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[SAMI 3] Song level audio features for navigating large music collections

Supervisor: Prof Mark Sandler

Research group(s): Centre for Digital Music

Some music services have 30 or 40 million tracks to choose from. So how do you find things to listen to that you don't already know and that will interest you? Well today, it's not easy, and perhaps the best way is to listen to a broadcast radio station that you trust, because the DJ presenter is deliberately searching out "interesting" music. But what if you can do something similar automatically using signal processing? This project aims to do that. Taking inspiration from psychological studies that show that surprise and change in music is interesting, the idea behind the project is to develop new audio features that capture 'interestingness', 'variety' and 'structured-ness' in a piece of music. Starting with pilot studies that determine which features are capable of representing 'interestingness' etc in musical pieces known to be interesting, the project will proceed by working with our partners who aggregate massive music collections (e.g. 40 million songs) and perform user trials. Students will need good familiarity with Signal Processing, with User Interaction (or be willing to learn quickly), and with coding in an appropriate programming language, especially for mobile platforms (or be willing to learn). The student will work with some MPEG standards. One aspect of this project will be to see how much benefit can be found by using the full stereo music signal to increase the discriminatory powers of the features.
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