Speech enhancement using generalized weighted β-order spectral amplitude estimator

•We propose a new generalized weighted β-order spectral amplitude estimator.•We employ the masking properties and compressive nonlinearities of the cochlea.•GW-β-STSA takes full advantage of traditional Bayesian STSA estimators.•Combining p and β parameters can obtain more flexible and effective gain functions.•PEDD method overcomes the drawback of DD method and therefore outperforms it.

In this paper, a single-channel speech enhancement method based on generalized weighted β-order spectral amplitude estimator is proposed. First, we derive a new kind of generalized weighted β-order Bayesian spectral amplitude estimator, which takes full advantage of both the traditional perceptually weighted estimators and β-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 β can be interpreted as the compression rate of the spectral amplitude, and then the adaptive calculation method of parameter β 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.