Open AccessA New Speech Enhancement Method Based on Nonnegative Low-rank and Sparse Decomposition
Author(s) -
Jiayi Sun,
Chengjie Sun,
Yi Hong
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1848/1/012090
Subject(s) - speech enhancement , noise (video) , rank (graph theory) , computer science , speech recognition , distortion (music) , sparse approximation , spectral density , sparse matrix , residual , pattern recognition (psychology) , mathematics , algorithm , noise reduction , artificial intelligence , image (mathematics) , amplifier , computer network , telecommunications , physics , bandwidth (computing) , combinatorics , quantum mechanics , gaussian
Enhancement of speech degraded by strong noises is a highly difficult task. In this paper, a nonnegative low-rank and sparse matrix decomposition (NLSMD) based speech enhancement method is given to address this problem. The proposed method is motivated with assumptions that in time-frequency (T-F) domain, since power spectrum of many types of noise with different frame are often correlative, noise can be assumed with a low-rank structure, while speeches are often sparse in T-F units. Based on these assumptions, we formulate the speech enhancement as a NLSMD problem, and design an objective function to recover speech component. Compared with traditional methods, the NLSMD-based method does not require a speech activity detector for noise density estimation. Experimental results show the proposed method can achieve better performance over many traditional methods in strong noise conditions, in terms of yielding less residual noise and lower speech distortion.