A Speech Enhancement Method Employing Sparse Representation of Power Spectral Density
Yanping Zhao, Xiaohui Zhao, Bo Wang
A Speech Enhancement Method Employing Sparse Representation of Power Spectral Density
Yanping Zhao, Xiaohui Zhao, Bo Wang
Abstract ：A speech enhancement method employing sparse reconstruction of the
power spectral density is proposed. The overcomplete dictionary of
the power spectral density is learned by approximation K-singular
value decomposition algorithm with non negative constraint. The
power spectral density of clean speech signal is reconstructed by
least angle regression method with a norm termination rule, and the
estimation of clean speech signal in the short-time Fourier
transform domain is obtained by using signal subspace approach on
the basis of short-time spectral amplitude. The experimental results
show that the proposed method can reconstruct structured speech
signal and suppress unstructured noise significantly even in low SNR
conditions.
Key words ：
Speech Enhancement
Sparse Representation
Dictionary Learning
Power Spectral Density
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2013, Vol. 10 
