Speech Communication - Special issue on speech processing in adverse conditions
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02
A Bayesian estimation approach for speech enhancement using hiddenMarkov models
IEEE Transactions on Signal Processing
Voice activity detection based on multiple statistical models
IEEE Transactions on Signal Processing - Part I
HMM-Based Gain Modeling for Enhancement of Speech in Noise
IEEE Transactions on Audio, Speech, and Language Processing
Auditory-Based Spectral Amplitude Estimators for Speech Enhancement
IEEE Transactions on Audio, Speech, and Language Processing
Simultaneous Detection and Estimation Approach for Speech Enhancement
IEEE Transactions on Audio, Speech, and Language Processing
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Transform coding of audio signals using perceptual noise criteria
IEEE Journal on Selected Areas in Communications
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In this paper, a single-channel speech enhancement method based on generalized weighted @b-order spectral amplitude estimator is proposed. First, we derive a new kind of generalized weighted @b-order Bayesian spectral amplitude estimator, which takes full advantage of both the traditional perceptually weighted estimators and @b-order spectral amplitude estimators and can obtain flexible and effective gain function. Second, according to the masking properties of human auditory system, the adaptive estimation methods for the perceptually weighted order p is proposed, which is based on a criterion that inaudible noise may be masked rather than removed. Thereby, the distortion of enhanced speech is reduced. Third, based on the compressive nonlinearity of the cochlea, the spectral amplitude order @b can be interpreted as the compression rate of the spectral amplitude, and then the adaptive calculation method of parameter @b is proposed. In addition, due to one frame delay, the a priori SNR estimation of decision-directed method in speech activity periods is inaccurate. In order to overcome the drawback, we present a new a priori SNR estimation method by combining predicted estimation with decision-directed rule. The subjective and objective test results indicate that the proposed Bayesian spectral amplitude estimator combined with the proposed a priori SNR estimation method can achieve a more significant segmental SNR improvement, a lower log-spectral distortion and a better speech quality over the reference methods.