000 03862nam a22006375i 4500
001 978-3-319-70609-2
003 DE-He213
005 20210201191507.0
007 cr nn 008mamaa
008 171104s2018 gw | s |||| 0|eng d
020 _a9783319706092
_9978-3-319-70609-2
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aTEC009000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aGrekow, Jacek.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aFrom Content-based Music Emotion Recognition to Emotion Maps of Musical Pieces
_h[electronic resource] /
_cby Jacek Grekow.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aXIV, 138 p. 71 illus., 22 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 _aStudies in Computational Intelligence,
_x1860-949X ;
_v747
500 _aAcceso multiusuario
505 0 _aIntroduction -- Representations of Emotions -- Human Annotation -- MIDI Features -- Hierarchical Emotion Detection in MIDI Files.
520 _aThe problems it addresses include emotion representation, annotation of music excerpts, feature extraction, and machine learning. The book chiefly focuses on content-based analysis of music files, a system that automatically analyzes the structures of a music file and annotates the file with the perceived emotions. Further, it explores emotion detection in MIDI and audio files. In the experiments presented here, the categorical and dimensional approaches were used, and the knowledge and expertise of music experts with a university music education were used for music file annotation. The automatic emotion detection systems constructed and described in the book make it possible to index and subsequently search through music databases according to emotion. In turn, the emotion maps of musical compositions provide valuable new insights into the distribution of emotions in music and can be used to compare that distribution in different compositions, or to conduct emotional comparisons of different interpretations of the same composition.
541 _fUABC ;
_cTemporal ;
_d01/01/2021-12/31/2023.
650 0 _aComputational intelligence.
650 0 _aMusic.
650 0 _aAcoustical engineering.
650 0 _aEmotions.
650 0 _aPattern recognition.
650 0 _aAcoustics.
650 1 4 _aComputational Intelligence.
_0https://scigraph.springernature.com/ontologies/product-market-codes/T11014
650 2 4 _aMusic.
_0https://scigraph.springernature.com/ontologies/product-market-codes/417000
650 2 4 _aEngineering Acoustics.
_0https://scigraph.springernature.com/ontologies/product-market-codes/T16000
650 2 4 _aEmotion.
_0https://scigraph.springernature.com/ontologies/product-market-codes/Y20140
650 2 4 _aPattern Recognition.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I2203X
650 2 4 _aAcoustics.
_0https://scigraph.springernature.com/ontologies/product-market-codes/P21069
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319706085
776 0 8 _iPrinted edition:
_z9783319706108
776 0 8 _iPrinted edition:
_z9783319889689
830 0 _aStudies in Computational Intelligence,
_x1860-949X ;
_v747
856 4 0 _zLibro electrónico
_uhttp://148.231.10.114:2048/login?url=https://doi.org/10.1007/978-3-319-70609-2
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cLIBRO_ELEC
999 _c244096
_d244095