Supervisor: Prof Mark Sandler
Research group(s): Centre for Digital Music
This project aims to explore in a robust and complete way, the sort of signal processing that should be deployed in the music recording studio in order to extract musical information from instruments: information that can be used to enhance the music listener experience. The project should also therefore understand the needs of music listeners in this changing musical landscape and develop a proof of concept demonstrator to be trialled with users, using the musical information in innovative ways. By ‘Studio Science’ we mean several things. This includes how to extract the best information from music recordings of instruments for: chord estimation, key signature, time signature and rhythm, musical structure and so on. To date although analysis algorithms abound, there are no comprehensive studies to understand how they work on individual instruments, and how to make them work optimally. Which algorithm suits which instrument and with what parameter settings? What resolution is necessary in the time domain to deliver the necessary response time to the consumer wanting to play along with a track? The project requires a good level of signal processing background and an understanding of scientific method. The student will gain experience of High Performance Computing, including GPUs, and will be able to explore new techniques for User Experience.