L'Arte Di Interazione Musicale: New Musical Possibilities Through Multimodal Techniques
Authors:   , Hochenbaum Jordan Natan 
Published: November 2021

<p>Multimodal communication is an essential aspect of human perception, facilitating the ability to reason, deduce, and understand meaning. Utilizing multimodal senses, humans are able to relate to the world in many different contexts. This dissertation looks at surrounding issues of multimodal communication as it pertains to human-computer interaction. If humans rely on multimodality to interact with the world, how can multimodality benefit the ways in which humans interface with computers? Can multimodality be used to help the machine understand more about the person operating it and what associations derive from this type of communication? This research places multimodality within the domain of musical performance, a creative field rich with nuanced physical and emotive aspects. This dissertation asks, what kinds of new sonic collaborations between musicians and computers are possible through the use of multimodal techniques? Are there specific performance areas where multimodal analysis and machine learning can benefit training musicians? In similar ways can multimodal interaction or analysis support new forms of creative processes? Applying multimodal techniques to music-computer interaction is a burgeoning effort. As such the scope of the research is to lay a foundation of multimodal techniques for the future. In doing so the first work presented is a software system for capturing synchronous multimodal data streams from nearly any musical instrument, interface, or sensor system. This dissertation also presents a variety of multimodal analysis scenarios for machine learning. This includes automatic performer recognition for both string and drum instrument players, to demonstrate the significance of multimodal musical analysis. Training the computer to recognize who is playing an instrument suggests important information is contained not only within the acoustic output of a performance, but also in the physical domain. Machine learning is also used to perform automatic drum-stroke identification; training the computer to recognize which hand a drummer uses to strike a drum. There are many applications for drum-stroke identification including more detailed automatic transcription, interactive training (e.g. computer-assisted rudiment practice), and enabling efficient analysis of drum performance for metrics tracking. Furthermore, this research also presents the use of multimodal techniques in the context of everyday practice. A practicing musician played a sensoraugmented instrument and recorded his practice over an extended period of time, realizing a corpus of metrics and visualizations from his performance. Additional multimodal metrics are discussed in the research, and demonstrate new types of performance statistics obtainable from a multimodal approach. The primary contributions of this work include (1) a new software tool enabling musicians, researchers, and educators to easily capture multimodal information from nearly any musical instrument or sensor system; (2) investigating multimodal machine learning for automatic performer recognition of both string players and percussionists; (3) multimodal machine learning for automatic drum-stroke identification; (4a) applying multimodal techniques to musical pedagogy and training scenarios; (4b) investigating novel multimodal metrics; (5) lastly this research investigates the possibilities, affordances, and design considerations of multimodal musicianship both in the acoustic domain, as well as in other musical interface scenarios. This work provides a foundation from which engaging musical-computer interactions can occur in the future, benefitting from the unique nuances of multimodal techniques.</p>