A Novel Voice Activity Detection for Multi-Channel Noise Reduction

Abstract

In this study, a voice activity detection technique is designed using features such as short-term energy, periodicity and spectral flatness. The desired results are obtained by using these three features, even at low signal to noise ratio values. In addition, performance of multi-channel noise reduction algorithms such as Wiener speech distortion weighted, spatial prediction, minimum variance distortion-less response are compared using the proposed voice activity detection. Two different audio signals and three different noise types are used in the experiment. Noisy speech and only detection of noisy areas have been performed by proposed voice activity detection algorithm. The filter coefficients have been calculated for each filter algorithm used after detection of noisy speech and only noisy areas. The calculated filter coefficients have been multiplied by the frequency components of the signal received from the reference microphone to obtain an enhanced signal. Segmental signal to noise ratio, an objective method, and mean opinion score as a subjective method have been used to evaluate the performance of the filters. Speech distortion weighted Wiener filter has been found to be the best filter for noise reduction performance.

Fault Detection of In‐Service Bridge Expansion Joint Based on Voiceprint Recognition

“…Tresholdbased methods, such as Short-Term Energy (STE) and Short-Term Cross-Zero Rate (STCZR), are popular approaches for audio event segmentation. However, in low signal-to-noise ratio (SNR) environments such as BEJs, these methods may not perform well for event segmentation [48,49].…”

A comparative study of multiple music signal noise reduction algorithms in music processing

“…Currently, the noise reduction algorithms frequently used for speech signals include filter technique, 5 spectral subtraction, 6 etc. Colak and Akdeniz 7 studied speech activity detection and compared the performance of multi-channel noise reduction algorithms using two different audio signals and three different noises. They found through experiments that the weighted wiener filtering had the best performance.…”