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Enhancing timbre model using MFCC and its time derivatives for music similarity estimation

Enhancing timbre model using MFCC and its time derivatives for music similarity estimation
Enhancing timbre model using MFCC and its time derivatives for music similarity estimation
One of the popular methods for content-based music similarity estimation is to model timbre with MFCC as a
single multivariate Gaussian with full covariance matrix,
then use symmetric Kullback-Leibler divergence. From the
field of speech recognition, we propose to use the same
approach on the MFCCs’ time derivatives to enhance the
timbre model. The Gaussian models for the delta and
acceleration coefficients are used to create their respective distance matrix. The distance matrices are then combined linearly to form a full distance matrix for music similarity estimation. In our experiments on two datasets, our novel approach performs better than using MFCC alone.
Moreover, performing genre classification using k-NN
showed that the accuracies obtained are already close to the
state-of-the-art.
2005-2009
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) Enhancing timbre model using MFCC and its time derivatives for music similarity estimation. EUSIPCO 2012: 20th European Signal Processing Conference, Romania. 27 - 31 Aug 2012. pp. 2005-2009 .

Record type: Conference or Workshop Item (Paper)

Abstract

One of the popular methods for content-based music similarity estimation is to model timbre with MFCC as a
single multivariate Gaussian with full covariance matrix,
then use symmetric Kullback-Leibler divergence. From the
field of speech recognition, we propose to use the same
approach on the MFCCs’ time derivatives to enhance the
timbre model. The Gaussian models for the delta and
acceleration coefficients are used to create their respective distance matrix. The distance matrices are then combined linearly to form a full distance matrix for music similarity estimation. In our experiments on two datasets, our novel approach performs better than using MFCC alone.
Moreover, performing genre classification using k-NN
showed that the accuracies obtained are already close to the
state-of-the-art.

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More information

Published date: 27 August 2012
Venue - Dates: EUSIPCO 2012: 20th European Signal Processing Conference, Romania, 2012-08-27 - 2012-08-31
Organisations: Web & Internet Science

Identifiers

Local EPrints ID: 361426
URI: https://eprints.soton.ac.uk/id/eprint/361426
PURE UUID: 4f9228de-3935-4ef1-b57d-38e7b4ee0d04
ORCID for Kirk Martinez: ORCID iD orcid.org/0000-0003-3859-5700

Catalogue record

Date deposited: 23 Jan 2014 16:34
Last modified: 15 Oct 2019 00:52

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