Abstract
Using sparse representation of power spectral density (PSD) approximated by magnitude-squared spectrum, a new speech enhancement method is presented. The approximation K-singular value decomposition (K-SVD) algorithm with nonnegative constraint is used to train an overcomplete dictionary of the clean speech PSD. The least angle regression algorithm (LARS) with a termination rule based on the ℓ2 norm of the sum of the noise PSD and cross term between the clean speech and noise spectra is applied to estimate the clean speech PSD. Combining the estimated PSD with the signal subspace approach based on the short-time spectral amplitude (SSB-STSA), the enhanced speech signal is obtained. The simulation results show that the new method can yield better performance in most of noise conditions.
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