Neural network based systems for computer-aided musical composition: supervised x unsupervised learning

Debora Correa, Alexandre L. M Levada, Jose H Saito, Joao F Mari

Research output: Chapter in Book/Conference paperConference paperpeer-review


This ongoing project describes neural network applications for helping musical composition using as inspiration the natural landscape contours. We propose supervised and unsupervised learning approaches, by using Back-Propagation-Through-Time (BPTT) and Self Organizing Maps (SOM) neural networks. In the supervised learning, the network learns certain aspects of musical structure by means of measure examples taken from melodies of the training set and uses these measures learned to compose new melodies using as input the extracted data of the landscapes contour. In the unsupervised learning, the network also uses measure examples as input during training and the extracted data of the landscapes contour in the composition stage. The obtained results show the viability of both approaches.
Original languageEnglish
Title of host publicationSAC '08
Subtitle of host publicationProceedings of the 2008 ACM symposium on Applied computing
PublisherAssociation for Computing Machinery (ACM)
Number of pages5
ISBN (Electronic)978-1-59593-753-7
Publication statusPublished - 16 Mar 2008
Externally publishedYes
EventThe 2008 ACM Symposium on Applied Computing
- Fortaleza, Brazil
Duration: 16 Mar 200820 Mar 2008


ConferenceThe 2008 ACM Symposium on Applied Computing
Abbreviated titleSAC '08
Internet address

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