An eigenvalue filtering based subspace approach for speech enhancement
In this paper, a subspace approach based on eigenvalue filtering is proposed for enhancement of corrupted speech. The new method firstly simultaneously diagonalizes the covariance matrix of clean speech and noise signal based on GEVD (generalized eigenvalues decomposition), and then
filters the smaller components whose eigenvalues are less than zero. Because the remainder eigenvector matrix after filtering is irreversible, we introduce the generalized inverse matrix transform to solve this problem for recovery of speech signal. Experimental results show the proposed method
performs better than many conventional methods under strong noise conditions, in terms of yielding less residual noise and lower speech distortion.
Document Type: Research Article
Affiliations: Nanchang Hang Kong University
Publication date: 01 January 2015
NCEJ is a peer reviewed Technical journal published every two months. The papers published in NCEJ cover general topics related to noise control engineering, ranging from fundamental research to applied case studies and histories.
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