Variance normalized perceptual subspace speech enhancement

Surendran, Sudeep; Kumar, T. K. Satish

doi:10.1016/j.aeue.2017.01.023

Cited by 5 publications

(1 citation statement)

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“…In [14], variance of the reconstruction error criterion was introduced to optimize the subspace selection for speech enhancement. In [15], to optimize the subspace decomposition model, human auditory psychoacoustic properties are incorporated into the subspace filter to reconstruct the enhanced signal. Although many efforts were conducts to improve the subspace methods, the existing subspace-based speech enhancement methods still suffer from the problem of low decomposition accuracy in the presence of large noise, resulting in a high remainder noise within enhanced speech in strong noise cases.…”

Section: Introductionmentioning

confidence: 99%

Speech Denoising in White Noise Based on Signal Subspace Low-rank Plus Sparse Decomposition

Yuan¹,

Sun

2017

MATEC Web Conf.

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Abstract-In this paper, a new subspace speech enhancement method using low-rank and sparse decomposition is presented. In the proposed method, we firstly structure the corrupted data as a Toeplitz matrix and estimate its effective rank for the underlying human speech signal. Then the low-rank and sparse decomposition is performed with the guidance of speech rank value to remove the noise. Extensive experiments have been carried out in white Gaussian noise condition, and experimental results show the proposed method performs better than conventional speech enhancement methods, in terms of yielding less residual noise and lower speech distortion.

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Section: Introductionmentioning

confidence: 99%