🚙 Multimodal Alcohol Impairment Classification in Drivers
Research on classifying driver impairment levels using multi-modal machine learning approaches.
Research on classifying driver impairment levels using multi-modal machine learning approaches.
Honors thesis research developing multimodal AI systems that understand emotional states in educational contexts.
Developing human-robot interaction systems for musical settings, focusing on nonverbal communication between musicians and robotic conducting agents.
Leading UC San Diego’s classical music organization as President, managing a 19-member board and organizing major events.
Published in EAI ArtsIT, 2024
This paper explores the integration of visual communication and musical interaction by implementing a robotic camera within a “Guided Harmony” musical game. We aim to examine co-creative behaviors between human musicians and robotic systems. Our research explores existing methodologies like improvisational game pieces and extends these concepts to include robotic participation using a PTZ camera. The robotic system interprets and responds to nonverbal cues from musicians, creating a collaborative and adaptive musical experience. This initial case study underscores the importance of intuitive visual communication channels. We also propose future research directions, including parameters for refining the visual cue toolkit and data collection methods to understand human-machine co-creativity further. Our findings contribute to the broader understanding of machine intelligence in augmenting human creativity, particularly in musical settings.
Recommended citation: Greer, R., Fleig, L., & Dubnov, S. (2024). Creativity and Visual Communication from Machine to Musician: Sharing a Score through a Robotic Camera. arXiv preprint arXiv:2409.05773.
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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