@inproceedings{6fcedf7c5eee48768de2915a21af015c,
title = "Evolutionary hypernetworks for learning to generate music from examples",
abstract = "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.",
author = "Kim, {Hyun Woo} and Kim, {Byoung Hee} and Zhang, {Byoung Tak}",
year = "2009",
doi = "10.1109/FUZZY.2009.5277047",
language = "English",
isbn = "9781424435975",
series = "IEEE International Conference on Fuzzy Systems",
pages = "47--52",
booktitle = "2009 IEEE International Conference on Fuzzy Systems - Proceedings",
note = "2009 IEEE International Conference on Fuzzy Systems ; Conference date: 20-08-2009 Through 24-08-2009",
}