000 04327nam a22005295i 4500
001 978-3-662-49722-7
003 DE-He213
005 20200420220216.0
007 cr nn 008mamaa
008 160528s2016 gw | s |||| 0|eng d
020 _a9783662497227
_9978-3-662-49722-7
024 7 _a10.1007/978-3-662-49722-7
_2doi
050 4 _aQA75.5-76.95
072 7 _aUNH
_2bicssc
072 7 _aUND
_2bicssc
072 7 _aCOM030000
_2bisacsh
082 0 4 _a025.04
_223
100 1 _aKnees, Peter.
_eauthor.
245 1 0 _aMusic Similarity and Retrieval
_h[electronic resource] :
_bAn Introduction to Audio- and Web-based Strategies /
_cby Peter Knees, Markus Schedl.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2016.
300 _aXX, 299 p. 82 illus., 47 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aThe Information Retrieval Series,
_x1387-5264 ;
_v36
505 0 _a1 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.
520 _aThis 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.>.
650 0 _aComputer science.
650 0 _aBig data.
650 0 _aInformation storage and retrieval.
650 0 _aApplication software.
650 1 4 _aComputer Science.
650 2 4 _aInformation Storage and Retrieval.
650 2 4 _aComputer Appl. in Arts and Humanities.
650 2 4 _aSignal, Image and Speech Processing.
650 2 4 _aBig Data/Analytics.
700 1 _aSchedl, Markus.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783662497203
830 0 _aThe Information Retrieval Series,
_x1387-5264 ;
_v36
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-662-49722-7
912 _aZDB-2-SCS
942 _cEBK
999 _c51631
_d51631