Title: Early MFCC And HPCP Fusion for Robust Cover Song Identification,
Abstract: While most schemes for automatic cover song identification have focused on
note-based features such as HPCP and chord profiles, a few recent papers
surprisingly showed that local self-similarities of MFCC-based features also
have classification power for this task. Since MFCC and HPCP capture
complementary information, we design an unsupervised algorithm that combines
normalized, beat-synchronous blocks of these features using cross-similarity
fusion before attempting to locally align a pair of songs. As an added bonus,
our scheme naturally incorporates structural information in each song to fill
in alignment gaps where both feature sets fail. We show a striking jump in
performance over MFCC and HPCP alone, achieving a state of the art mean
reciprocal rank of 0.87 on the Covers80 dataset. We also introduce a new
medium-sized hand designed benchmark dataset called "Covers 1000," which
consists of 395 cliques of cover songs for a total of 1000 songs, and we show
that our algorithm achieves an MRR of 0.9 on this dataset for the first
correctly identified song in a clique. We provide the precomputed HPCP and MFCC
features, as well as beat intervals, for all songs in the Covers 1000 dataset
for use in further research.