Title :
Automatic Music Mood Classification Based on Timbre and Modulation Features
Author :
Jia-Min Ren ; Ming-Ju Wu ; Jang, Jyh-Shing Roger
Author_Institution :
Data Anal. Technol. Dept., Ind. Technol. Res. Inst., Hsinchu, Taiwan
Abstract :
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.
Keywords :
acoustic signal processing; audio signal processing; emotion recognition; feature extraction; music; pattern classification; spectral analysis; statistical analysis; AMSCM; AMSFM; AMSV; acoustic frequency feature extraction; acoustic-modulation spectral contrast; acoustic-modulation spectral crest measure; acoustic-modulation spectral flatness measure; acoustic-modulation spectral valley; automatic music mood classification; long-term modulation features; modulation analysis; modulation frequency feature extraction; modulation spectral analysis; short-term timbre modulation features; statistical descriptors; two-dimensional acoustic frequency representation; two-dimensional modulation frequency representation; Feature extraction; Frequency modulation; Mel frequency cepstral coefficient; Mood; Timbre; Music mood classification; modulation spectrogram; octave-based spectral contrast/valley; spectral flatness/crest measure;
Journal_Title :
Affective Computing, IEEE Transactions on
DOI :
10.1109/TAFFC.2015.2427836