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Exploring Music Contents

Overview of attention for book
Cover of 'Exploring Music Contents'

Table of Contents

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    Book Overview
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    Chapter 1 Probabilistic and Logic-Based Modelling of Harmony
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    Chapter 2 Interactive Music Applications and Standards
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    Chapter 3 Interactive Music with Active Audio CDs
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    Chapter 4 Pitch Gestures in Generative Modeling of Music
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    Chapter 5 An Entropy Based Method for Local Time-Adaptation of the Spectrogram
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    Chapter 6 Transcription of Musical Audio Using Poisson Point Processes and Sequential MCMC
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    Chapter 7 Exploring Music Contents
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    Chapter 8 Notes on Nonnegative Tensor Factorization of the Spectrogram for Audio Source Separation: Statistical Insights and Towards Self-Clustering of the Spatial Cues
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    Chapter 9 What Signal Processing Can Do for the Music
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    Chapter 10 Speech/Music Discrimination in Audio Podcast Using Structural Segmentation and Timbre Recognition
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    Chapter 11 Computer Music Cloud
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    Chapter 12 Abstract Sounds and Their Applications in Audio and Perception Research
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    Chapter 13 Pattern Induction and Matching in Music Signals
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    Chapter 14 Unsupervised Analysis and Generation of Audio Percussion Sequences
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    Chapter 15 Identifying Attack Articulations in Classical Guitar
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    Chapter 16 Comparing Approaches to the Similarity of Musical Chord Sequences
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    Chapter 17 Songs2See and GlobalMusic2One: Two Applied Research Projects in Music Information Retrieval at Fraunhofer IDMT
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    Chapter 18 MusicGalaxy: A Multi-focus Zoomable Interface for Multi-facet Exploration of Music Collections
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    Chapter 19 A Database Approach to Symbolic Music Content Management
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    Chapter 20 Error-Tolerant Content-Based Music-Retrieval with Mathematical Morphology
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    Chapter 21 Melodic Similarity through Shape Similarity
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    Chapter 22 Content-Based Music Discovery
Overall attention for this book and its chapters
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Exploring Music Contents
Published by
Springer Berlin Heidelberg, September 2011
DOI 10.1007/978-3-642-23126-1
978-3-64-223125-4, 978-3-64-223126-1

Ystad, Sølvi, Aramaki, Mitsuko, Kronland-Martinet, Richard, Jensen, Kristoffer, Ystad, Sølvi, Aramaki, Mitsuko, Kronland-Martinet, Richard, Jensen, Kristoffer

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 9%
Unknown 10 91%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 3 27%
Researcher 2 18%
Student > Master 2 18%
Student > Ph. D. Student 2 18%
Professor 1 9%
Other 1 9%
Readers by discipline Count As %
Arts and Humanities 3 27%
Engineering 3 27%
Social Sciences 2 18%
Physics and Astronomy 1 9%
Unspecified 1 9%
Other 1 9%