A Novel Interface for the Graphical Analysis of Music Practice Behaviors

Practice is an essential part of music training, but critical content-based analyses of practice behaviors still lack tools for conveying informative representation of practice sessions. To bridge this gap, we present a novel visualization system, the Music Practice Browser, for representing, identifying, and analysing music practice behaviors. The Music Practice Browser provides a graphical interface for reviewing recorded practice sessions, which allows musicians, teachers, and researchers to examine aspects and features of music practice behaviors. The system takes beat and practice segment information together with a musical score in XML format as input, and produces a number of different visualizations: Practice Session Work Maps give an overview of contiguous practice segments; Practice Segment Arcs make evident transitions and repeated segments; Practice Session Precision Maps facilitate the identifying of errors; Tempo-Loudness Evolution Graphs track expressive variations over the course of a practice session. We then test the new system on practice sessions of pianists of varying levels of expertise ranging from novice to expert. The practice patterns found include Drill-Correct, Drill-Smooth, Memorization Strategy, Review and Explore, and Expressive Evolution. The analysis reveals practice patterns and behavior differences between beginners and experts, such as a higher proportion of Drill-Smooth patterns in expert practice

Friend Me Your Ears: A Musical Approach to Human-Robot Relationships.

PhDA relationship is something that is necessarily built up over time, however, Human-Robot Interaction (HRI) trials are rarely extended beyond a single ses- sion. These studies are insufficient for examining multi-interaction scenarios, which will become commonplace if the robot is situated in a workplace or adopts a role that is part of a human's routine. Long term studies that have been exe- cuted often demonstrate a declining novelty effect. Music, however, provides an opportunity for affective engagement, shared creativity, and social activity. This being said, it is unlikely that a robot best equipped to build sustainable and meaningful relationships with humans will be one that can solely play music. In their day-to-day lives, most humans encounter machines and computer programs capable of executing impressively complex tasks to a high standard that may provide them with hours of engagement. In order to have anything that that could be classed as a social relationship, the human must have the sense that their interactions are taking place with another, a phenomenon known as social presence. In this thesis, we examine whether the addition of simulated social behaviours will improve a sense of believability or social presence, which, along with an engaging musical interaction, will allow us to move towards something that could be called a human-robot relationship. First, we conducted a large online survey to gain insight into relationships based in regular music activ- ity. Using these results, we designed, constructed and programmed Mortimer, a robotic system capable of playing the drums and a responsive composition algorithm to best meet these aims. This robot was then used in a series of studies, one single session and two long-term, testing various simulated social behaviours to compliment the musical improvisation. These experiments and their results address the paucity of long-term studies both speci cally in Social Robotics and in the broader HRI eld, and provide a promising insight into a possible solution to generally poor outcomes in this area. This conclusion is based upon the model of a positive human-robot relationship and the method- ological approach of automated behavioural metrics to evaluate robotic systems in this regard developed and detailed within the thesis.the EPSRC as part of the Media and Arts Tech-nology Doctoral Training Centre, EP/G03723X/2