Modelling Musical Cognition With Artificial Neural Networks
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Modelling Musical Cognition With Artificial Neural Networks
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Author : Petri Toiviainen
language : en
Publisher: University of Jyvaskyla
Release Date : 1996
Modelling Musical Cognition With Artificial Neural Networks written by Petri Toiviainen and has been published by University of Jyvaskyla this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Music categories.
Yhteenveto: Musiikin kognition mallintaminen keinotekoisilla hermoverkoilla.
Musical Networks
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Author : Niall Griffith
language : en
Publisher: MIT Press
Release Date : 1999
Musical Networks written by Niall Griffith and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with Music categories.
This volume presents the most up-to-date collection of neural network models of music and creativity gathered together in one place. Chapters by leaders in the field cover new connectionist models of pitch perception, tonality, musical streaming, sequential and hierarchical melodic structure, composition, harmonization, rhythmic analysis, sound generation, and creative evolution. The collection combines journal papers on connectionist modeling, cognitive science, and music perception with new papers solicited for this volume. It also contains an extensive bibliography of related work. Contributors Shumeet Baluja, M.I. Bellgard, Michael A. Casey, Garrison W. Cottrell, Peter Desain, Robert O. Gjerdingen, Mike Greenhough, Niall Griffith, Stephen Grossberg, Henkjan Honing, Todd Jochem, Bruce F. Katz, John F. Kolen, Edward W. Large, Michael C. Mozer, Michael P.A. Page, Caroline Palmer, Jordan B. Pollack, Dean Pomerleau, Stephen W. Smoliar, Ian Taylor, Peter M. Todd, C.P. Tsang, Gregory M. Werner
Music And Connectionism
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Author : Peter M. Todd
language : en
Publisher: MIT Press
Release Date : 1991
Music And Connectionism written by Peter M. Todd and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991 with Computers categories.
Annotation As one of our highest expressions of thought and creativity, music has always been a difficult realm to capture, model, and understand. The connectionist paradigm, now beginning to provide insights into many realms of human behavior, offers a new and unified viewpoint from which to investigate the subtleties of musical experience. Music and Connectionism provides a fresh approach to both fields, using the techniques of connectionism and parallel distributed processing to look at a wide range of topics in music research, from pitch perception to chord fingering to composition.The contributors, leading researchers in both music psychology and neural networks, address the challenges and opportunities of musical applications of network models. The result is a current and thorough survey of the field that advances understanding of musical phenomena encompassing perception, cognition, composition, and performance, and in methods for network design and analysis.Peter M. Todd is a doctoral candidate in the PDP Research Group of the Psychology Department at Stanford University. Gareth Loy is an award-winning composer, a lecturer in the Music Department of the University of California, San Diego, and a member of the technical staff of Frox Inc.Contributors. Jamshed J. Bharucha. Peter Desain. Mark Dolson. Robert Gjerclingen. Henkjan Honing. B. Keith Jenkins. Jacqueline Jons. Douglas H. Keefe. Tuevo Kohonen. Bernice Laden. Pauli Laine. Otto Laske. Marc Leman. J. P. Lewis. Christoph Lischka. D. Gareth Loy. Ben Miller. Michael Mozer. Samir I. Sayegh. Hajime Sano. Todd Soukup. Don Scarborough. Kalev Tiits. Peter M. Todd. Kari Torkkola.
Deep Learning Techniques For Music Generation
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Author : Jean-Pierre Briot
language : en
Publisher: Springer
Release Date : 2019-11-08
Deep Learning Techniques For Music Generation written by Jean-Pierre Briot and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-08 with Computers categories.
This book is a survey and analysis of how deep learning can be used to generate musical content. The authors offer a comprehensive presentation of the foundations of deep learning techniques for music generation. They also develop a conceptual framework used to classify and analyze various types of architecture, encoding models, generation strategies, and ways to control the generation. The five dimensions of this framework are: objective (the kind of musical content to be generated, e.g., melody, accompaniment); representation (the musical elements to be considered and how to encode them, e.g., chord, silence, piano roll, one-hot encoding); architecture (the structure organizing neurons, their connexions, and the flow of their activations, e.g., feedforward, recurrent, variational autoencoder); challenge (the desired properties and issues, e.g., variability, incrementality, adaptability); and strategy (the way to model and control the process of generation, e.g., single-step feedforward, iterative feedforward, decoder feedforward, sampling). To illustrate the possible design decisions and to allow comparison and correlation analysis they analyze and classify more than 40 systems, and they discuss important open challenges such as interactivity, originality, and structure. The authors have extensive knowledge and experience in all related research, technical, performance, and business aspects. The book is suitable for students, practitioners, and researchers in the artificial intelligence, machine learning, and music creation domains. The reader does not require any prior knowledge about artificial neural networks, deep learning, or computer music. The text is fully supported with a comprehensive table of acronyms, bibliography, glossary, and index, and supplementary material is available from the authors' website.
Connectionist Models Of Musical Thinking
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Author : Harold E. Fiske
language : en
Publisher: Lewiston, N.Y. ; Queenston, Ont. : E. Mellen Press
Release Date : 2004
Connectionist Models Of Musical Thinking written by Harold E. Fiske and has been published by Lewiston, N.Y. ; Queenston, Ont. : E. Mellen Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Music categories.
