Fundamentals of speech recognition
Fundamentals of speech recognition
Markov random field modeling in computer vision
Markov random field modeling in computer vision
Multiresolution sampling procedure for analysis and synthesis of texture images
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
Learning in graphical models
Musical networks
Machine Learning
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Probabilistic characterization and synthesis of complex driven systems
Probabilistic characterization and synthesis of complex driven systems
General sound classification and similarity in MPEG-7
Organised Sound
Organised Sound
Organised Sound
Human-Scale Systems in Responsive Environments
IEEE MultiMedia
Dynamic Independent Mapping Layers for Concurrent Control of Audio and Video Synthesis
Computer Music Journal
ParticleTecture: interactive granular soundspaces for architectural design
NIME '07 Proceedings of the 7th international conference on New interfaces for musical expression
An algebra for tree-based music generation
CAI'07 Proceedings of the 2nd international conference on Algebraic informatics
AI methods in algorithmic composition: a comprehensive survey
Journal of Artificial Intelligence Research
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Pattern theory provides a set of principles for constructing generative models of the information contained in natural signals, such as images or sound. Consequently, it also represents a useful language within which to develop generative systems of art. A pattern theory inspired framework and set of algorithms for interactive computer music composition are presented in the form of a self-organising hidden Markov model – a modular, graphical approach to the representation and spontaneous organisation of sound events in time and in parameter space. The result constitutes a system for composing stochastic music which incorporates creative and structural ideas such as uncertainty, variability, hierarchy and complexity, and which bears a strong relationship to realistic models of statistical physics or perceptual systems. The pattern theory approach to composition provides an elegant set of organisational principles for the production of sound by computer. Further, its machine learning underpinnings suggest many interesting applications to emergent tasks concerning the learning and creative modification of musical forms.