@inproceedings{50c1a5b268a843d0a5c806950bb23806,
title = "Visualized feature fusion and style evaluation for musical genre analysis",
abstract = "Different kinds of features in time domain, spectral domain and cepstral domain are used for musical genre classification. In this paper, through the fusion of short-term timbral features and long-term rhythmic feature, we propose a novel method where: musical genre vector is constructed using the likelihood ratio of GMM (Gaussian Mixture Model) and radar chart is applied to provide visualized style evaluation for musical genre analysis, a promising performance is achieved over our database consisting of seven different types of music. Because of the fuzzy definition of musical genres, we also investigate the music with dual-genre based on musical genre vector and radar chart.",
keywords = "Beat histogram, Feature fusion, GMM, Musical genre analysis, Musical genre vector, Radar chart",
author = "Qingjun Yao and Haifeng Li and Jiayin Sun and Lin Ma",
year = "2010",
doi = "10.1109/PCSPA.2010.218",
language = "英语",
isbn = "9780769541808",
series = "Proceedings - 2010 1st International Conference on Pervasive Computing, Signal Processing and Applications, PCSPA 2010",
pages = "883--886",
booktitle = "Proceedings - 2010 1st International Conference on Pervasive Computing, Signal Processing and Applications, PCSPA 2010",
note = "1st International Conference on Pervasive Computing, Signal Processing and Applications, PCSPA 2010 ; Conference date: 17-09-2010 Through 19-09-2010",
}