Cover Song Detection: From High Scores to General Classification
Suman Ravuri; Daniel P. W. Ellis
- Cover Song Detection: From High Scores to General Classification
Ellis, Daniel P. W.
- Electrical Engineering
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- Book/Journal Title:
- 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing: Proceedings: March 14-19, 2010, Sheraton Dallas Hotel, Dallas, Texas, U.S.A.
- Publisher Location:
- Piscataway, N.J.
- Existing cover song detection systems require prior knowledge of the number of cover songs in a test set in order to identify cover(s) to a reference song. We describe a system that does not require such prior knowledge. The input to the system is a reference track and test track, and the output is a binary classification of whether the inputs are either a reference and a cover or a reference and a non-cover. The system differs from state-of-the-art detectors by calculating multiple input features, performing a novel type of test song normalization in order to combat against "impostor" tracks, and performing classification using either a support vector machine (SVM) or multi-layer perceptron (MLP). On the covers80 test set, the system achieves an equal error rate of 10%, compared to 21.3% achieved by the 2007 LabROSA cover song detection system.
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