City Research Online

Abstract

Automatically following rhythms by beat tracking is by no means a solved problem, especially when dealing with varying tempo and expressive timing. This paper presents a connectionist machine learning approach to expressive rhythm prediction, based on cognitive and neurological models. We detail a multi-layered recurrent neural network combining two complementary network models as hidden layers within one system. The first layer is a Gradient Frequency Neural Network (GFNN), a network of nonlinear oscillators which acts as an entraining and learning resonant filter to an audio signal. The GFNN resonances are used as inputs to a second layer, a Long Short-term Memory Recurrent Neural Network (LSTM). The LSTM learns the long-term temporal structures present in the GFNN's output, the metrical structure implicit within it. From these inferences, the LSTM predicts when the next rhythmic event is likely to occur. We train the system on a dataset selected for its expressive timing qualities and evaluate the system on its ability to predict rhythmic events. We show that our GFNN-LSTM model performs as well as state-of-the art beat trackers and has the potential to be used in real-time interactive systems, following and generating expressive rhythmic structures.

Publication Type:	Book Section
Additional Information:	© 2015 Andrew J. Lambert et al.
Subjects:	M Music and Books on Music > M Music Q Science > QA Mathematics > QA76 Computer software
Departments:	School of Communication & Creativity > Performing Arts > Music School of Science & Technology > Computer Science

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