I would welcome people to contact me if they are interested in conducting PhD research in areas that I research. Prospective PhD students may be interested in one of the following projects, or may wish to propose the area of research (see below). Either way, please do contact me using the contact details in my profile page.
Funding may be available for PhDs through the School of Computing, for good candidates.
Traditionally, when computational software performs music the performances can be criticised for being too unnatural, lacking interpretation and, in short, being too mechanical. However much progress has been made within the field of expressive musical performance and musical interpretation expression. Alongside these advances have been interesting findings in musical expectation (i.e. what people expect to hear when listening to a piece of music), as well as work on emotions that are present within music and on how information and meaning are conveyed in music. Each of these advances raises questions of how the relevant aspects could be interpreted by a musical performer.
Potential application areas for computer systems that can perform music in an appropriately expressive manner include, for example, improving playback in music notation editors (like Sibelius), or the automated performance of music generated on-the-fly for 'hold' music (played when waiting on hold during phone calls). Practical work exploring this could involve writing software that performs existing pieces, or could be to write software that can improvise, interpreting incoming sound/music and generating an appropriate sonic/musical response to it in real time.
NB Other suggested research questions in this area are also welcomed.
Computational creativity is the computational study of computer systems that can produce creative work or act creatively. Recent research has looked at how we can make such research more scientifically rigorous. In particular, how do we evaluate how creative these systems are?Various contributions (including my own) have been made in terms of methodologies for evaluation of creative systems; the time is ripe for comparing these methods in different domains. Students may wish to employ comparison metrics such as information-theoretic measures, statistical methods, relating the results to user evaluations and/or computational modelling in this research.
It is expected that the PhD work will result in clear recommendations to the computational creativity research community about how to evaluate their systems, and contribute towards solving any issues not addressed by the fledgling existing methodologies that exist.
Digital preservation of audio material raises many interesting questions to be investigated, including how to archive a sound, what metadata to keep, and future-proofing. Of particular interest is how to explore issues of retention of musical/sonic information from relevant digital audio material, for later access and analysis. Sound and music are typically very open to interpretation, with much information being conveyed through musical/sonic material.
Music Information Retrieval (MIR) allows us to see what information is communicated by musical material, using techniques from Computing and Music. Typically MIR is applied to digital rather than physical materials and comes in a variety of forms that could be explored, such as using digital tools or computational analysis for informing and enhancing musicological analysis or musical interpretation. In this PhD project, the PhD candidate will carry out such explorations, towards the development of an archive or a methodology for existing archives to access and retrieve musical information from archive music-based data.
The Semantic Web is a vision of the Web where items on the web are data, which get linked together if they are data referring to similar things. In the Semantic Web, "a computer program can learn enough about what the data means to process it." (Tim Berners-Lee, Weaving the Web, 2000) There are some data and ontologies (computational models of knowledge) published on the Semantic Web about music, for example the Music Ontology (musicontology.com).
Research is starting to emerge on using information retrieval in conjunction with data on the Semantic Web; this project proposes that the PhD candidate explores how Music Information Retrieval (MIR) can be enhanced using Semantic Web data and tools. During this PhD project, the candidate would look at a particular question in music information retrieval, such as how to use MIR to perform computational musicological analysis or how to identify music that is intended to express similar meanings or emotions. (Alternatively the candidate may wish to address a different music information retrieval problem, in an area of specific interest to them; this is welcome.) The PhD candidate would explore how this MIR question can be addressed by using music-specific Semantic Web data/models/technologies to enhance the process of identifying relevant information.
It is expected that the PhD candidate will produce computational tools or software that engages directly with the Semantic Web in order to perform the musical information retrieval task. The performance of Semantic-Web enhanced solutions should be compared to traditional MIR solutions for that task, if any exist, and evaluated as to the accuracy and comprehensiveness with which the tools or software carry out the task.
As well as the above suggested projects, I would welcome self-proposed research proposals in my research interests, particularly in:
Information about becoming a postgraduate in the School of Computing is available here.