Music Similarity and Retrieval
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 multimedia, text IR, recommender systems, and digital media arts. Markus Schedl is an associate professor of the Johannes Kepler University Linz / Department of Computational Perception. His main research interests include music and multimedia information retrieval, web and social media mining, and recommender systems. In addition to regularly publishing in and offering scientific services to top-tier conferences and journals of these fields, he is associate editor of the Springer International Journal of Multimedia Information Retrieval. He is also a keen lecturer and taught classes at the Universitat Pompeu Fabra Barcelona, Queen Mary University London, and Kungliga Tekniska Högskolan Stockholm, among others.
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