3 research outputs found

    Sensing and mapping for interactive performance

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    This paper describes a trans-domain mapping (TDM) framework for translating meaningful activities from one creative domain onto another. The multi-disciplinary framework is designed to facilitate an intuitive and non-intrusive interactive multimedia performance interface that offers the users or performers real-time control of multimedia events using their physical movements. It is intended to be a highly dynamic real-time performance tool, sensing and tracking activities and changes, in order to provide interactive multimedia performances. From a straightforward definition of the TDM framework, this paper reports several implementations and multi-disciplinary collaborative projects using the proposed framework, including a motion and colour-sensitive system, a sensor-based system for triggering musical events, and a distributed multimedia server for audio mapping of a real-time face tracker, and discusses different aspects of mapping strategies in their context. Plausible future directions, developments and exploration with the proposed framework, including stage augmenta tion, virtual and augmented reality, which involve sensing and mapping of physical and non-physical changes onto multimedia control events, are discussed

    A Motion Recognition Method for a Wearable Dancing Musical Instrument

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    Abstract In this paper, we constructed a system for realizing a new style of dance performance that dancers play music by dancing. From pilot study, we have found that the motion recognition for dance performance needed the synchronism to back ground music (BGM). Therefore, we propose a new motion recognition method specialized to dance performances. The key techniques of the proposed method are (1) adaptive decision of the size of recognition window to recognize a motion in sync with BGM, and (2) motion recognition in two-phase (rough and detailed) to fulfill the accuracy in high speed recognition. Data was recorded using a 3-axis wireless accelerometers mounted on both shoes. We evaluated the method on a dataset of 5 different dance steps (each repeated 100 times). The results show that this method is capable of improving recognition for all steps (in one case improving recognition from 62% to 99%) while retaining a feeling of seamless connection between movement and sound
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