# Voice Activity Detection system
## Description
VAD system based on Deep Neural Networks (DNN) and feature fusion (Gammatone, Gabor, Long-term Spectral Variability and voicing).
System was developed as part of the RATS (Robust Automated Transcription of Speech) program of DARPA.
## Instructions:
1. open Matlab
2. run the script **apply_vad(_path/to/audio_)**:
3. a figure will appear that shows the original signal, and VAD labels, given a directory of audio wav files.
4. to control the accuracy (depending on how noisy the files are), you can play with the parameters p1 and p2
5. additional info
Apply Voice Activity Detection to all files in a specified audio directory
--IN--
audiodir: directory of audio files (WAV format)
p1: speech/non-speech threshold [default:0.1]
p2: speech region smoothing [default:20]
--OUT--
vadout: VAD labels at frame level (10 ms)
## Reference citation:
Van Segbroeck, Maarten, Andreas Tsiartas, and Shrikanth Narayanan. _"A robust frontend for VAD: exploiting contextual, discriminative and spectral cues of human voice."_ INTERSPEECH. 2013.
**bibtex**
@inproceedings{van2013robust,
title={A robust frontend for VAD: exploiting contextual, discriminative and spectral cues of human voice.},
author={Van Segbroeck, Maarten and Tsiartas, Andreas and Narayanan, Shrikanth},
booktitle={INTERSPEECH},
pages={704--708},
year={2013}
}
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Voice Activity Detection system (Matlab-based implementation)
共22个文件
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Voice Activity Detection system (Matlab-based implementation).zip (22个子文件)
vad-master
tools
nr 443KB
apply_vad.m 3KB
models
model.mat 477KB
README.md 1KB
mfiles
spmax2.m 610B
convert_to_context_stream.m 412B
vec2featmat_d1.m 165B
FE_LTSV.m 2KB
mvn.m 210B
nntest.m 340B
voicingfeature.m 12KB
FE_MEL.m 2KB
nnff.m 1KB
FE_GBF.m 13KB
hz2meldm.m 108B
gammatone_matrix.m 4KB
sigmoid.m 690B
spc2hz.m 49B
meldm2hz.m 105B
sigm.m 48B
FE_GT.m 1KB
hz2spc.m 54B
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