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Speech presence probability (SPP) and gain functions such as Wiener filter or MMSE estimators require an estimate of the a-priori signal-to-noise ratio (SNR). However, the estimation of the a-priori SNR is computationally involved and sensitive to noise variations. This paper proposes to approximate the SPP and the overall gain function of a speech enhancement system by using sigmoid functions to reduce the need of estimating the a-prior SNR. By applying an approximation via the sigmoid functions it is shown that only the a-posteriori estimate of SNR is needed, resulting in a low complexity system. The sigmoid function is designed with an optimization algorithm to optimize its parameters with respect to speech quality measures. The optimization algorithm is based on the idea that the solution obtained for a given problem should move towards the best solution and avoid the worst solution. The proposed algorithm requires minimal control parameters and does not require any algorithm specific parameters. Simulation results show that the proposed sigmoid functions achieve good results in terms of speech quality measures when compared with existing methods while providing significantly lower complexity for implementation. [ABSTRACT FROM AUTHOR]

This paper proposes a new method to design the fixed single-channel equalizer parameters based on perceptual evaluation of speech quality (PESQ) and the short-time objective intelligibility (STOI). The proposed equalizer does not require the need for noise estimation nor a voice activity detector. The fixed weightings are obtained by a training process containing a set of training data. A multi-objective optimization problem was formulated with the PESQ and STOI as the objective measures, which results in a set of candidate solutions via the Pareto optimality. Experimental results demonstrate improvements in PESQ and STOI, and the trade-off between these two measures is well defined in a set of Pareto optimal solutions to allow flexible adjustment. [ABSTRACT FROM AUTHOR]

Estimation of noise often has a major impact on the quality of enhanced signal, especially when it comes in speech enhancement applications. The non-stationary noise statistics vary with time, making decision of speech active/inactive frame is however difficult. Further, since there is no prior information of noise distribution, the estimators use the recursive averaging with a fixed smoothing coefficient ranging from 0.70 to 0.99. This fixed smoothing coefficient actually correlates the previous frames of noise statistics. Unfortunately, using fixed smoothing coefficient, the estimator treats both speech active/inactive frames equally which may cause the leakage of speech/noise power and results in loss of speech intelligibility. To address this problem and to increase the noise estimation accuracy, this paper proposes a posteriori SNR and frequency dependent adaptive smoothing coefficient. Further, this paper investigates the performance of proposed weighted sigmoid function (WSIG) noise estimator. From both objective and subjective quality assessments, it is clearly evident that the proposed noise estimator yields considerably better tracking of noise spectral variations compared to the existing state of the art methods. [ABSTRACT FROM AUTHOR]