Music Similarity And Retrieval: An Introduction To Audio- And Web-based Strategies by Peter KneesMusic Similarity And Retrieval: An Introduction To Audio- And Web-based Strategies by Peter Knees

Music Similarity And Retrieval: An Introduction To Audio- And Web-based Strategies

byPeter Knees, Markus Schedl

Hardcover | June 3, 2016

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This book provides a summary of the manifold audio- and web-based approaches to music information retrieval (MIR) research. In contrast to other books dealing solely with music signal processing, it addresses additional cultural and listener-centric aspects and thus provides a more holistic view. Consequently, the text includes methods operating on features extracted directly from the audio signal, as well as methods operating on features extracted from contextual information, either the cultural context of music as represented on the web or the user and usage context of music.

Following the prevalent document-centered paradigm of information retrieval, the book addresses models of music similarity that extract computational features to describe an entity that represents music on any level (e.g., song, album, or artist), and methods to calculate the similarity between them. While this perspective and the representations discussed cannot describe all musical dimensions, they enable us to effectively find music of similar qualities by providing abstract summarizations of musical artifacts from different modalities.

The text at hand provides a comprehensive and accessible introduction to the topics of music search, retrieval, and recommendation from an academic perspective. It will not only allow those new to the field to quickly access MIR from an information retrieval point of view but also raise awareness for the developments of the music domain within the greater IR community. In this regard, Part I deals with content-based MIR, in particular the extraction of features from the music signal and similarity calculation for content-based retrieval. Part II subsequently addresses MIR methods that make use of the digitally accessible cultural context of music. Part III addresses methods of collaborative filtering and user-aware and multi-modal retrieval, while Part IV explores current and future applications of music retrieval and recommendation.>

Peter Knees holds a doctorate degree in computer science and is currently assistant professor of the Department of Computational Perception of the Johannes Kepler University Linz in Austria. For over a decade, he has been an active member of the music information retrieval research community, branching out to the related areas of multi...
Title:Music Similarity And Retrieval: An Introduction To Audio- And Web-based StrategiesFormat:HardcoverDimensions:299 pages, 23.5 × 15.5 × 0.03 inPublished:June 3, 2016Publisher:Springer-Verlag/Sci-Tech/TradeLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:3662497204

ISBN - 13:9783662497203


Table of Contents

1 Introduction to Music Similarity and Retrieval.- 2 Basic Methods of Audio Signal Processing.- 3 Audio Feature Extraction for Similarity Measurement.- 4 Semantic Labeling of Music.- 5 Contextual Music Meta-data: Comparison and Sources.- 6 Contextual Music Similarity, Indexing, and Retrieval.- 7 Listener-centered Data Sources and Aspects: Traces of Music Interaction.- 8 Collaborative Music Similarity and Recommendation.- 9 Applications.- 10 Grand Challenges and Outlook.- Appendix.

Editorial Reviews

"Knees and Schedl's book on Music similarity and retrieval differs from other books on the topic in that the authors have focused on music rather than acoustical signal processing, adding several cultural- and listener-centric aspects thereby rendering a holistic view. As a result, we find not only methods working on musical characteristics retrieved from the audio signal, but also techniques working on characteristics obtained from contextual information . Although not a textbook, I would definitely recommend it as handy reference material for music researchers, postgraduate students, and teachers of music or musicology." (Soubhik Chakraborty, Computing Reviews, February, 2017)