Music emotion recognition: The combined evidence of MFCC and residual phase

The proposed work combines the evidence from mel frequency cepstral coefficients (MFCC) and residual phase (RP) features for emotion recognition in music. Emotion recognition in music considers the emotions namely anger, fear, happy, neutral and sad. Residual phase feature is an excitation source fe...

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Main Authors: N.J. Nalini, S. Palanivel
Format: Article
Language:English
Published: Elsevier 2016-03-01
Series:Egyptian Informatics Journal
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S1110866515000419
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author N.J. Nalini
S. Palanivel
author_facet N.J. Nalini
S. Palanivel
author_sort N.J. Nalini
collection DOAJ
description The proposed work combines the evidence from mel frequency cepstral coefficients (MFCC) and residual phase (RP) features for emotion recognition in music. Emotion recognition in music considers the emotions namely anger, fear, happy, neutral and sad. Residual phase feature is an excitation source feature and it is used to exploit emotion specific information present in music signal. The residual phase is defined as the cosine of the phase function of the analytic signal derived from the linear prediction (LP) residual and also it is demonstrated that the residual phase signal contains emotion specific information that is complementary to the MFCC features. MFCC and residual phase features are used to create separate models for each emotion. The evidence from the models is combined at the score level for each emotion and it is used to recognize the emotion. The proposed method is evaluated using music files recorded from various websites and the method achieves a performance of 96.0%, 99.0%, 95.0% using AANN, SVM, RBFNN, respectively.
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spelling doaj.art-78dd20d9b7844715aa586b4a310045272022-12-21T22:41:33ZengElsevierEgyptian Informatics Journal1110-86652016-03-0117111010.1016/j.eij.2015.05.004Music emotion recognition: The combined evidence of MFCC and residual phaseN.J. NaliniS. PalanivelThe proposed work combines the evidence from mel frequency cepstral coefficients (MFCC) and residual phase (RP) features for emotion recognition in music. Emotion recognition in music considers the emotions namely anger, fear, happy, neutral and sad. Residual phase feature is an excitation source feature and it is used to exploit emotion specific information present in music signal. The residual phase is defined as the cosine of the phase function of the analytic signal derived from the linear prediction (LP) residual and also it is demonstrated that the residual phase signal contains emotion specific information that is complementary to the MFCC features. MFCC and residual phase features are used to create separate models for each emotion. The evidence from the models is combined at the score level for each emotion and it is used to recognize the emotion. The proposed method is evaluated using music files recorded from various websites and the method achieves a performance of 96.0%, 99.0%, 95.0% using AANN, SVM, RBFNN, respectively.http://www.sciencedirect.com/science/article/pii/S1110866515000419Music emotion recognitionMel frequency cepstral coefficientResidual phaseAutoassociative neural networkSupport vector machine
spellingShingle N.J. Nalini
S. Palanivel
Music emotion recognition: The combined evidence of MFCC and residual phase
Egyptian Informatics Journal
Music emotion recognition
Mel frequency cepstral coefficient
Residual phase
Autoassociative neural network
Support vector machine
title Music emotion recognition: The combined evidence of MFCC and residual phase
title_full Music emotion recognition: The combined evidence of MFCC and residual phase
title_fullStr Music emotion recognition: The combined evidence of MFCC and residual phase
title_full_unstemmed Music emotion recognition: The combined evidence of MFCC and residual phase
title_short Music emotion recognition: The combined evidence of MFCC and residual phase
title_sort music emotion recognition the combined evidence of mfcc and residual phase
topic Music emotion recognition
Mel frequency cepstral coefficient
Residual phase
Autoassociative neural network
Support vector machine
url http://www.sciencedirect.com/science/article/pii/S1110866515000419
work_keys_str_mv AT njnalini musicemotionrecognitionthecombinedevidenceofmfccandresidualphase
AT spalanivel musicemotionrecognitionthecombinedevidenceofmfccandresidualphase