Show simple item record

dc.contributor.authorFoster, P
dc.contributor.authorDixon, S
dc.contributor.authorKlapuri, A
dc.date.accessioned2015-09-15T08:27:24Z
dc.date.available2015-09-15T08:27:24Z
dc.date.issued2015-06-01
dc.identifier.issn1558-7916
dc.identifier.urihttp://qmro.qmul.ac.uk/xmlui/handle/123456789/8653
dc.descriptionThis work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/
dc.description.abstractThis paper investigates methods for quantifying similarity between audio signals, specifically for the task of cover song detection. We consider an information-theoretic approach, where we compute pairwise measures of predictability between time series. We compare discrete-valued approaches operating on quantized audio features, to continuous-valued approaches. In the discrete case, we propose a method for computing the normalized compression distance, where we account for correlation between time series. In the continuous case, we propose to compute information-based measures of similarity as statistics of the prediction error between time series. We evaluate our methods on two cover song identification tasks using a data set comprised of 300 Jazz standards and using the Million Song Dataset. For both datasets, we observe that continuous-valued approaches outperform discrete-valued approaches. We consider approaches to estimating the normalized compression distance (NCD) based on string compression and prediction, where we observe that our proposed normalized compression distance with alignment (NCDA) improves average performance over NCD, for sequential compression algorithms. Finally, we demonstrate that continuous-valued distances may be combined to improve performance with respect to baseline approaches. Using a large-scale filter-and-refine approach, we demonstrate state-of-the-art performance for cover song identification using the Million Song Dataset.
dc.description.sponsorshipThe work of P. Foster was supported by an Engineering and Physical Sciences Research Council Doctoral Training Account studentship.
dc.format.extent993 - 1005
dc.relation.isreplacedby123456789/11224
dc.relation.isreplacedbyhttp://qmro.qmul.ac.uk/xmlui/handle/123456789/11224
dc.titleIdentifying Cover Songs Using Information-Theoretic Measures of Similarity
dc.typeJournal Article
dc.identifier.doi10.1109/TASLP.2015.2416655
dc.relation.isPartOfIEEE Transactions on Audio, Speech and Language Processing
pubs.issue6
pubs.volume23


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record