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| Content Provider | ACM Digital Library |
|---|---|
| Author | Wang, Ye Nwe, Tin Lay Shenoy, Arun |
| Abstract | We propose a novel technique for the automatic classification of vocal and non-vocal regions in an acoustic musical signal. Our technique uses a combination of harmonic content attenuation using higher level musical knowledge of key followed by sub-band energy processing to obtain features from the musical audio signal. We employ a Multi-Model Hidden Markov Model (MM-HMM) classifier for vocal and non-vocal classification that utilizes song structure information to create multiple models as opposed to conventional HMM training methods that employ only one model for each class. A statistical hypothesis testing approach followed by an automatic bootstrapping process is employed to further improve the accuracy of classification. An experimental evaluation on a database of 20 popular songs shows the validity of the proposed approach with an average classification accuracy of 86.7%. |
| Starting Page | 324 |
| Ending Page | 327 |
| Page Count | 4 |
| File Format | |
| ISBN | 1581138938 |
| DOI | 10.1145/1027527.1027602 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2004-10-10 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Bootstrapping Song structure Hmm Singing voice |
| Content Type | Text |
| Resource Type | Article |
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