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.
| Original language | English |
|---|---|
| Title of host publication | 2009 IEEE International Conference on Fuzzy Systems - Proceedings |
| Pages | 47-52 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 2009 Dec 10 |
| Externally published | Yes |
| Event | 2009 IEEE International Conference on Fuzzy Systems - Jeju Island, Korea, Republic of Duration: 2009 Aug 20 → 2009 Aug 24 |
Publication series
| Name | IEEE International Conference on Fuzzy Systems |
|---|---|
| ISSN (Print) | 1098-7584 |
Conference
| Conference | 2009 IEEE International Conference on Fuzzy Systems |
|---|---|
| Country | Korea, Republic of |
| City | Jeju Island |
| Period | 09/8/20 → 09/8/24 |
Fingerprint
ASJC Scopus subject areas
- Software
- Theoretical Computer Science
- Artificial Intelligence
- Applied Mathematics
Cite this
Evolutionary hypernetworks for learning to generate music from examples. / Kim, Hyun Woo; Kim, Byoung Hee; Zhang, Byoung Tak.
2009 IEEE International Conference on Fuzzy Systems - Proceedings. 2009. p. 47-52 5277047 (IEEE International Conference on Fuzzy Systems).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Evolutionary hypernetworks for learning to generate music from examples
AU - Kim, Hyun Woo
AU - Kim, Byoung Hee
AU - Zhang, Byoung Tak
PY - 2009/12/10
Y1 - 2009/12/10
N2 - 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.
AB - 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.
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U2 - 10.1109/FUZZY.2009.5277047
DO - 10.1109/FUZZY.2009.5277047
M3 - Conference contribution
SN - 9781424435975
T3 - IEEE International Conference on Fuzzy Systems
SP - 47
EP - 52
BT - 2009 IEEE International Conference on Fuzzy Systems - Proceedings
ER -