A Hierarchical Self-Organizing Map Model for Sequence Recognition

A novel neural model made up of two self-organizing maps nets – one on top of the other – is introduced and analysed experimentally. The model makes effective use of context information, and that enables it to perform sequence classification and discrimination efficiently. It was successfully applied to real sequences, taken from the third voice of the sixteenth four-part fugue in G minor of the Well-Tempered Clavier (vol. I) of J.S. Bach. The model has an application in domains which require pattern recognition, or more specifically, which demand the recognition of either a set of sequences of vectors in time or sub-sequences into a unique and large sequence of vectors in time.

[1]  Peter M. Todd,et al.  Creation by Refinement and the Problem of Algorithmic Music Composition , 2003 .

[2]  Ian J. Taylor,et al.  Modelling Pitch Perception with Adaptive Resonance Theory Artificial Neural Networks , 1994, Connect. Sci..

[3]  Teuvo Kohonen,et al.  Self-organization and associative memory: 3rd edition , 1989 .

[4]  J. P. Lewis,et al.  Algorithms for Music Composition by Neural Nets: Improved CBR Paradigms , 1989, International Conference on Mathematics and Computing.

[5]  Robert O. Gjerdingen,et al.  Categorization of Musical Patterns by Self-Organizing Neuronlike Networks , 1990 .

[6]  Risto Miikkulainen,et al.  SARDNET: A Self-Organizing Feature Map for Sequences , 1994, NIPS.

[7]  Robert O. Gjerdingen.  Using Connectionist Models to Explore Complex Musical Patterns , 1989 .

[8]  Peter M. Todd,et al.  A Connectionist Approach To Algorithmic Composition , 1989 .

[9]  Jacqueline A. Jones,et al.  Connectionist Models for Tonal Analysis , 1989 .

[10]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[11]  J. Bharucha.  Music Cognition and Perceptual Facilitation: A Connectionist Framework , 1987 .

[12]  John G. Taylor,et al.  The temporal Kohönen map , 1993, Neural Networks.

[13]  Peter M. Todd,et al.  The Ontogenesis of Tonal Semantics: Results of a Computer Study , 2003 .

[14]  Peter M. Todd,et al.  Pitch, Harmony, and Neural Nets: A Psychological Perspective , 2003 .

[15]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory, Third Edition , 1989, Springer Series in Information Sciences.

[16]  Peter Desain,et al.  Quantization of musical time: a connectionist approach , 1989 .

[17]  Peter M. Todd,et al.  Modeling the Perception of Tonal Structure with Neural Nets , 1989 .

[18]  Michael C. Mozer,et al.  Connectionist Music Composition Based on Melodic and Stylistic Constraints , 1990, NIPS.

[19]  John R. Anderson.  Cognitive Psychology and Its Implications , 1980 .

[20]  Otávio Augusto S. Carpinteiro.  A hierarchical self-organizing map model for sequence recognition , 1998 .

[21]  B. Keith Jenkins,et al.  A Neural Network Model for Pitch Perception , 1989 .

[22]  Otávio Augusto S. Carpinteiro,et al.  A Hierarchical Self-Organizing Map Model in Short-Term Load Forecasting , 2001, J. Intell. Robotic Syst..

[23]  Douglas H. Keefe,et al.  The Representation of Pitch in a Neural Net Model of Chord Classification , 1989 .

[24]  MICHAEL P. A. PAGE.  Modelling the Perception of Musical Sequences with Self-organizing Neural Networks , 1994, Connect. Sci..

[25]  Peter M. Todd,et al.  Connectionist Music Composition Based on Melodic, Stylistic, and Psychophysical Constraints , 2003 .

[26]  Z.-P. Lo,et al.  Improved rate of convergence in Kohonen neural network , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.