Classified comfort noise generation for efficient voice transmission

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.