scholarly journals Voice Activity Detection for Speech Enhancement Applications

10.14311/1251 ◽  
2010 ◽  
Vol 50 (4) ◽  
Author(s):  
E. Verteletskaya ◽  
K. Sakhnov
Keyword(s):  
Background Noise ◽  
Voice Activity Detection ◽  
Energy Ratio ◽  
Activity Detection ◽  
Noisy Environment ◽  
Signal Energy ◽  
Low Snr ◽  
Noise Robust ◽  
Voice Activity ◽  
Band Signal

This paper describes a study of noise-robust voice activity detection (VAD) utilizing the periodicity of the signal, full band signal energy and high band to low band signal energy ratio. Conventional VADs are sensitive to a variably noisy environment especially with low SNR, and also result in cutting off unvoiced regions of speech as well as random oscillating of output VAD decisions. To overcome these problems, the proposed algorithm first identifies voiced regions of speech and then differentiates unvoiced regions from silence or background noise using the energy ratio and total signal energy. The performance of the proposed VAD algorithm is tested on real speech signals. Comparisons confirm that the proposed VAD algorithm outperforms the conventional VAD algorithms, especially in the presence of background noise.

Noise robust model-based voice activity detection

2006 ◽  
Author(s):  
Ángel de la Torre ◽  
Javier Ramírez ◽  
Carmen Benítez ◽  
José C. Segura ◽  
L. García ◽  
...  
Keyword(s):  
Voice Activity Detection ◽  
Activity Detection ◽  
Model Based ◽  
Robust Model ◽  
Noise Robust ◽  
Voice Activity

scholarly journals Negentropy based voice-activity detection for noise estimation in very low SNR condition

2004 ◽  
Vol 1 (16) ◽  
pp. 495-500
Author(s):  
Rajkishore Prasad ◽  
Hiroshi Saruwatari ◽  
Kiyohiro Shikano
Keyword(s):  
Voice Activity Detection ◽  
Noise Estimation ◽  
Activity Detection ◽  
Low Snr ◽  
Voice Activity

scholarly journals Real Time Implementation of Voice Activity Detection based on False Acceptance Regulation

2020 ◽  
Vol 12 (3) ◽  
pp. 654-666
Author(s):  
Charaf Eddine Chelloug ◽  
◽  
Atef Farrouki ◽  
Keyword(s):  
Real Time ◽  
Voice Activity Detection ◽  
Activity Detection ◽  
Speech Compression ◽  
Noisy Environments ◽  
Signal Energy ◽  
Active Voice ◽  
Voice Activity ◽  
Time Acquisition ◽  
False Acceptance

In speech compression systems, Voice Activity Detection (VAD) is frequently used to distinguish active voice from other noisy sounds. In this paper, a robust approach of VAD is presented to deal with non-stationary noisy environments. The proposed algorithm exploits adaptive thresholding technique to keep a desired False Acceptance (FA) rate. Iterative hypothesis tests, using signal energy, are implemented to discard or to accept the successive audio frames as active voice. According to the stationary property of the speech, we provide a smoothing method to obtain final VAD decisions. The main contribution of the proposed algorithm concerns its ability to automatically adjust the energy threshold according to the local noise estimator. We analyzed the proposed approach by presenting a comparison with the G.729-B via the NOIZEUS database. The VAD architecture is implemented on a Microcontroller-based system (MCU). Several tests have been conducted by performing real time acquisition via the Input/Output ports of the MCU-system.

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