A MULTI-MODEL APPROACH TO BEAT TRACKING CONSIDERING HETEROGENEOUS MUSIC STYLES

In this paper we present a new beat tracking algorithm which extends an existing state-of-the-art system with a multi-model approach to represent different music styles. The system uses multiple recurrent neural networks, which are specialised on certain musical styles, to estimate possi-ble beat positions. It chooses the model with the most ap-propriate beat activation function for the input signal and jointly models the tempo and phase of the beats from this activation function with a dynamic Bayesian network. We test our system on three big datasets of various styles and report performance gains of up to 27 % over existing state-of-the-art methods. Under certain conditions the system is able to match even human tapping performance. 1. INTRODUCTION AN