Open AccessSingle‐channel speech enhancement based on multi‐band spectrogram‐rearranged RPCA
Author(s) -
Luo Yongjiang,
Mao Yu
Publication year - 2019
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2018.8131
Subject(s) - spectrogram , robust principal component analysis , computer science , speech enhancement , speech recognition , noise (video) , sparse matrix , matrix (chemical analysis) , pattern recognition (psychology) , white noise , algorithm , speech coding , principal component analysis , artificial intelligence , noise reduction , physics , telecommunications , materials science , composite material , quantum mechanics , image (mathematics) , gaussian
Robust principal component analysis (RPCA), a novel method for speech enhancement (SE), is expected to decompose the spectrogram of a noisy speech into a low‐rank matrix and a sparse matrix, which contain noise components and speech components, respectively. However, some speech components, which are not so variable in different time frames, are possible to be decomposed into a low‐rank matrix as noise mistakenly. To address this problem, a novel SE method based on spectrogram‐rearranged RPCA (SRPCA) is proposed for a sparse matrix with better decomposition for all speech components in white noise environments. For further improvement under coloured noises corruption, the multi‐band method is introduced for SRPCA to be applied in all bands individually. Accordingly, a time‐domain enhanced speech is reconstructed from the processed sparse matrix. Numerical experiments show the effectiveness of the proposed method.