03991nam 22007575 450 991029996030332120200702232627.03-319-70609-810.1007/978-3-319-70609-2(CKB)4340000000223546(DE-He213)978-3-319-70609-2(MiAaPQ)EBC6298184(MiAaPQ)EBC5590649(Au-PeEL)EBL5590649(OCoLC)1066192745(PPN)221251995(EXLCZ)99434000000022354620171104d2018 u| 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierFrom Content-based Music Emotion Recognition to Emotion Maps of Musical Pieces /by Jacek Grekow1st ed. 2018.Cham :Springer International Publishing :Imprint: Springer,2018.1 online resource (XIV, 138 p. 71 illus., 22 illus. in color.) Studies in Computational Intelligence,1860-949X ;7473-319-70608-X Includes bibliographical references and index.Introduction -- Representations of Emotions -- Human Annotation -- MIDI Features -- Hierarchical Emotion Detection in MIDI Files.The 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.Studies in Computational Intelligence,1860-949X ;747Computational intelligenceMusicAcoustical engineeringEmotionsPattern recognitionAcousticsComputational Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/T11014Musichttps://scigraph.springernature.com/ontologies/product-market-codes/417000Engineering Acousticshttps://scigraph.springernature.com/ontologies/product-market-codes/T16000Emotionhttps://scigraph.springernature.com/ontologies/product-market-codes/Y20140Pattern Recognitionhttps://scigraph.springernature.com/ontologies/product-market-codes/I2203XAcousticshttps://scigraph.springernature.com/ontologies/product-market-codes/P21069Computational intelligence.Music.Acoustical engineering.Emotions.Pattern recognition.Acoustics.Computational Intelligence.Music.Engineering Acoustics.Emotion.Pattern Recognition.Acoustics.780.285Grekow Jacekauthttp://id.loc.gov/vocabulary/relators/aut1063953MiAaPQMiAaPQMiAaPQBOOK9910299960303321From Content-based Music Emotion Recognition to Emotion Maps of Musical Pieces2535461UNINA