Çoklu model dans performans analizi ile müzikle sürülen dans sentezinin yapılması

We present a framework for audio-visual analysis of dance performances towards the goalof music-driven dance synthesis. Dance ? gures, which are performed synchronously with themusical rhythm, can be analyzed through the audio spectra using spectral and chromaticmusical 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 trainhidden Markov model (HMM) structures to learn meter related temporal audio patternswhich are correlated with the dance ? gures. Bi-gram based co-occurences of temporal audiopatterns and dance ? gures are calculated. and bi-gram based co-occurrence performancesfor two different audio feature streams are evaluated. In our evaluations, mel-scale cepstralcoeffcients (MFCC) with their ? rst and second derivatives and chroma features are usedas our candidate audio feature set. The proposed framework in this thesis, can be usedtowards analysis and synthesis of audio-driven human body animation.