Timbre and Modulation Timbre and Modulation Features for Features for Music Genre/Mood Music Genre/Mood Classification Classification J.-S. Roger Jang & Jia-Min Ren J.-S. Roger Jang & Jia-Min Ren Multimedia Information Retrieval Multimedia Information Retrieval Lab Lab Dept. of CSIE, National Taiwan Dept. of CSIE, National Taiwan University University
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Timbre and Modulation Features for Music Genre/Mood Classification
Timbre and Modulation Features for Music Genre/Mood Classification. J.-S. Roger Jang & Jia -Min Ren Multimedia Information Retrieval Lab Dept. of CSIE, National Taiwan University. Outline. Audio features and modulation spectral analysis MIREX 2011 method and its improvement - PowerPoint PPT Presentation
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Timbre and Modulation Features forTimbre and Modulation Features forMusic Genre/Mood ClassificationMusic Genre/Mood Classification
J.-S. Roger Jang & Jia-Min RenJ.-S. Roger Jang & Jia-Min RenMultimedia Information Retrieval LabMultimedia Information Retrieval LabDept. of CSIE, National Taiwan UniversityDept. of CSIE, National Taiwan University
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Outline Audio features and modulation spectral analysis MIREX 2011 method and its improvement Experimental setup and results Conclusions and future work
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Introduction – music genres/moods
*pictures from www.playonradio.com, brainpickings.org & mpac.ee.ntu.edu.tw
Descriptions of music contents
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Motivation Rapid growth of digital music
Apple iTunes: 28 million songs; 7digital: 20 million tracks Organization of large collections of audio music
Important but challenging Manual labeling by tags: labor intensive/time consuming
Thus, machine learning for classification is called for!
Modulation spectral analysis of timbre features Reference
C.-H. Lee, J.-L. Shih, K.-M. Yu, and H.-S. Lin, “Automatic music genre classification based on modulation spectral analysis of spectral and cepstral features,” IEEE Trans. Multimedia, vol. 11, no. 4, pp.670-682, June 2009.
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Proposed joint acoustic frequency and modulation frequency features Motivation
Averaging and mean/std computation smooth out MD info. Computation of joint frequency features (proposed)
Compute modulation spectrogram from an entire music clip Compute SCV (spectral contrast/valley), SFM/SCM (spectral
flatness/crest measure) within each joint acoustic-modulation (AM) frequency subband AMSCV, AMSFM/AMSCM
FFT
ComputeAMSCVAMSFMAMSCM
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Audio features used in our study All possible audio features
Extract SSD, MFCC, SCV, and SFM/SCM from audio frames mean/std computation MuStd MuStd dim=2*(5+21+16+16)=116