For the past decade, Fiske (music, U. of Western Ontario) has been using neural network models to test his theory that musical thinking can be described as a hierarchy of progressively more intricate pattern-comparison activity, and that the resulting musical realizations are limited to only three cognitive category types. He describes the development of his theory, several related experimental studies, and the neural network models he uses to test the theory. Neural network methodology can seem daunting, he admits, so he has tried to keep technical descriptions to a minimum in order to highlight his main goal: to describe and test a set of principles that appear to represent the foundation of musical understanding. Annotation : 2004 Book News, Inc., Portland, OR (booknews.com).
Connectionist Representations Of Tonal Music
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Author : Michael R. W. Dawson
language : en
Publisher: Athabasca University Press
Release Date : 2018-03-13
Connectionist Representations Of Tonal Music written by Michael R. W. Dawson and has been published by Athabasca University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-13 with Psychology categories.
Previously, artificial neural networks have been used to capture only the informal properties of music. However, cognitive scientist Michael Dawson found that by training artificial neural networks to make basic judgments concerning tonal music, such as identifying the tonic of a scale or the quality of a musical chord, the networks revealed formal musical properties that differ dramatically from those typically presented in music theory. For example, where Western music theory identifies twelve distinct notes or pitch-classes, trained artificial neural networks treat notes as if they belong to only three or four pitch-classes, a wildly different interpretation of the components of tonal music. Intended to introduce readers to the use of artificial neural networks in the study of music, this volume contains numerous case studies and research findings that address problems related to identifying scales, keys, classifying musical chords, and learning jazz chord progressions. A detailed analysis of the internal structure of trained networks could yield important contributions to the field of music cognition.
Connectionism Music And Fourier Phase Spaces
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Author : Arturo Pérez
language : en
Publisher:
Release Date : 2022
Connectionism Music And Fourier Phase Spaces written by Arturo Pérez and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Fourier analysis categories.
How does the brain represent musical properties? Even with our growing understanding of the cognitive neuroscience of music (Abbott, 2002; Peretz and Zatorre, 2003; Peretz and Zatorre, 2005; Zatorre and McGill, 2005), the answer to this question remains unclear. One method for conceiving possible representations is to use artificial neural networks, which can provide biologically plausible models of cognition (Rumelhart and McClelland, 1986; Bechtel and Abrahamsen, 2002; Enquist and Ghirlanda, 2005). One could train networks to solve musical problems, (Todd and Loy, 1991; Griffith and Todd, 1999) and then study how these networks encode musical properties. However, researchers rarely conduct detailed examinations of network structure (Dawson, 2009, 2013, 2018) because networks are difficult to interpret, and because it is assumed that networks capture informal or subsymbolic properties (Smolensky, 1988; McCloskey, 1991; Bharucha, 1999). Within this thesis, we report very high correlations between network connection weights and discrete Fourier phase spaces used to represent musical sets (Amiot, 2016; Callender, 2007; Quinn, 2006, 2007; Yust, 2016). This is remarkable because there is no clear mathematical relationship between network learning rules and discrete Fourier analysis (Rumelhart, Hinton et al., 1986; Dawson and Schopflocher, 1992; Amiot, 2016). That networks discover Fourier phase spaces indicates that these spaces have an important role to play outside of formal music theory. Finding phase spaces in networks raises the strong possibility that Fourier components are possible codes for musical cognition.
Connectionist Representations Of Tonal Music
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Author : Michael Robert William Dawson
language : en
Publisher:
Release Date : 2018
Connectionist Representations Of Tonal Music written by Michael Robert William Dawson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Computers categories.
Previously, artificial neural networks have been used to capture only the informal properties of music. However, cognitive scientist Michael Dawson found that by training artificial neural networks to make basic judgments concerning tonal music, such as identifying the tonic of a scale or the quality of a musical chord, the networks revealed formal musical properties that differ dramatically from those typically presented in music theory. For example, where Western music theory identifies twelve distinct notes or pitch-classes, trained artificial neural networks treat notes as if they belong to only three or four pitch-classes, a wildly different interpretation of the components of tonal music. Intended to introduce readers to the use of artificial neural networks in the study of music, this volume contains numerous case studies and research findings that address problems related to identifying scales, keys, classifying musical chords, and learning jazz chord progressions. A detailed analysis of the internal structure of trained networks could yield important contributions to the field of music cognition.
Connectionist Models Of Learning Development And Evolution
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Author : Robert M. French
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Connectionist Models Of Learning Development And Evolution written by Robert M. French and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Psychology categories.
Connectionist Models of Learning, Development and Evolution comprises a selection of papers presented at the Sixth Neural Computation and Psychology Workshop - the only international workshop devoted to connectionist models of psychological phenomena. With a main theme of neural network modelling in the areas of evolution, learning, and development, the papers are organized into six sections: The neural basis of cognition Development and category learning Implicit learning Social cognition Evolution Semantics Covering artificial intelligence, mathematics, psychology, neurobiology, and philosophy, it will be an invaluable reference work for researchers and students working on connectionist modelling in computer science and psychology, or in any area related to cognitive science.
The Cognitive Neuroscience Of Music
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Author : Isabelle Peretz
language : en
Publisher: OUP Oxford
Release Date : 2003-07-10
The Cognitive Neuroscience Of Music written by Isabelle Peretz and has been published by OUP Oxford this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-07-10 with Music categories.
This title includes the following features: The first book to describe the neural bases of music; Edited and written by the leading researchers in this field; An important addition to OUP's acclaimed list in music psychology