مكتبة جامعة عجلون الوطنية

فهرس بحث المكتبة

Deep and shallow : machine learning in music and audio / Shlomo Dubnov and Ross Greer.

By: Contributor(s): Material type: TextLanguage: English Series: CRC machine learning & pattern recognitionPublisher: Boca Raton : CRC Press, 2024Edition: First editionDescription: 1 online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781000984538
  • 9781003240198
Subject(s): Additional physical formats: Print version:: Deep and shallowDDC classification:
  • 780.285/63 23/eng/20230830
LOC classification:
  • ML74
Contents:
Introduction to Sounds of Music -- Noise : the Hidden Dynamics of Music -- Communicating Musical Information -- Understanding and (Re)Creating Sound -- Generating and Listening to Audio Information -- Artificial Musical Brains -- Representing Voices in Pitch and Time -- Noise Revisited : Brains that Imagine -- Paying (Musical) Attention -- Last Noisy Thoughts, Summary and Conclusion -- Appendix B. Summary of Programming Examples and Exercises -- Appendix C. Software Packages for Music and Audio Representation and Analysis -- Appendix D. Free Music and Audio editing software -- Appendix E. Datasets.
Summary: "Providing an essential and unique bridge between the theories of signal processing, machine learning and artificial intelligence (AI) in music, this book provides a holistic overview of foundational ideas in music, from the physical and mathematical properties of sound to symbolic representations. Combining signals and language models in one place, this book explores how sound may be represented and manipulated by computer systems, and how our devices may come to recognize particular sonic patterns as musically meaningful or creative through the lens of information theory. Introducing popular fundamental ideas in AI at a comfortable pace, more complex discussions around implementations and implications in musical creativity are gradually incorporated as the book progresses. Each chapter is accompanied by guided programming activities designed to familiarise readers with practical implications of discussed theory, without the frustrations of free-form coding. Surveying state of the art methods in applications of deep neural networks to audio and sound computing, as well as offering a research perspective that suggests future challenges in music and AI research, this book appeals to both students of AI and music, as well as industry professionals in the fields of machine learning, music and AI"-- Provided by publisher.
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Cover image Item type Current library Home library Collection Shelving location Call number Materials specified Vol info URL Copy number Status Notes Date due Barcode Item holds Item hold queue priority Course reserves
Books مكتبة جامعة عجلون الوطنية ML74 2024 (Browse shelf(Opens below)) Available E3966
Books مكتبة جامعة عجلون الوطنية ML74 2024 (Browse shelf(Opens below)) Available E3965

Includes bibliographical references and index.

Introduction to Sounds of Music -- Noise : the Hidden Dynamics of Music -- Communicating Musical Information -- Understanding and (Re)Creating Sound -- Generating and Listening to Audio Information -- Artificial Musical Brains -- Representing Voices in Pitch and Time -- Noise Revisited : Brains that Imagine -- Paying (Musical) Attention -- Last Noisy Thoughts, Summary and Conclusion -- Appendix B. Summary of Programming Examples and Exercises -- Appendix C. Software Packages for Music and Audio Representation and Analysis -- Appendix D. Free Music and Audio editing software -- Appendix E. Datasets.

"Providing an essential and unique bridge between the theories of signal processing, machine learning and artificial intelligence (AI) in music, this book provides a holistic overview of foundational ideas in music, from the physical and mathematical properties of sound to symbolic representations. Combining signals and language models in one place, this book explores how sound may be represented and manipulated by computer systems, and how our devices may come to recognize particular sonic patterns as musically meaningful or creative through the lens of information theory. Introducing popular fundamental ideas in AI at a comfortable pace, more complex discussions around implementations and implications in musical creativity are gradually incorporated as the book progresses. Each chapter is accompanied by guided programming activities designed to familiarise readers with practical implications of discussed theory, without the frustrations of free-form coding. Surveying state of the art methods in applications of deep neural networks to audio and sound computing, as well as offering a research perspective that suggests future challenges in music and AI research, this book appeals to both students of AI and music, as well as industry professionals in the fields of machine learning, music and AI"-- Provided by publisher.

Description based on print version record and CIP data provided by publisher.

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