151. Modeling and segmentation of audio descriptor profiles with segmental models
- Author
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Nicolas Rasamimanana, Frédéric Bevilacqua, Julien Bloit, Equipe Interactions musicales temps-réel, Sciences et Technologies de la Musique et du Son (STMS), Institut de Recherche et Coordination Acoustique/Musique (IRCAM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche et Coordination Acoustique/Musique (IRCAM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), and Analyse et synthèse sonores [Paris]
- Subjects
Computer science ,Speech recognition ,Feature extraction ,[INFO.INFO-OH]Computer Science [cs]/Other [cs.OH] ,Context (language use) ,Musical instrument ,Musical ,computer.software_genre ,050105 experimental psychology ,060404 music ,Glissando ,[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] ,Violin ,[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing ,[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] ,Artificial Intelligence ,0501 psychology and cognitive sciences ,Segmentation ,music ,[INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC] ,Audio signal processing ,Profiles ,Hidden Markov Models ,Primitives ,segmental models ,[SPI.ACOU]Engineering Sciences [physics]/Acoustics [physics.class-ph] ,Signal processing ,[SHS.MUSIQ]Humanities and Social Sciences/Musicology and performing arts ,[SCCO.NEUR]Cognitive science/Neuroscience ,05 social sciences ,segmentation ,[INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM] ,[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] ,06 humanities and the arts ,[INFO.INFO-IA]Computer Science [cs]/Computer Aided Engineering ,audio descriptors ,Dynamics (music) ,Signal Processing ,[INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD] ,Computer Vision and Pattern Recognition ,computer ,[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing ,0604 arts ,Software - Abstract
cote interne IRCAM: Bloit10a; None / None; National audience; We present a method to model sound descriptor temporal profiles using Segmental Models. Unlike standard HMM, such an approach allows for the modeling of fine structures of temporal profiles with a reduced number of states. These states, we called primitives, can be chosen by the user using prior knowledge, and assembled to model symbolic musical elements. In this paper, we describe this general methodology and evaluate it on a dataset made of of violin recording containing crescendo/decrescendo, glissando and sforzando. The results show that, in this context, the segmental model can segment and recognize these different musical elements with a satisfactory level.
- Published
- 2010
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