Pulse clarity metrics developed from a deep learning beat tracking model

In this paper we present novel pulse clarity metrics based on different sections of a state-of-the-art beat tracking model. Said model consists of two sections: a recurrent neural network that estimates beat probabilities for audio and a dynamic Bayesian network (DBN) that determines beat moments from the neural network's output. We obtained pulse clarity metrics by analyzing periodical behavior from neuron activation values and we interpreted the probability distribution computed by the DBN as the model's certainty. To analyze whether the inner workings of the model provide new insight into pulse clarity, we also proposed reference metrics using the output of both networks. We evaluated the pulse clarity metrics over a wide range of stimulus types such as songs and mono-tonal rhythms, obtaining comparable results to previous models. These results suggest that adapting a model from a related task is feasible for the pulse clarity problem. Additionally, results of the evaluation of pulse clarity models on multiple datasets showed that, with some variability, both ours and previous work generalized well beyond their original training datasets