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This paper presents a method for mapping embodied gesture , acquired with electromyography and motion sensing, to a corpus of small sound units, organised by derived timbral features using concatenative synthesis. Gestures and sounds can be associated directly using individual units and static poses, or by using a sound tracing method that leverage...
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Context 1
... playback, the amplitude and panning of the output is controlled by the "Amplitude Panner" (Fig. 5, upper right panel). The EMG sensors are divided into two groups and their amplitude envelopes are summed. The sum of each group is used to control the overall amplitude of the audio output in the left and right channels, respectively. When there is no muscular activation, both channels have near zero gain, giving the performer a natural method to make ...
Context 2
... this way, the corpus was enriched in a way that was directly related to the sound design of the original stimulus but had a greater diversity of timbral features, creating potential for more expressive variation in performance. 5 shows the controls to interact with our system. At the beginning of a session, a performer activates the sensor armband using the toggle in the first column. ...
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Citations
The application of Artifcial Intelligence (AI) across a wide range of domains comes with both high expectations of its benefts and dire predictions of misuse. While AI systems have largely been driven by a technology-centered design approach, the potential societal conse- quences of AI have mobilized both HCI and AI researchers towards researching human-centered artifcial intelligence (HCAI). How- ever, there remains considerable ambiguity about what it means to frame, design and evaluate HCAI. This paper presents a critical review of the large corpus of peer-reviewed literature emerging on HCAI in order to characterize what the community is defning as HCAI. Our review contributes an overview and map of HCAI research based on work that explicitly mentions the terms ‘human- centered artifcial intelligence’ or ‘human-centered machine learn- ing’ or their variations, and suggests future challenges and research directions. The map reveals the breadth of research happening in HCAI, established clusters and the emerging areas of Interaction with AI and Ethical AI. The paper contributes a new defnition of HCAI, and calls for greater collaboration between AI and HCI research, and new HCAI constructs.
