Anticipatory model of musical style imitation using collaborative and competitive reinforcement learning

Arshia Cont, Shlomo Dubnov, Gérard Assayag

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Scopus citations

Abstract

The role of expectation in listening and composing music has drawn much attention in music cognition since about half a century ago. In this paper, we provide a first attempt to model some aspects of musical expectation specifically pertained to short-time and working memories, in an anticipatory framework. In our proposition anticipation is the mental realization of possible predicted actions and their effect on the perception of the world at an instant in time. We demonstrate the model in applications to automatic improvisation and style imitation. The proposed model, based on cognitive foundations of musical expectation, is an active model using reinforcement learning techniques with multiple agents that learn competitively and in collaboration. We show that compared to similar models, this anticipatory framework needs little training data and demonstrates complex musical behavior such as long-term planning and formal shapes as a result of the anticipatory architecture. We provide sample results and discuss further research.

Original languageEnglish
Title of host publicationAnticipatory Behavior in Adaptive Learning Systems
Subtitle of host publicationFrom Brains to Individual and Social Behavior
PublisherSpringer Verlag
Pages285-306
Number of pages22
ISBN (Print)9783540742616
DOIs
StatePublished - 1 Jan 2007
Externally publishedYes
Event3rd Workshop on Anticipatory Behavior in Adaptive Learning Systems, ABiALS 2006 - Rome, Italy
Duration: 30 Sep 200630 Sep 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4520 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd Workshop on Anticipatory Behavior in Adaptive Learning Systems, ABiALS 2006
Country/TerritoryItaly
CityRome
Period30/09/0630/09/06

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