Analysing Gesture and Sound Similarities with a HMM-based Divergence Measure
Résumé
In this paper we propose a divergence measure which is applied to the analysis of the relationships between gesture and sound. Technically, the divergence measure is defined based on a Hidden Markov Model (HMM) that is used to model the time profile of sound descriptors. We show that the divergence has the following properties: non- negativity, global minimum and non-symmetry. Particularly, we used this divergence to analyze the results of experiments where participants were asked to perform physical gestures while listening to specific sounds. We found that the proposed divergence is able to measure global and local differences in either time alignment or amplitude between gesture and sound descriptors.
Domaines
Son [cs.SD] Interface homme-machine [cs.HC] Musique, musicologie et arts de la scène Traitement du signal et de l'image [eess.SP] Apprentissage [cs.LG] Intelligence artificielle [cs.AI] Ingénierie assistée par ordinateur Multimédia [cs.MM] Vision par ordinateur et reconnaissance de formes [cs.CV] Autre [cs.OH] Traitement du signal et de l'image [eess.SP]| Origine | Fichiers produits par l'(les) auteur(s) |
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