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. **This is an MLBriefs article, the source code has not been reviewed!**<br> **The original source code is [[available here|https://github.com/zhenghuatan/rVAD/tree/d41f5354317bf13c1d8b31cb4f7ad4bf5112cd34]] (last checked 2022/10/10).**
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