A Novel Approach to a Robust a Priori SNR Estimator in Speech Enhancement

This paper presents a novel approach to single channel speech enhancement in noisy environments. Widely adopted noise reduction techniques based on the spectral subtraction are generally expressed as a spectral gain depending on the signal-to-noise ratio (SNR) [1]–[4]. As the estimation method of the SNR, the well-known decision-directed (DD) estimator of Ephraim and Malah efficiently is known to reduces musical noise in noise frames, but the a priori SNR, which is a crucial parameter of the spectral gain, follows the a posteriori SNR with a delay of one frame in speech frames [5]. Therefore, the noise suppression gain using the delayed a priori SNR, which is estimated by the DD algorithm matches the previous frame rather than the current one, so after noise suppression, this degrades the performance of a noise reduction during abrupt transient parts. To overcome this artifact, we propose a computationally simple but effective speech enhancement technique based on the sigmoid type function to adaptively determine the weighting factor of the DD algorithm. Actually, the proposed approach avoids the delay problem of the a priori SNR while maintaining the advantage of the DD algorithm. The performance of the proposed enhancement algorithm is evaluated by the objective and subjective test under various environments and yields better results compared with the conventional DD scheme based approach. key words: a priori SNR, decision-directed, speech enhancement, sigmoid type