Abstract
Subspace-based methods have been effectively used to estimate enhanced
speech from noisy speech samples. In the traditional subspace approaches,
a critical step is splitting of two invariant subspaces associated with
signal and noise via subspace decomposition, which is often performed by
singular-value decomposition or eigenvalue decomposition. However, these
decomposition algorithms are highly sensitive to the presence of large
corruptions, resulting in a large amount of residual noise within enhanced
speech in low signal-to-noise ratio (SNR) situations. In this paper, a
joint low-rank and sparse matrix decomposition (JLSMD) based subspace
method is proposed for speech enhancement. In the proposed method, we
firstly structure the corrupted data as a Toeplitz matrix and estimate its
effective rank value for the underlying clean speech matrix. Then the
subspace decomposition is performed by means of JLSMD, where the
decomposed low-rank part corresponds to enhanced speech and the sparse
part corresponds to noise signal, respectively. An extensive set of
experiments have been carried out for both of white Gaussian noise and
real-world noise. Experimental results show that the proposed method
performs better than conventional methods in many types of strong noise
conditions, in terms of yielding less residual noise and lower speech
distortion.
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