Cover song detection: From high scores to general classification

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 from the system is a binary classification of whether the reference/test pair is either from a reference/cover or reference/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 SVM or multi-layer perceptron (MLP). On the covers80 test set, the system achieves an equal error rate of

• Type: Conference
• Year: 2010
• Where: ICASSP
• Authors: Suman Ravuri, Daniel P. W. Ellis
• Keywords: Cover Song Detection, Cover Songs, ICASSP 2010, Prior Knowledge, Signal Processing