Comfort noise insertion during speech pause has been applied to Voice-over-IP and wireless networks for increasing bandwidth efficiency. We present two classified comfort noise generation (CCNG) schemes using Gaussian Mixture classifiers (GMM-C). Our first scheme employs a classified prototype background noise codebook with the prototype noise waveform chosen using a GMM-C. The second scheme utilizes a classified enhanced excitation codebook. The new CCNG algorithms provide better comfort noise during speech pauses and a smaller misclassification rate. We have retrofitted the scheme into existing speech transmission system, such as ITU-T G.711/Appendix II and G.723.1/Annex A. The perceived quality of a voice conversation of the novel system has been noticeably enhanced for car and babble noise. For the G.711 system, a large improvement is obtained for car noise while the largest amelioration is for babble noise in the G.723.1 system.
Cite as: Qian, Y., Hsu, W.-S., Kabal, P. (2006) Classified comfort noise generation for efficient voice transmission. Proc. Interspeech 2006, paper 1307-Mon1FoP.6, doi: 10.21437/Interspeech.2006-63
@inproceedings{qian06_interspeech,
author={Yasheng Qian and Wei-Shou Hsu and Peter Kabal},
title={{Classified comfort noise generation for efficient voice transmission}},
year=2006,
booktitle={Proc. Interspeech 2006},
pages={paper 1307-Mon1FoP.6},
doi={10.21437/Interspeech.2006-63}
}