Electronic Dance Music (EDM) is a class of music produced primarily using electronic instruments and computers comprising several sub-genres. Disc jockeys (DJs) mix EDM songs from similar sub-genres for live performances in concerts and festivals. Given a large database of electronic music, if a system can classify songs into their respective sub-genres, it can greatly help producers and DJs while making mixes. We describe an approach to classify four major sub-genres of EDM using artificial neural networks. We extracted several rhythmic and statistical features from a database of 400 songs, evenly distributed among the four sub-genres, and used these features to train a Back Propagation Network (BPN) and a Probabilistic Neural Network (PNN). In addition to classification, we also employed the RELIEFF feature selection algorithm to reduce the number of features and improve our results. Comparing the performances of the two networks on the basis of their correct classifications, we observed that BPN combined with the features proposed yielded a better result of 94.16%. © 2014 IEEE.
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|Journal||Data powered by Typeset2014 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)|
|Publisher||Data powered by TypesetIEEE|