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010   $a3-319-70609-8
024 7 $a10.1007/978-3-319-70609-2
035   $a(CKB)4340000000223546
035   $a(DE-He213)978-3-319-70609-2
035   $a(MiAaPQ)EBC6298184
035   $a(MiAaPQ)EBC5590649
035   $a(Au-PeEL)EBL5590649
035   $a(OCoLC)1066192745
035   $a(PPN)221251995
035   $a(EXLCZ)994340000000223546
100   $a20171104d2018      u|         0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aFrom Content-based Music Emotion Recognition to Emotion Maps of Musical Pieces /$fby Jacek Grekow
205   $a1st ed. 2018.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018.
215   $a1 online resource (XIV, 138 p. 71 illus., 22 illus. in color.) 
225 1 $aStudies in Computational Intelligence,$x1860-949X ;$v747
311   $a3-319-70608-X 
320   $aIncludes bibliographical references and index.
327   $aIntroduction -- Representations of Emotions -- Human Annotation -- MIDI Features -- Hierarchical Emotion Detection in MIDI Files.
330   $aThe problems it addresses include emotion representation, annotation of music excerpts, feature extraction, and machine learning. The book chiefly focuses on content-based analysis of music files, a system that automatically analyzes the structures of a music file and annotates the file with the perceived emotions. Further, it explores emotion detection in MIDI and audio files. In the experiments presented here, the categorical and dimensional approaches were used, and the knowledge and expertise of music experts with a university music education were used for music file annotation. The automatic emotion detection systems constructed and described in the book make it possible to index and subsequently search through music databases according to emotion. In turn, the emotion maps of musical compositions provide valuable new insights into the distribution of emotions in music and can be used to compare that distribution in different compositions, or to conduct emotional comparisons of different interpretations of the same composition.
410  0$aStudies in Computational Intelligence,$x1860-949X ;$v747
606   $aComputational intelligence
606   $aMusic
606   $aAcoustical engineering
606   $aEmotions
606   $aPattern recognition
606   $aAcoustics
606   $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014
606   $aMusic$3https://scigraph.springernature.com/ontologies/product-market-codes/417000
606   $aEngineering Acoustics$3https://scigraph.springernature.com/ontologies/product-market-codes/T16000
606   $aEmotion$3https://scigraph.springernature.com/ontologies/product-market-codes/Y20140
606   $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X
606   $aAcoustics$3https://scigraph.springernature.com/ontologies/product-market-codes/P21069
615  0$aComputational intelligence.
615  0$aMusic.
615  0$aAcoustical engineering.
615  0$aEmotions.
615  0$aPattern recognition.
615  0$aAcoustics.
615 14$aComputational Intelligence.
615 24$aMusic.
615 24$aEngineering Acoustics.
615 24$aEmotion.
615 24$aPattern Recognition.
615 24$aAcoustics.
676   $a780.285
700   $aGrekow$b Jacek$4aut$4http://id.loc.gov/vocabulary/relators/aut$01063953
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910299960303321
996   $aFrom Content-based Music Emotion Recognition to Emotion Maps of Musical Pieces$92535461
997   $aUNINA