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While most schemes for automatic cover song identification have focused onnote-based features such as HPCP and chord profiles, a few recent paperssurprisingly showed that local self-similarities of MFCC-based features alsohave classification power for this task. Since MFCC and HPCP capturecomplementary information, we design an unsupervised algorithm that combinesnormalized, beat-synchronous blocks of these features using cross-similarityfusion before attempting to locally align a pair of songs. As an added bonus,our scheme naturally incorporates structural information in each song to fillin alignment gaps where both feature sets fail. We show a striking jump inperformance over MFCC and HPCP alone, achieving a state of the art meanreciprocal rank of 0.87 on the Covers80 dataset. We also introduce a newmedium-sized hand designed benchmark dataset called "Covers 1000," whichconsists of 395 cliques of cover songs for a total of 1000 songs, and we showthat our algorithm achieves an MRR of 0.9 on this dataset for the firstcorrectly identified song in a clique. We provide the precomputed HPCP and MFCCfeatures, as well as beat intervals, for all songs in the Covers 1000 datasetfor use in further research.

Early MFCC And HPCP Fusion for Robust Cover Song Identification