000 | 03862nam a22006375i 4500 | ||
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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 |
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050 | 4 | _aQ342 | |
072 | 7 |
_aUYQ _2bicssc |
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072 | 7 |
_aTEC009000 _2bisacsh |
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072 | 7 |
_aUYQ _2thema |
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082 | 0 | 4 |
_a006.3 _223 |
100 | 1 |
_aGrekow, Jacek. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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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. |
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300 |
_aXIV, 138 p. 71 illus., 22 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aStudies in Computational Intelligence, _x1860-949X ; _v747 |
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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. |
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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 |