Daudet, Laurent

Morvidone, Marcela

Sturm, Bob L.

English

We investigate the benefits of evaluating Mel-frequency cepstral coefficients (MFCCs) over several time scalesin the context of automatic musical instrument identificationfor signals that are monophonic but derived from real musical settings.We define several sets of features derived from MFCCs computed using multiple time resolutions,and compare their performance against other featuresthat are computed using a single time resolution,such as MFCCs, and derivatives of MFCCs.We find that in each task --- pairwise discrimination, and one vs. all classification --- the features involvingmultiscale decompositions perform significantly better thanfeatures computed using a single time-resolution

Musical Instrument Identification using Multiscale Mel-frequency Cepstral Coefficients

https://vbn.aau.dk/ws/files/48186471/melfreqovercomplete.pdf

Musical Instrument Identification using Multiscale Mel-frequency Cepstral Coefficients

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