Name
Abstract | ||
|---|---|---|
We propose a technique for the automatic vocal segments detection in an acoustical polyphonic music signal. We use a combination of several characteristics specific to singing voice as the feature and employ a Gaussian Mixture Model (GMM) classifier for vocal and non-vocal classification. We have employed a pre-processing of spectral whitening and archived a performance of 81.3% over the RWC popular music dataset. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/CIS.2013.80 | CIS |
Keywords | Field | DocType |
rwc popular music dataset,gaussian mixture model,popular music,spectral whitening,non-vocal classification,automatic vocal segments detection,acoustical polyphonic music signal,gaussian processes,music,mixture models,speech recognition | Mel-frequency cepstrum,Pattern recognition,Computer science,Speech recognition,Popular music,Singing,Gaussian process,Artificial intelligence,Signal classification,Polyphony,Classifier (linguistics),Mixture model | Conference |
Citations | PageRank | References |
4 | 0.45 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liming Song | 1 | 14 | 3.08 |
| Ming Li | 2 | 4 | 0.79 |
| Yonghong Yan | 3 | 656 | 114.13 |