Locating singing voice segments is essential for convenient indexing, browsing and retrieval large music archives and catalogues. Furthermore, it is beneficial for automatic music transcription and annotations. The approach described in this paper uses Mel-Frequency Cepstral Coefficients in conjunction with Gaussian Mixture Models for discriminating two classes of data (instrumental music and singing voice with music background). Due to imperfect classification behavior, the categorization without additional post-processing tends to alternate within a very short time span, whereas singing voice tends to be continuous for several frames. Thus, various tests have been performed to identify a suitable decision function and corresponding smoothing methods. Results are reported by comparing the performance of straightforward likelihood based classifications vs. postprocessing with an autoregressive moving average filtering method.

Download