Novel Algorithmic Approaches & Machine Learning for Analysis of Musical Signals

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The aim of this research is to investigate, design and develop both novel algorithmic and machine learning based approaches to the analysis of musical signals in real-time. This research aims to push the boundaries of current knowledgeand approaches to this kind of analysis, proposing new methods of tackling yet-unsolved problems in the field such as polyphonic pitch detection. As an intersection between such a wide variety of disciplines including computer science, electrical engineering and music, research in this space also gives way to various opportunities for cross-school collaboration as well as helping to expand knowledge in a range of distinct fields.Music has been a fundamental part of human culture for aeons - with the earliest known instruments found to date back c. 36000 years (Morley 2003). As such a deep-rooted facet of life, there is a plethora of musical theory as well asmathematics that underpins the field as a whole. With the emergence of computers in the last century, analysis of musical signals is an increasingly growing area of study that is host to a range of challenging problems, forexample pitch detection (both monophonic and polyphonic), blind source separation and musical synchronisation in live ensembles to name but a few.The purpose of this research is to develop efficient and accurate approaches to problems in this space, as well as performing investigations into the viability of various approaches to these problems. Moreover, it is incredibly interesting toexplore multiple approaches to single problems and compare and contrast the results of these experiments over a range of environments.Initially, an in-depth investigation into current and historical approaches will be undertaken, as well as a study into the perceived requirements of new approaches as seen by both academics and commercial "users". The data from these investigations can then be used to identify key pieces of analysis that require more reliable or efficient methods in order to be effective. For each of these identified areas it will be imperative to develop and test multiple approaches (both novel and machine learning based) in order to ascertain which methods perform best under certain conditions or in different environments. The best performing of each of these will then be iteratively improved with the aim of reaching a predetermined accuracy and efficiency when bench marked. As the research is a joint collaboration between computer science and electrical engineering, it is especially important to compare and contrast the different approaches that others in the thesis group have taken to similar problems in the space, especially because others are likely to have varying approaches that could easily influence or improve current implementations.