Abstract
We investigate the benefits of evaluating
Mel-frequency cepstral coefficients (MFCCs) over several time scales
in the context of automatic musical instrument identification
for 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 features
that 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 involving
multiscale decompositions perform significantly better than
features computed using a single time-resolution.
Mel-frequency cepstral coefficients (MFCCs) over several time scales
in the context of automatic musical instrument identification
for 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 features
that 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 involving
multiscale decompositions perform significantly better than
features computed using a single time-resolution.
| Original language | English |
|---|---|
| Journal | Proceedings of the European Signal Processing Conference |
| Pages (from-to) | 477-481 |
| Number of pages | 5 |
| ISSN | 2076-1465 |
| Publication status | Published - 2010 |
| Event | EUSIPCO 2010 - Aalborg, Denmark Duration: 23 Aug 2010 → … |
Conference
| Conference | EUSIPCO 2010 |
|---|---|
| Country/Territory | Denmark |
| City | Aalborg |
| Period | 23/08/2010 → … |