Incorporating scale information with cepstral features: Experiments on musical instrument recognition
We present two sets of novel features that combine multiscale representations of signals with the compact timbral description of Mel-frequency cepstral coefficients (MFCCs). We define one set of features, OverCs, from overcomplete transforms at multiple scales. We define the second set of features, SparCs, from a signal model found by sparse approximation. We compare the descriptiveness of our features against that of MFCCs by performing two simple tasks: pairwise musical instrument discrimination, and musical instrument classification. Our tests show that both OverCs and SparCs improve the characterization of the global timbre and local stationarity of an audio signal than do mean MFCCs with respect to these tasks.
musical instrument recognition audio signal classification sparse decompositions timefrequency/time-scale features