dc.contributor.author |
Tralie, Christopher |
|
dc.date.accessioned |
2017-12-11T15:06:08Z |
|
dc.date.available |
2017-12-11T15:06:08Z |
|
dc.date.issued |
2017-12-11 |
|
dc.identifier |
http://arxiv.org/abs/1707.04680v1 |
|
dc.identifier.uri |
https://hdl.handle.net/10161/15840 |
|
dc.description.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.
|
|
dc.format.extent |
11 pages, 7 figures |
|
dc.subject |
cs.IR |
|
dc.subject |
cs.IR |
|
dc.subject |
cs.SD |
|
dc.subject |
H.5.5 |
|
dc.title |
Early MFCC And HPCP Fusion for Robust Cover Song Identification |
|
dc.type |
Journal article |
|
pubs.author-url |
http://arxiv.org/abs/1707.04680v1 |
|
pubs.notes |
Proceedings of The International Society for Music Information Retrieval (ISMIR) 2017 |
|
pubs.organisational-group |
Duke |
|
pubs.organisational-group |
Mathematics |
|
pubs.organisational-group |
Staff |
|
pubs.organisational-group |
Temp group - logins allowed |
|
pubs.organisational-group |
Trinity College of Arts & Sciences |
|