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Computational analysis of sound scen...

Ellis, Dan.

Computational analysis of sound scenes and events

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Computational analysis of sound scenes and eventsedited by Tuomas Virtanen, Mark D. Plumbley, Dan Ellis.
其他作者:	Virtanen, Tuomas.
出版者:	Cham :Springer International Publishing :2018.
面頁冊數:	x, 422 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Signal processingDigital techniques.
電子資源:	http://dx.doi.org/10.1007/978-3-319-63450-0
ISBN:	9783319634500$q(electronic bk.)

Computational analysis of sound scenes and events
Computational analysis of sound scenes and events[electronic resource] /edited by Tuomas Virtanen, Mark D. Plumbley, Dan Ellis. - Cham :Springer International Publishing :2018. - x, 422 p. :ill., digital ;24 cm.

Introduction to sound scene and event analysis -- The Machine Learning Approach for Analysis of Sound Scenes and Events -- Acoustics and psychacoustics of sound scenes and events -- Acoustic features for environmental sound analysis -- Statistical Methods for Scene and Event Classification -- Datasets and evaluation -- Everyday Sound Categorization -- Approaches to complex sound scene analysis -- Multiview approaches to event detection and scene analysis -- Sound sharing and retrieval -- Computational bioacoustic scene analysis -- Audio Event Recognition in the Smart Home -- Sound Analysis in Smart Cities -- Future Perspective -- Index.

This book presents computational methods for extracting the useful information from audio signals, collecting the state of the art in the field of sound event and scene analysis. The authors cover the entire procedure for developing such methods, ranging from data acquisition and labeling, through the design of taxonomies used in the systems, to signal processing methods for feature extraction and machine learning methods for sound recognition. The book also covers advanced techniques for dealing with environmental variation and multiple overlapping sound sources, and taking advantage of multiple microphones or other modalities. The book gives examples of usage scenarios in large media databases, acoustic monitoring, bioacoustics, and context-aware devices. Graphical illustrations of sound signals and their spectrographic representations are presented, as well as block diagrams and pseudocode of algorithms. Gives an overview of methods for computational analysis of sounds scenes and events, allowing those new to the field to become fully informed; Covers all the aspects of the machine learning approach to computational analysis of sound scenes and events, ranging from data capture and labeling process to development of algorithms; Includes descriptions of algorithms accompanied by a website from which software implementations can be downloaded, facilitating practical interaction with the techniques.

ISBN: 9783319634500$q(electronic bk.)

Standard No.: 10.1007/978-3-319-63450-0doiSubjects--Topical Terms:

182126
Signal processing
--Digital techniques.

LC Class. No.: TK5102.9

Dewey Class. No.: 621.3822

Computational analysis of sound scenes and events
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505 0 $a Introduction to sound scene and event analysis -- The Machine Learning Approach for Analysis of Sound Scenes and Events -- Acoustics and psychacoustics of sound scenes and events -- Acoustic features for environmental sound analysis -- Statistical Methods for Scene and Event Classification -- Datasets and evaluation -- Everyday Sound Categorization -- Approaches to complex sound scene analysis -- Multiview approaches to event detection and scene analysis -- Sound sharing and retrieval -- Computational bioacoustic scene analysis -- Audio Event Recognition in the Smart Home -- Sound Analysis in Smart Cities -- Future Perspective -- Index.
520 $a This book presents computational methods for extracting the useful information from audio signals, collecting the state of the art in the field of sound event and scene analysis. The authors cover the entire procedure for developing such methods, ranging from data acquisition and labeling, through the design of taxonomies used in the systems, to signal processing methods for feature extraction and machine learning methods for sound recognition. The book also covers advanced techniques for dealing with environmental variation and multiple overlapping sound sources, and taking advantage of multiple microphones or other modalities. The book gives examples of usage scenarios in large media databases, acoustic monitoring, bioacoustics, and context-aware devices. Graphical illustrations of sound signals and their spectrographic representations are presented, as well as block diagrams and pseudocode of algorithms. Gives an overview of methods for computational analysis of sounds scenes and events, allowing those new to the field to become fully informed; Covers all the aspects of the machine learning approach to computational analysis of sound scenes and events, ranging from data capture and labeling process to development of algorithms; Includes descriptions of algorithms accompanied by a website from which software implementations can be downloaded, facilitating practical interaction with the techniques.
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