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008			160528s2016 gw | s |||| 0|eng d
020			_a9783662497227
040			_aES-MaUEC
050		4	_aZA3075 _b.K544 2016
082	0	4	_a025.04
100	1		_aKnees, Peter _0Local _9100310
245	1	0	_aMusic Similarity and Retrieval : _bAn Introduction to Audio- and Web-based Strategies _cby Peter Knees, Markus Schedl
260			_aBerlin, Heidelberg _bSpringer Berlin Heidelberg _c2016
300			_a1 recurso en línea (XX, 299 p.) _b82 ilustraciones, 47 ilustraciones en color
336			_aTexto (visual) _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
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.>.
710	2		_aSpringerLink (Online service) _0Local _9106996
942			_2lcc _cLE
988	0	0	_aEBOOK, EBSPRINGER
650	0	7	_aData mining _0LocalX _2embne _9162648
650	0	7	_aRecuperación de la información _0LocalX _2embne _9147823
650	0	7	_aInformática _0LocalX _2embne _9139268
700	1		_aSchedl, Markus _0Local _9100311
830		0	_aThe Information Retrieval Series _x1387-5264 _v36 _9134307 _0Local
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-662-49722-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901			_ai9783662497227
907			_a.b12958050 _b10-10-17 _c21-11-16
998			_am _a_alco _a_vill _b - - _cm _dz _e- _feng _ggw _h0
945			_aZA3075 .K544 2016 EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i11599765 _z06-04-17
999			_c86527 _d86527 _x1