Cover Song Detection: From High Scores to General Classification

Articles:

Title:: Cover Song Detection: From High Scores to General Classification
Author(s):: Ravuri, Suman
Ellis, Daniel P. W.
Date:: 2010
Type:: Articles
Department:: Electrical Engineering
Permanent URL:: http://hdl.handle.net/10022/AC:P:13648
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:: IEEE
Publisher Location:: Piscataway, N.J.
Abstract:: 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.
Subject(s):: Electrical engineering
Information science
DOI:: http://dx.doi.org/10.1109/ICASSP.2010.5496214