Discontinuous transmission based on speech/pause detection represents a valid solution to improve the spectral efficiency of new-generation wireless communication systems. In this context robust voice activity detection (VAD) algorithms are required, as traditional solutions present a high misclassification rate in the presence of the background noise typical of mobile environments. The fuzzy voice activity detector (FVAD) proposed by Beritelli, Casale, and Cavallaro (see Contribution ITU-T, Study Group 16, question 19/16, Washington, September 1997), shows that a valid alternative to deal with the problem of activity decision is to use methodologies like fuzzy logic. In this paper we propose a multichannel approach to activity detection using both fuzzy logic and time delay estimation. Objective and subjective tests confirm a significant improvement over traditional methods, above all in terms of a reduction in activity increase for nonstationary noise.

In the field of mobile communications correct voice activity detection (VAD) is a crucial point for perceived speech quality, reduction of co-channel interference, power consumption in portable equipment. This paper shows that a valid alternative to deal with the problem of activity decision is to use methodologies like fuzzy logic, which are suitable for problems requiring approximate rather than exact solutions, and which can be presented through descriptive or qualitative expressions. The fuzzy voice activity detector (FVAD) proposed uses the same set of parameters adopted by the VAD in Annex B to ITU-T G.729 and a set of six fuzzy rules automatically extracted through supervised learning. Objective and listening tests confirm a significative improvement respect the traditional methods above all for low signal-to-noise ratios.

Discontinuous transmission based on speech/pause detection represents a valid solution to improve the spectral efficiency of new generation wireless communication systems. In this context, robust voice activity detection (VAD) algorithms are required, as traditional solutions present a high misclassification rate in the presence of the background noise typical of mobile environments. This paper presents a voice detection algorithm which is robust to noisy environments, thanks to a new methodology adopted for the matching process. More specifically, the VAD proposed is based on a pattern recognition approach in which the matching phase is performed by a set of six fuzzy rules, trained by means of a new hybrid learning tool. A series of objective tests performed on a large speech database, varying the signal-to-noise ratio (SNR), the types of background noise, and the input signal level, showed that, as compared with the VAD standardized by ITU-T in Recommendation G.729 annex B, the fuzzy VAD, on average, achieves an improvement in reduction both of the activity factor of about 25% and of the clipping introduced of about 43%. Informal listening tests also confirm an improvement in the perceived speech quality.