Since 2007 the Bloomsbury Colleges have offered outstanding interdisciplinary research opportunities through the Bloomsbury Colleges PhD Studentships programme.

Dynamic Bayesian Models for the Analysis of Music (Birkbeck/SOAS)
Subject areas/ keywords: Analysis of world musics; Bayesian statistics; Hidden Markov models; Markov chain Monte Carlo; North Indian classical music

Quantitative analysis of music is important in many areas of research and it has many meaningful applications. Research questions that can be addressed by such analyses include classification of musical works and characterization of the degree of similarity between them. Practical applications include building systems for aiding music theory study and teaching, designing music search and organization tools, and applications in machine listening, for instance for automatic speech recognition. While most studies have prioritised western popular or classical music, it is important to test the validity of models cross-culturally.

This PhD project will explore the application of Bayesian dynamic nonparametric models for addressing important research questions in the area of musicology. There are two important objectives. The first one is to develop new statistical methods that are nonparametric and dynamic in nature i.e. they rely on a minimal set of assumptions and they can be used for analysing sequential data, such as music data. The second one is to apply the developed methods to analyse North Indian music, and in classifying and characterizing the degree of similarity between North Indian musical works.

See further details at the link below.