The speech signal in general is corrupted by noise and the noise signal does not affect the speech signal uniformly over the entire spectrum. An improved Wiener filtering method is proposed in this paper for reducing background noise from speech signal in colored noise environments. In view of nonlinear variation of human ear sensibility in frequency spectrum, nonlinear multi-band Bark scale frequency spacing approach is used. The cross-correlation between the speech and noise signal is considered in the proposed method to reduce colored noise. To overcome harmonic distortion introduced in enhanced speech, in the proposed method regenerate the suppressed harmonics are regenerated. Objective and subjective tests were carried out to demonstrate improvement in the perceptual quality of speeches by the proposed technique
The noise signal does not affect uniformly the speech signal over the whole spectrum isn the case of colored noise. In order to deal with speech improvement in such situations a new spectral subtraction algorithm is proposed for reducing colored noise from noise corrupted speech. The spectrum is divided into frequency sub-bands based on a nonlinear multiband bark scale. For each sub-band, the noise corrupted speech power in past and present time frames is compared to statistics of the noise power to improve the determination of the presence or absence of speech. During the subtraction process, a larger proportion of noise is removed from sub-bands that do not contain speech. For sub-bands that contain speech, a function is developed which allows for the removal of less noise during relatively low amplitude speech and more noise during relatively high amplitude speech .Further the performance of the spectral subtraction is improved by formulating process without neglecting the cross correlation between the speech signal and background noise. Residual noise can be masked by exploiting the masking properties of the human auditory system. In the proposed method subtraction parameters are adaptively adjusted using noise masking threshold. A psychoacoustically motivated weighting filter was included to eliminate residual musical noise. Experimental results show that the algorithm removes more colored noise without removing the relatively low amplitude speech at the beginning and ending of words.
This paper proposes a two stage hybrid speech enhancement system with nonuniform subbands. Frequency bins after Fourier transform are nonuniformly grouped to reduce the computations in calculating the spectral gain. First stage includes a soft decision gain modification and applied to the Ephraim-Malah gain function based on Minimum Mean Square Error estimation (MMSE) and a psychoacoustic masking threshold is used in the second stage noise reduction. The performance of this algorithm is compared to modified spectral subtraction and spectral weighting algorithms in terms of listening tests and spectrograms. The proposed algorithm is suitable for noise reduction in non-stationary noise environments.
A two stage novel speech enhancement algorithm is presented in this paper using speech and noise spectral estimators with hybrid a priori SNR combined with perceptual weighting filter. The first stage is designed such that it improves the performance of Minimum Mean Square Error Short-Time Spectral Amplitude (MMSE-STSA) estimator by using hybrid a priori SNR and combining statistical estimators of the spectral magnitude of the speech and noise. A perceptual weighting filter is designed and used as a second stage to improve the intelligibility of the processed speech signal. The performance of proposed scheme is tested and results indicate the performance of the proposed algorithm is better than several other MMSE-STSA algorithms available in literature.
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