factorization with Mixed Group Sparsity Constraint to Separate Speech Signal from Single-channel Mixture with Unknown Ambient Noise

Thanh Thi Hien Duong; Phuong Cong Nguyen; Cuong Quoc Nguyen

Research Article

Exploiting Nonnegative Matrix Factorization with Mixed Group Sparsity Constraint to Separate Speech Signal from Single-channel Mixture with Unknown Ambient Noise

Download977 downloads

Cite: BibTeX Plain Text

@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

Thanh Thi Hien Duong¹^,*, Phuong Cong Nguyen², Cuong Quoc Nguyen³

1: International Research Institute MICA, Hanoi University of Science and Technology, Vietnam, Dept. of Information Technology, Hanoi University of Mining and Geology, Vietnam
2: International Research Institute MICA, Hanoi University of Science and Technology, Vietnam, Dept. of Instrumentation and Industrial Informatic, Hanoi University of Science and Technology, Vietnam
3: Dept. of Instrumentation and Industrial Informatic, Hanoi University of Science and Technology, Vietnam

*Contact email: duongthihienthanh@humg.edu.vn

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.

Keywords: Speech enhancement, source separation, nonnegative matrix factorizarion (NMF), sparsity constraint, generic source spectral model

Received: 2017-12-30
Accepted: 2018-02-24
Published: 2018-03-14
Publisher: EAI

: http://dx.doi.org/10.4108/eai.14-3-2018.154342

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.