Similarity Search and Applications, Amsaleg

Автор: Knees
Название: Music Similarity and Retrieval
ISBN: 3662497204 ISBN-13(EAN): 9783662497203
Издательство: Springer
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Цена: 15372.00 р.
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Описание: This book providesa summary of the manifold audio- and web-based approaches to music informationretrieval (MIR) research. In contrast to other books dealing solely with musicsignal processing, it addresses additional cultural and listener-centric aspectsand thus provides a more holistic view. Consequently, the text includes methodsoperating on features extracted directly from the audio signal, as well asmethods operating on features extracted from contextual information, either thecultural context of music as represented on the web or the user and usagecontext of music.Followingthe prevalent document-centered paradigm of information retrieval, the bookaddresses models of music similarity that extract computational features todescribe an entity that represents music on any level (e.g., song, album, orartist), and methods to calculate the similarity between them. While thisperspective and the representations discussed cannot describe all musicaldimensions, they enable us to effectively find music of similar qualities byproviding abstract summarizations of musical artifacts from differentmodalities. The text athand provides a comprehensive and accessible introduction to the topics ofmusic search, retrieval, and recommendation from an academic perspective. Itwill not only allow those new to the field to quickly access MIR from aninformation retrieval point of view but also raise awareness for thedevelopments of the music domain within the greater IR community. In thisregard, Part I deals with content-based MIR, in particular the extraction offeatures from the music signal and similarity calculation for content-basedretrieval. Part II subsequently addresses MIR methods that make use of thedigitally accessible cultural context of music. Part III addresses methods of collaborativefiltering and user-aware and multi-modal retrieval, while Part IV explorescurrent and future applications of music retrieval and recommendation.>