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Computer Music Modeling and Retrieval. Sense of Sounds

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Cover of 'Computer Music Modeling and Retrieval. Sense of Sounds'

Table of Contents

  1. Altmetric Badge
    Book Overview
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    Chapter 1 Fifty Years of Computer Music: Ideas of the Past Speak to the Future
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    Chapter 2 Music Cognition: Learning, Perception, Expectations
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    Chapter 3 Capturing Expressive and Indicative Qualities of Conducting Gesture: An Application of Temporal Expectancy Models
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    Chapter 4 Musicians Outperform Nonmusicians in Speech Imitation
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    Chapter 5 Cognitive Styles and Computer-Based Creativity Support Systems: Two Linked Studies of Electro-acoustic Music Composers
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    Chapter 6 The Usability of Music Theory Software: The Analysis of Twelve-Tone Music as a Case Study
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    Chapter 7 Understanding Emotion in Raag : An Empirical Study of Listener Responses
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    Chapter 8 The Artistic Play of Spatial Organization: Spatial Attributes, Scene Analysis and Auditory Spatial Schemata
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    Chapter 9 Semiotics of Sounds Evoking Motions: Categorization and Acoustic Features
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    Chapter 10 Exploring Perceptual Based Timbre Feature for Singer Identification
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    Chapter 11 Cognitive Implications of Musical Perception
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    Chapter 12 A Meta-analysis of Timbre Perception Using Nonlinear Extensions to CLASCAL
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    Chapter 13 Real-Time Analysis of Sensory Dissonance
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    Chapter 14 Multimodal Design for Enactive Toys
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    Chapter 15 Psychoacoustic Manipulation of the Sound-Induced Illusory Flash
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    Chapter 16 On Cross-Modal Perception of Musical Tempo and the Speed of Human Movement
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    Chapter 17 Between Mapping, Sonification and Composition: Responsive Audio Environments in Live Performance
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    Chapter 18 Retrieving and Recreating Musical Form
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    Chapter 19 Placement of Sound Sources in the Stereo Field Using Measured Room Impulse Responses
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    Chapter 20 Rule-Based Expressive Modifications of Tempo in Polyphonic Audio Recordings
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    Chapter 21 Exploring the Perceptual Relevance of Inherent Variability of Drum Sounds
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    Chapter 22 Improving Musical Expressiveness by Time-Varying Brightness Shaping
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    Chapter 23 NN Music: Improvising with a ‘Living’ Computer
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    Chapter 24 A Real-Time Genetic Algorithm in Human-Robot Musical Improvisation
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    Chapter 25 A Musical Framework with Swarming Robots
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    Chapter 26 Emergent Rhythms through Multi-agency in Max/MSP
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    Chapter 27 Experiencing Audio and Music in a Fully Immersive Environment
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    Chapter 28 A Network-Based Framework for Collaborative Development and Performance of Digital Musical Instruments
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    Chapter 29 The ImmApp: A Digital Application for Immersive Interaction with Sound Art Archives
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    Chapter 30 BioTools: A Biosignal Toolbox for Composers and Performers
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    Chapter 31 Focus-Plus-Context Audio Interaction Design
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    Chapter 32 Maps and Legends : Designing FPS-Based Interfaces for Multi-user Composition, Improvisation and Immersive Performance
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    Chapter 33 DECONcert: Making Waves with Water, EEG, and Music
Attention for Chapter 3: Capturing Expressive and Indicative Qualities of Conducting Gesture: An Application of Temporal Expectancy Models
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Chapter title
Capturing Expressive and Indicative Qualities of Conducting Gesture: An Application of Temporal Expectancy Models
Chapter number 3
Book title
Computer Music Modeling and Retrieval. Sense of Sounds
Published by
Springer, Berlin, Heidelberg, August 2007
DOI 10.1007/978-3-540-85035-9_3
Book ISBNs
978-3-54-085034-2, 978-3-54-085035-9
Authors

Dilip Swaminathan, Harvey Thornburg, Todd Ingalls, Stjepan Rajko, Jodi James, Ellen Campana, Kathleya Afanador, Randal Leistikow

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 5%
France 1 5%
Canada 1 5%
Unknown 16 84%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 42%
Researcher 5 26%
Professor > Associate Professor 2 11%
Student > Bachelor 1 5%
Other 1 5%
Other 2 11%
Readers by discipline Count As %
Computer Science 12 63%
Engineering 2 11%
Arts and Humanities 1 5%
Psychology 1 5%
Mathematics 1 5%
Other 2 11%