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Towards efficient music genre classification using FastMap

Towards efficient music genre classification using FastMap
Towards efficient music genre classification using FastMap
Automatic genre classification aims to correctly categorize an unknown recording with a music genre. Recent studies use the Kullback-Leibler (KL) divergence to estimate music similarity then perform classification using k-nearest neighbours (k-NN). However, this approach is not practical for large databases. We propose an efficient genre classifier that addresses the scalability problem. It uses a combination of modified FastMap algorithm and KL divergence to return the nearest neighbours then use 1- NN for classification. Our experiments showed that high accuracies are obtained while performing classification in less than 1/20 second per track.
de Leon, Franz
49495c02-9bb1-4366-b354-a49268e42c8b
Martinez, Kirk
5f711898-20fc-410e-a007-837d8c57cb18
de Leon, Franz
49495c02-9bb1-4366-b354-a49268e42c8b
Martinez, Kirk
5f711898-20fc-410e-a007-837d8c57cb18

de Leon, Franz and Martinez, Kirk (2012) Towards efficient music genre classification using FastMap. Towards efficient music genre classification using FastMap. 4 pp .

Record type: Conference or Workshop Item (Paper)

Abstract

Automatic genre classification aims to correctly categorize an unknown recording with a music genre. Recent studies use the Kullback-Leibler (KL) divergence to estimate music similarity then perform classification using k-nearest neighbours (k-NN). However, this approach is not practical for large databases. We propose an efficient genre classifier that addresses the scalability problem. It uses a combination of modified FastMap algorithm and KL divergence to return the nearest neighbours then use 1- NN for classification. Our experiments showed that high accuracies are obtained while performing classification in less than 1/20 second per track.

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More information

Published date: 17 September 2012
Venue - Dates: Towards efficient music genre classification using FastMap, 2012-09-17
Organisations: Web & Internet Science, Electronics & Computer Science

Identifiers

Local EPrints ID: 361423
URI: https://eprints.soton.ac.uk/id/eprint/361423
PURE UUID: f32f0f86-b531-4aa6-9a62-fc9ab99b48b5
ORCID for Kirk Martinez: ORCID iD orcid.org/0000-0003-3859-5700

Catalogue record

Date deposited: 23 Jan 2014 16:08
Last modified: 31 Jul 2019 00:50

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