An emotion-driven musical piece generator for a constructive adaptive user interface

Authors:
Roberto Legaspi;Yuya Hashimoto;Masayuki Numao
Affiliations:
The Institute of Scientific and Industrial Research, Osaka University, Ibaraki, Osaka, Japan;The Institute of Scientific and Industrial Research, Osaka University, Ibaraki, Osaka, Japan;The Institute of Scientific and Industrial Research, Osaka University, Ibaraki, Osaka, Japan
Venue:
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Year:
2006

Citing 7
Cited 1

Refining a relational theory with multiple faults in the concept and subconcepts

ML92 Proceedings of the ninth international workshop on Machine learning
Learning Logical Definitions from Relations

Machine Learning
Unsupervised Learning and Interactive Jazz/Blues Improvisation

Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
AI and music from composition to expressive performance

AI Magazine
Constructive adaptive user interfaces: composing music based on human feelings

Eighteenth national conference on Artificial intelligence
Discovering simple rules in complex data: a meta-learning algorithm and some surprising musical discoveries

Artificial Intelligence
Relational IBL in classical music

Machine Learning

Modelling affective-based music compositional intelligence with the aid of ANS analyses

Knowledge-Based Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents the results of recent modification in the Constructive Adaptive User Interface (CAUI) that induces a model of emotional impressions towards certain musical piece structures and improvises a piece based on the model. The CAUI previously employed a ready-made melody generating module with its internal workings abstracted from the CAUI. Utilizing such black-box modules, however, may impede further effort to enhance the CAUI. To address this problem, a replacement module that automatically creates tunes tailored to the listener's impressions has been incorporated. Current results indicate that the CAUI may induce relevant relations that can support the adaptive improvisation of impression-causing tunes of a musical piece.