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Evolutionary hypernetworks (EHNs) are recently introduced models for learning higher-order probabilistic relations of data by an evolutionary self-organizing process. We present a method that enables EHNs to learn and generate music from examples. Short-term and long-term sequential patterns can be extracted and combined to generate music with various styles by our method. Based on a music corpus consisting of several genres and artists, an EHN generates genre-specific or artist-dependent musicdoi:10.1109/fuzzy.2009.5277047 dblp:conf/fuzzIEEE/KimKZ09 fatcat:g3nli2kr6jcubmi4xy6hzq247q