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
Hauptverfasser: Jia-Min Ren, Ming-Ju Wu, Jang, Jyh-Shing Roger
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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.
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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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