We present a framework for audio-visual analysis of dance performances towards the goal of music-driven dance synthesis. Dance figures, which are performed synchronously with the musical rhythm, can be analyzed through the audio spectra using spectral and chromatic musical features. In the proposed multimodal dance performance analysis system, dance figures are manually labeled over the video stream and modeled by employing HMMs. The music segments, which correspond to beat and meter boundaries, are used to train hidden Markov model (HMM) structures to learn meter related temporal audio patterns which are correlated with the dance figures. Bi-gram based co-occurences of temporal audio patterns and dance figures are calculated. and bi-gram based co-occurrence performances for two different audio feature streams are evaluated. In our evaluations, mel-scale cepstral coefficients (MFCC) with their first and second derivatives and chroma features are used as our candidate audio feature set. The proposed framework in this thesis, can be used towards analysis and synthesis of audio-driven human body animation.
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YASEMIN DEMIR was born in Turkey on May 21, 1984. She received her B.Sc.degree in Telecommunications Engineering from Istanbul Technical University, Istanbul,Turkey, in 2006. From August 2006 to July 2008, she worked as a teaching and research assistant in Koc University, Istanbul, Turkey. At Koc University, she focused on Signal Processing.
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -We present a framework for audio-visual analysis of dance performances towards the goal of music-driven dance synthesis. Dance gures, which are performed synchronously with the musical rhythm, can be analyzed through the audio spectra using spectral and chromatic musical features. In the proposed multimodal dance performance analysis system, dance gures are manually labeled over the video stream and modeled by employing HMMs. The music segments, which correspond to beat and meter boundaries, are used to train hidden Markov model (HMM) structures to learn meter related temporal audio patterns which are correlated with the dance gures. Bi-gram based co-occurences of temporal audio patterns and dance gures are calculated. and bi-gram based co-occurrence performances for two di erent audio feature streams are evaluated. In our evaluations, mel-scale cepstral coe cients (MFCC) with their rst and second derivatives and chroma features are used as our candidate audio feature set. The proposed framework in this thesis, can be used towards analysis and synthesis of audio-driven human body animation. 64 pp. Englisch. Bestandsnummer des Verkäufers 9783846549902
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - We present a framework for audio-visual analysis of dance performances towards the goal of music-driven dance synthesis. Dance gures, which are performed synchronously with the musical rhythm, can be analyzed through the audio spectra using spectral and chromatic musical features. In the proposed multimodal dance performance analysis system, dance gures are manually labeled over the video stream and modeled by employing HMMs. The music segments, which correspond to beat and meter boundaries, are used to train hidden Markov model (HMM) structures to learn meter related temporal audio patterns which are correlated with the dance gures. Bi-gram based co-occurences of temporal audio patterns and dance gures are calculated. and bi-gram based co-occurrence performances for two di erent audio feature streams are evaluated. In our evaluations, mel-scale cepstral coe cients (MFCC) with their rst and second derivatives and chroma features are used as our candidate audio feature set. The proposed framework in this thesis, can be used towards analysis and synthesis of audio-driven human body animation. Bestandsnummer des Verkäufers 9783846549902
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Demir YaseminYASEMIN DEMIR was born in Turkey on May 21, 1984. She received her B.Sc.degree in Telecommunications Engineering from Istanbul Technical University, Istanbul,Turkey, in 2006. From August 2006 to July 2008, she worked as . Bestandsnummer des Verkäufers 5498392
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -We present a framework for audio-visual analysis of dance performances towards the goal of music-driven dance synthesis. Dance ¿gures, which are performed synchronously with the musical rhythm, can be analyzed through the audio spectra using spectral and chromatic musical features. In the proposed multimodal dance performance analysis system, dance ¿gures are manually labeled over the video stream and modeled by employing HMMs. The music segments, which correspond to beat and meter boundaries, are used to train hidden Markov model (HMM) structures to learn meter related temporal audio patterns which are correlated with the dance ¿gures. Bi-gram based co-occurences of temporal audio patterns and dance ¿gures are calculated. and bi-gram based co-occurrence performances for two di¿erent audio feature streams are evaluated. In our evaluations, mel-scale cepstral coe¿cients (MFCC) with their ¿rst and second derivatives and chroma features are used as our candidate audio feature set. The proposed framework in this thesis, can be used towards analysis and synthesis of audio-driven human body animation.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 64 pp. Englisch. Bestandsnummer des Verkäufers 9783846549902
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