Music score alignment and computer accompaniment
Communications of the ACM - Music information retrieval
Evolutionary hypernetwork models for aptamer-based cardiovascular disease diagnosis
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
MySong: automatic accompaniment generation for vocal melodies
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Evolving hypernetwork models of binary time series for forecasting price movements on stock markets
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
DNA hypernetworks for information storage and retrieval
DNA'06 Proceedings of the 12th international conference on DNA Computing
Hypernetworks: A Molecular Evolutionary Architecture for Cognitive Learning and Memory
IEEE Computational Intelligence Magazine
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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 music fragments when a fraction of score is given as a cue. Our method shows about 88% of success rate in partial music completion task. By inspecting hyperedges in the trained hypernetworks, we can extract a set of arguments that constitutes melodic structures in music.