Abstract Andrew Lambert 20 September 2016

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Modelling Metrical Flux: Adaptive Oscillator Networks for Expressive Rhythmic Perception and Prediction

Andrew Lambert, City University of London

Abstract: Beat induction is the perceptual and cognitive process by which humans listen to music and perceive a steady pulse. Computationally modelling beat induction is important for many Music Information Retrieval (MIR) methods and is in general an open problem, especially when processing expressive timing, e.g. tempo changes or rubato. A neural oscillator model has been proposed, the Gradient Frequency Neural Network (GFNN), which can model the perception of pulse and metre. GFNNs have been applied successfully to a range of 'difficult' music perception problems such as polyrhythms and syncopation. Our research explores the use of GFNNs for expressive rhythm perception and modelling, addressing the current gap in knowledge for how to deal with varying tempo and expressive timing in automated and interactive music systems. The cannonical oscillators contained in a GFNN have entrainment properties, allowing phase shifts and resulting in changes to the observed frequencies. This makes them good candidates for solving the expressive timing problem. We have found that GFNNs perform poorly when dealing with tempo changes in the stimulus. Therefore, we introduce a novel Adaptive Frequency Neural Network (AFNN); extending the GFNN with a Hebbian learning rule on oscillator frequencies. Two new adaptive behaviours (attraction and elasticity) increase entrainment, and increase model efficiency by allowing for a great reduction in the size of the network.

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