Filterbank coefficients selection for segmentation in singer turns

Abstract

Audio segmentation is often the first step of audio indexing systems. It provides segments supposed to be acoustically homogeneous. In this paper, we report our recent experiments on segmenting music recordings into singer turns, by analogy with speaker turns in speech processing. We compare several acoustic features for this task: FilterBANK coefficients (FBANK), and Mel frequency cepstral coefficients (MFCC). FBANK features were shown to outperform MFCC on a “clean” singing corpus. We describe a coefficient selection method that allowed further improvement on this corpus. A 75.8% F-measure was obtained with FBANK features selected with this method, corresponding to a 30.6% absolute gain compared to MFCC. On another corpus comprised of ethno-musicological recordings, both feature types showed a similar performance of about 60%. This corpus presents an increased difficulty due to the presence of instruments overlapped with singing and to a lower recording audio quality.