Automatic Music Mood Classification Based on Timbre and Modulation Features
In recent years, many short-term timbre and long-term modulation features have been developed for content-based music classification. However, two operations in modulation analysis are likely to smooth out useful modulation information, which may degrade classification performance. To deal with this...
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Veröffentlicht in: | IEEE transactions on affective computing 2015-07, Vol.6 (3), p.236-246 |
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description | In recent years, many short-term timbre and long-term modulation features have been developed for content-based music classification. However, two operations in modulation analysis are likely to smooth out useful modulation information, which may degrade classification performance. To deal with this problem, this paper proposes the use of a two-dimensional representation of acoustic frequency and modulation frequency to extract joint acoustic frequency and modulation frequency features. Long-term joint frequency features, such as acoustic-modulation spectral contrast/valley (AMSC/AMSV), acoustic-modulation spectral flatness measure (AMSFM), and acoustic-modulation spectral crest measure (AMSCM), are then computed from the spectra of each joint frequency subband. By combining the proposed features, together with the modulation spectral analysis of MFCC and statistical descriptors of short-term timbre features, this new feature set outperforms previous approaches with statistical significance. |
doi_str_mv | 10.1109/TAFFC.2015.2427836 |
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However, two operations in modulation analysis are likely to smooth out useful modulation information, which may degrade classification performance. To deal with this problem, this paper proposes the use of a two-dimensional representation of acoustic frequency and modulation frequency to extract joint acoustic frequency and modulation frequency features. Long-term joint frequency features, such as acoustic-modulation spectral contrast/valley (AMSC/AMSV), acoustic-modulation spectral flatness measure (AMSFM), and acoustic-modulation spectral crest measure (AMSCM), are then computed from the spectra of each joint frequency subband. By combining the proposed features, together with the modulation spectral analysis of MFCC and statistical descriptors of short-term timbre features, this new feature set outperforms previous approaches with statistical significance.</description><identifier>ISSN: 1949-3045</identifier><identifier>EISSN: 1949-3045</identifier><identifier>DOI: 10.1109/TAFFC.2015.2427836</identifier><identifier>CODEN: ITACBQ</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Feature extraction ; Frequency modulation ; Mel frequency cepstral coefficient ; modulation spectrogram ; Mood ; Music mood classification ; octave-based spectral contrast/valley ; spectral flatness/crest measure ; Timbre</subject><ispartof>IEEE transactions on affective computing, 2015-07, Vol.6 (3), p.236-246</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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However, two operations in modulation analysis are likely to smooth out useful modulation information, which may degrade classification performance. To deal with this problem, this paper proposes the use of a two-dimensional representation of acoustic frequency and modulation frequency to extract joint acoustic frequency and modulation frequency features. Long-term joint frequency features, such as acoustic-modulation spectral contrast/valley (AMSC/AMSV), acoustic-modulation spectral flatness measure (AMSFM), and acoustic-modulation spectral crest measure (AMSCM), are then computed from the spectra of each joint frequency subband. By combining the proposed features, together with the modulation spectral analysis of MFCC and statistical descriptors of short-term timbre features, this new feature set outperforms previous approaches with statistical significance.</description><subject>Feature extraction</subject><subject>Frequency modulation</subject><subject>Mel frequency cepstral coefficient</subject><subject>modulation spectrogram</subject><subject>Mood</subject><subject>Music mood classification</subject><subject>octave-based spectral contrast/valley</subject><subject>spectral flatness/crest measure</subject><subject>Timbre</subject><issn>1949-3045</issn><issn>1949-3045</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpNUE1Lw0AQXUTBUvsH9BLwnLrJ7Ef2WINRseIlnpf9hJS2qbvJwX_vxhRxDjMP3nszzEPotsDrosDiod00Tb0ucUHXJSl5BewCLQpBRA6Y0Mt_-BqtYtzhVADASr5Ab5tx6A9q6Ez2Psap973N6r2KsfOdSUR_zB5VdDZLoO0OOrhMHW3S2XE_041TwxhcvEFXXu2jW53nEn02T239km8_nl_rzTY3paBDTo0VpsSegtWGEM6VrjR4ogEDZUooXxEmdMWcAG4rRQXmugLOwVaeOAZLdD_vPYX-a3RxkLt-DMd0UhacCjr9x5OqnFUm9DEG5-UpdAcVvmWB5ZSb_M1NTrnJc27JdDebOufcn4FjwRkD-AHwP2g6</recordid><startdate>20150701</startdate><enddate>20150701</enddate><creator>Jia-Min Ren</creator><creator>Ming-Ju Wu</creator><creator>Jang, Jyh-Shing Roger</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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However, two operations in modulation analysis are likely to smooth out useful modulation information, which may degrade classification performance. To deal with this problem, this paper proposes the use of a two-dimensional representation of acoustic frequency and modulation frequency to extract joint acoustic frequency and modulation frequency features. Long-term joint frequency features, such as acoustic-modulation spectral contrast/valley (AMSC/AMSV), acoustic-modulation spectral flatness measure (AMSFM), and acoustic-modulation spectral crest measure (AMSCM), are then computed from the spectra of each joint frequency subband. By combining the proposed features, together with the modulation spectral analysis of MFCC and statistical descriptors of short-term timbre features, this new feature set outperforms previous approaches with statistical significance.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/TAFFC.2015.2427836</doi><tpages>11</tpages></addata></record> |
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subjects | Feature extraction Frequency modulation Mel frequency cepstral coefficient modulation spectrogram Mood Music mood classification octave-based spectral contrast/valley spectral flatness/crest measure Timbre |
title | Automatic Music Mood Classification Based on Timbre and Modulation Features |
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