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Author: Arturo Pérez
Publisher:
ISBN:
Category : Fourier analysis
Languages : en
Pages : 0
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Book Description
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.
Author: Arturo Pérez
Publisher:
ISBN:
Category : Fourier analysis
Languages : en
Pages : 0
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Book Description
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.
Author: Emmanuel Amiot
Publisher: Springer
ISBN: 3319455818
Category : Computers
Languages : en
Pages : 214
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Book Description
This book explains the state of the art in the use of the discrete Fourier transform (DFT) of musical structures such as rhythms or scales. In particular the author explains the DFT of pitch-class distributions, homometry and the phase retrieval problem, nil Fourier coefficients and tilings, saliency, extrapolation to the continuous Fourier transform and continuous spaces, and the meaning of the phases of Fourier coefficients. This is the first textbook dedicated to this subject, and with supporting examples and exercises this is suitable for researchers and advanced undergraduate and graduate students of music, computer science and engineering. The author has made online supplementary material available, and the book is also suitable for practitioners who want to learn about techniques for understanding musical notions and who want to gain musical insights into mathematical problems.
Author: Rolf Bader
Publisher: Springer Science & Business Media
ISBN: 364236098X
Category : Technology & Engineering
Languages : en
Pages : 476
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Book Description
This book offers an overview of models, measurements, calculations and examples connecting musical acoustics and music psychology. Indeed, many mathematical formulations that explain musical acoustics can also be used to help predict human auditory perception.
Author: Michael R. W. Dawson
Publisher: Athabasca University Press
ISBN: 1771993421
Category : Psychology
Languages : en
Pages : 200
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Book Description
What Is Cognitive Psychology? identifies the theoretical foundations of cognitive psychology—foundations which have received very little attention in modern textbooks. Beginning with the basics of information processing, Michael R. W. Dawson explores what experimental psychologists infer about these processes and considers what scientific explanations are required when we assume cognition is rule-governed symbol manipulation. From these foundations, psychologists can identify the architecture of cognition and better understand its role in debates about its true nature. This volume offers a deeper understanding of cognitive psychology and presents ideas for integrating traditional cognitive psychology with more modern fields like cognitive neuroscience.
Author: Eduardo Reck Miranda
Publisher: Routledge
ISBN: 113665285X
Category : Performing Arts
Languages : en
Pages : 319
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Book Description
The interplay between emotional and intellectual elements feature heavily in the research of a variety of scientific fields, including neuroscience, the cognitive sciences and artificial intelligence (AI). This collection of key introductory texts by top researchers worldwide is the first study which introduces the subject of artificial intelligence and music to beginners. Eduardo Reck Miranda received a Ph.D. in music and artificial intelligence from the University of Edinburgh, Scotland. He has published several research papers in major international journals and his compositions have been performed worldwide. Also includes 57 musical examples.
Author: Jean-Pierre Briot
Publisher: Springer
ISBN: 3319701630
Category : Computers
Languages : en
Pages : 284
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Book Description
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.
Author:
Publisher:
ISBN:
Category : Physics
Languages : en
Pages : 820
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Book Description
Author: Pietro Polotti
Publisher: Logos Verlag Berlin GmbH
ISBN: 383251600X
Category : Computers
Languages : en
Pages : 487
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Book Description
Since the 1950s, Sound and Music Computing (SMC) research has had a profound impact on the development of culture and technology in our post-industrial society. SMC research approaches the whole sound and music communication chain from a multidisciplinary point of view. By combining scientific, technological and artistic methodologies it aims at understanding, modeling, representing and producing sound and music using computational approaches. This book, by describing the state of the art in SMC research, gives hints of future developments, whose general purpose will be to bridge the semantic gap, the hiatus that currently separates sound from sense and sense from sound.
Author: G. Buzsáki
Publisher: Oxford University Press
ISBN: 0199828237
Category : Medical
Languages : en
Pages : 465
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Book Description
Studies of mechanisms in the brain that allow complicated things to happen in a coordinated fashion have produced some of the most spectacular discoveries in neuroscience. This book provides eloquent support for the idea that spontaneous neuron activity, far from being mere noise, is actually the source of our cognitive abilities. It takes a fresh look at the coevolution of structure and function in the mammalian brain, illustrating how self-emerged oscillatory timing is the brain's fundamental organizer of neuronal information. The small-world-like connectivity of the cerebral cortex allows for global computation on multiple spatial and temporal scales. The perpetual interactions among the multiple network oscillators keep cortical systems in a highly sensitive "metastable" state and provide energy-efficient synchronizing mechanisms via weak links. In a sequence of "cycles," György Buzsáki guides the reader from the physics of oscillations through neuronal assembly organization to complex cognitive processing and memory storage. His clear, fluid writing-accessible to any reader with some scientific knowledge-is supplemented by extensive footnotes and references that make it just as gratifying and instructive a read for the specialist. The coherent view of a single author who has been at the forefront of research in this exciting field, this volume is essential reading for anyone interested in our rapidly evolving understanding of the brain.
Author:
Publisher:
ISBN:
Category : Computers
Languages : en
Pages : 640
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Book Description
