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Digital signal processing in audio and acoustical engineering

Author: Francis F Li; Trevor J Cox
Publisher: Boca Raton, FL : CRC Press, [2019]
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
Starting with essential maths, fundamentals of signals and systems, and classical concepts of DSP, this book presents, from an application-oriented perspective, modern concepts and methods of DSP including machine learning for audio acoustics and engineering. Content highlights include but are not limited to room acoustic parameter measurements, filter design, codecs, machine learning for audio pattern recognition  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Li, Francis F.
Digital signal processing in audio and acoustical engineering.
Boca Raton : Taylor & Francis, a CRC title, part of the Taylor & Francis imprint, a member of the Taylor & Francis Group, the academic division of T & F Informa, plc, [2019]
(DLC) 2018037679
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Francis F Li; Trevor J Cox
ISBN: 9781315117881 1315117886 9781466593893 146659389X 9781351634663 1351634666 9781351644150 1351644157
OCLC Number: 1048660390
Description: 1 online resource
Contents: Acoustic signals and audio systems --
Sampling quantization and discrete fourier --
DSP in acoustical transfer function measurements --
Digital filter and z-transform --
Audio codecs --
DSP in binaural hearing and microphone arrays --
Adaptive filters --
Machine learning in acoustic DSP --
Unsupervised learning and blind source separation --
DSP in hearing aids.
Responsibility: Francis F. Li and Trevor J. Cox.

Abstract:

Starting with essential maths, fundamentals of signals and systems, and classical concepts of DSP, this book presents, from an application-oriented perspective, modern concepts and methods of DSP including machine learning for audio acoustics and engineering. Content highlights include but are not limited to room acoustic parameter measurements, filter design, codecs, machine learning for audio pattern recognition and machine audition, spatial audio, array technologies and hearing aids. Some research outcomes are fed into book as worked examples. As a research informed text, the book attempts to present DSP and machine learning from a new and more relevant angle to acousticians and audio engineers. Some MATLAB codes or frameworks of algorithms are given as downloads available on the CRC Press website. Suggested exploration and mini project ideas are given for "proof of concept" type of exercises and directions for further study and investigation. The book is intended for researchers, professionals, and senior year students in the field of audio acoustics.

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