Speech enhancement using an equivalent source inverse filtering-based microphone array.

This paper presents a microphone array technique aimed at enhancing speech quality in a reverberant environment. This technique is based on the central idea of single-input-multiple-output equivalent source inverse filtering (SIMO-ESIF). The inverse filters required by the time-domain processing in the technique serve two purposes: de-reverberation and noise reduction. The proposed approach could be useful in telecommunication applications such as automotive hands-free systems, where noise-corrupted speech signal generally needs to be enhanced. SIMO-ESIF can be further enhanced against uncertainties and perturbations by including an adaptive generalized side-lobe canceller. The system is implemented and validated experimentally in a car. As indicated by numerous performance measures, the proposed system proved effective in reducing noise in human speech without significantly compromising the speech quality. In addition, listening tests were conducted to assess the subjective performance of the proposed system, with results processed by using the analysis of variance and a post hoc Fisher's least significant difference (LSD) test to assess the pairwise difference between the noise reduction (NR) algorithms.

Optimal design of minimum mean-square error noise reduction algorithms using the simulated annealing technique.

The performance of the minimum mean-square error noise reduction (MMSE-NR) algorithm in conjunction with time-recursive averaging (TRA) for noise estimation is found to be very sensitive to the choice of two recursion parameters. To address this problem in a more systematic manner, this paper proposes an optimization method to efficiently search the optimal parameters of the MMSE-TRA-NR algorithms. The objective function is based on a regression model, whereas the optimization process is carried out with the simulated annealing algorithm that is well suited for problems with many local optima. Another NR algorithm proposed in the paper employs linear prediction coding as a preprocessor for extracting the correlated portion of human speech. Objective and subjective tests were undertaken to compare the optimized MMSE-TRA-NR algorithm with several conventional NR algorithms. The results of subjective tests were processed by using analysis of variance to justify the statistic significance. A post hoc test, Tukey's Honestly Significant Difference, was conducted to further assess the pairwise difference between the NR algorithms.