Novel Features for Effective Speech and Music Discrimination

Speech and music discrimination has gained much popularity in recent years for efficient coding and automatic retrieval of multimedia sources and automated speech recognition (ASR). Two novel features that can be concatenated with Mel frequency cepstral coefficients are presented in this paper: delta cepstral energy (DCE) and power spectrum deviation (PSDev). Employing a Gaussian mixture model for classification as a back-end to the system, a significant improvement in the error rate was found using these features. The effects of different musical instruments on error rates were also analyzed. Low frequency musical instruments like piano and electric bass guitar were found to be more difficult to discriminate from speech, however, the proposed features are also able to reduce such errors significantly