Adaptive voice activity detection for wireless communications based on hybrid fuzzy learning

Discontinuous transmission based on voice activity detection (VAD) represents a valid solution to improve the spectral efficiency of new-generation wireless communication systems. This paper initially emphasizes the need for more efficient methods to evaluate the performance of a VAD, proposing new performance evaluation criteria, and then presents a speech/pause detection algorithm which is robust to noisy environments. The VAD proposed is based on a pattern approach in which the matching phase is performed by a set of six fuzzy rules trained by means of a new hybrid learning tool. The decision module uses an adaptive threshold varying the estimated signal-to-noise ratio. A series of subjective tests showed that, as compared with the VAD G.729 Annex B, the new algorithm is statistically equivalent with the advantage of providing both a percentage of reduction in the activity increase of about 82% and a clipping reduction percentage of about 24%.