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Doctor of Philosophy with a Major in Music Technology

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    Machine Learning Driven Emotional Musical Prosody for Human-Robot Interaction
    (Georgia Institute of Technology, 2021-11-18) Savery, Richard
    This dissertation presents a method for non-anthropomorphic human-robot interaction using a newly developed concept entitled Emotional Musical Prosody (EMP). EMP consists of short expressive musical phrases capable of conveying emotions, which can be embedded in robots to accompany mechanical gestures. The main objective of EMP is to improve human engagement with, and trust in robots while avoiding the uncanny valley. We contend that music - one of the most emotionally meaningful human experiences - can serve as an effective medium to support human-robot engagement and trust. EMP allows for the development of personable, emotion-driven agents, capable of giving subtle cues to collaborators while presenting a sense of autonomy. We present four research areas aimed at developing and understanding the potential role of EMP in human-robot interaction. The first research area focuses on collecting and labeling a new EMP dataset from vocalists, and using this dataset to generate prosodic emotional phrases through deep learning methods. Through extensive listening tests, the collected dataset and generated phrases were validated with a high level of accuracy by a large subject pool. The second research effort focuses on understanding the effect of EMP in human-robot interaction with industrial and humanoid robots. Here, significant results were found for improved trust, perceived intelligence, and likeability of EMP enabled robotic arms, but not for humanoid robots. We also found significant results for improved trust in a social robot, as well as perceived intelligence, creativity and likeability in a robotic musician. The third and fourth research areas shift to broader use cases and potential methods to use EMP in HRI. The third research area explores the effect of robotic EMP on different personality types focusing on extraversion and neuroticism. For robots, personality traits offer a unique way to implement custom responses, individualized to human collaborators. We discovered that humans prefer robots with emotional responses based on high extraversion and low neuroticism, with some correlation between the humans collaborator’s own personality traits. The fourth and final research question focused on scaling up EMP to support interaction between groups of robots and humans. Here, we found that improvements in trust and likeability carried across from single robots to groups of industrial arms. Overall, the thesis suggests EMP is useful for improving trust and likeability for industrial, social and robot musicians but not in humanoid robots. The thesis bears future implications for HRI designers, showing the extensive potential of careful audio design, and the wide range of outcomes audio can have on HRI.
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    Towards an embodied musical mind: Generative algorithms for robotic musicians
    (Georgia Institute of Technology, 2017-04-19) Bretan, Peter Mason
    Embodied cognition is a theory stating that the processes and functions comprising the human mind are influenced by a person's physical body. The theory of embodied musical cognition holds that a person's body largely influences his or her musical experiences and actions. This work presents multiple frameworks for computer music generation as it pertains to robotic musicianship such that the musical decisions result from a joint optimization between the robot's physical constraints and musical knowledge. First, a generative framework based on hand-designed higher level musical concepts and the Viterbi beam search algorithm is described. The system allows for efficient and autonomous exploration on the relationship between music and physicality and the resulting music that is contingent on such a connection. It is evaluated objectively based on its ability to plan a series of sound actuating robotic movements (path planning) that minimize risk of collision, the number of dropped notes, spurious movements, and energy expenditure. Second, a method for developing higher level musical concepts (semantics) based on machine learning is presented. Using strategies based on neural networks and deep learning we show that it is possible to learn perceptually meaningful higher-level representations of music. These learned musical ``embeddings'' are applied to an autonomous music generation system that utilizes unit selection. The embeddings and generative system are evaluated based on objective ranking tasks and a subjective listening study. Third, the method for learning musical semantics is extended to a robot such that its embodiment becomes integral to the learning process. The resulting embeddings simultaneously encode information describing both important musical features and the robot's physical constraints.