Music feature extraction based on fractal dimension theory for music recommendation system

DOI

10.2991/icmia-16.2016.97How to use a DOI?

Keywords

feature extraction, spectral envelope, Hilbert transform, fractal dimension

Abstract

Music feature extraction is widely used in music recommendation system. The recommended music is somewhat similar in the form of melody. This phenomenon exhibits a repeating pattern that between the music set and the recommended one, which reveals that the similar music has the characteristics of fractal dimension. In this paper, we selected time energy, frequency energy, Mel-Frequency Cepstral Coefficient (MFCC) and spectral envelope as the music features. These four features were integrated as the feature vectors that were calculated the fractal dimension by Hilbert transform. Compared with the traditional content-based music retrieval, this method focuses on the certain degree of self-similarity between the whole and the local area. This result shows that the feature extraction approach of fractal dimension provide an effective method for music retrieval.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Bi Li
AU  - Qiang Tao
AU  - Xiang Li
PY  - 2016/11
DA  - 2016/11
TI  - Music feature extraction based on fractal dimension theory for music recommendation system
BT  - Proceedings of the 2016 5th International Conference on Measurement, Instrumentation and Automation (ICMIA 2016)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/icmia-16.2016.97
DO  - 10.2991/icmia-16.2016.97
ID  - Li2016/11
ER  -