In music performance studies, prosody is the musician-specific timing, stress, and sometimes intonation added when interpreting a notated score. Mid- to high-level music prosodic gestures, for example tempo trajectories, often invoke parallels in the physical world, such as a damped oscillator. This project seeks to identify and mathematically model such gestures. The mathematical descriptors will form the basis for a vocabulary of prosodic gestures for music.

When a small group of musicians negotiate in performance (i.e. real-time) the shaping and execution of a collective interpretation, the communication is non-verbal; some of the cues can be embedded in the musical prosody, and some demonstrated through gestures. This project aims to capture, analyze, quantify, and model the cues necessary for effective and engaging ensemble performance, by studying both co-located as well as distributed (over the Internet) ensembles.

A performer can take decades to learn a musical instrument. This studentship will focus on creating instruments that learn the capabilities and artistic preferences of the individual performer, with a particular focus on the relationship between physical gesture and sound production. The successful candidate will develop intelligent gesture-sound mapping strategies, which dynamically update based on feedback from the performer. User studies with professional and amateur musicians will be integral to all stages of the project, and the successful candidate will take a leading role in designing and conducting these studies. The project aims to make performance more accessible to beginning musicians while enabling new modes of expression for experts.

The project’s goal is to construct and evaluate computational models of human temporal expectation. It involves developing probabilistic models of temporal prediction, taking representational account of rhythm and metre. The models and parameters are optimised to maximise prediction performance and compared to human temporal expectations in empirical studies of listeners.

Ubiquitous access to digital music, and the hours of practice required to master new pieces, has led to a decline in amateur instrument playing. Cognisance of music structure can facilitate planning and sequence production, and enhance music making pleasure. Machine intelligence can help diagnose areas of difficulty and offer targeted constructive assistance. The studentship will propose/evaluate score-based visualisations of music structure and gestures that accelerate intermediate piano piece mastery for young and adult learners. Candidates should be proficient at programming, experienced with user interface design, have some background in statistics, and possess at least amateur-level piano playing ability.

The project is concerned with analysis of musical content where it is created (typically in studios), thus affording much cleaner computer representation of musicological information in the music that can then be used both to enhance consumer experiences and recording studio practices. We base the representation on RDF and ontologies, which are the technologies that underpin Open Data, Semantic Web and the Internet of Things. We have collaborated with organisations such as BBC, MusicBrainz and the British Library in developing these principles.

Content (e.g. music, film, tv) recommendation and discovery is reaching a level of maturity (for example, last.fm and Genius). But today, these content descriptions and semantics are derived from the finished product (e.g. CD, MP3, DVD). The research question explored in this PhD relies on performing the audio signal content analysis at the point of content creation. By using ontologies and RDF (Resource Description Framework – a superior version of XML), many new user modalities are enabled. For example, new and complex user queries/searches of the form, “find songs in A minor, with lead and rhythm guitars, less than 2m30secs, and a rhythm that modulates between 90 and 120 beats per minute”. Not only can content semantics enhance the listener/consumer experience, they also enhance the workflow in the recording studio. This is where Focusrite’s interest lies, and especially integration with OSC.