[IPOL announce] new article: A Presentation and Short Discussion of rVAD-fast, a Fast Voice Activity Detector

[announcements about the IPOL journal]

A new article is available in IPOL: https://www.ipol.im/pub/art/2022/427/

Sam Perochon,
A Presentation and Short Discussion of rVAD-fast, a Fast Voice Activity 
Detector,
Image Processing On Line, 12 (2022), pp. 404–419.
https://doi.org/10.5201/ipol.2022.427

Abstract
Voice activity detection (VAD) usually refers to the detection of human 
voices in acoustic signals and is often used as a pre-processing step in 
numerous audio signal processing tasks. The unsupervised method proposed 
here was originally developed by Zheng-Hua Tan, Achintya kr. Sarkar and 
Najim Dehak [Computer Speech & Language, 2020] and consists of a robust 
segment-based approach. The voice activity detection stage follows two 
denoising steps. The first one detects high energy segments using a 
posteriori SNR weighted energy difference, and the second enhances the 
speech using the MSNE-mod approach. Use cases or downstream tasks 
include intrusion detection, speech-to-text, speaker diarization, or 
emotion estimation.