Bayesian Meter Tracking on Learned Signal Representations

Most music exhibits a pulsating temporal structure, known as meter. Consequently, the task of meter tracking is of great importance for the domain of Music Information Retrieval. In our contribution, we specifically focus on Indian art musics, where meter is conceptualized at several hierarchical levels, and a diverse variety of metrical hierarchies exist, which poses a challenge for state of the art analysis methods. To this end, for the first time, we combine Convolutional Neural Networks (CNN), allowing to transcend manually tailored signal representations, with subsequent Dynamic Bayesian Tracking (BT), modeling the recurrent metrical structure in music. Our approach estimates meter structures simultaneously at two metrical levels. The results constitute a clear advance in meter tracking performance for Indian art music, and we also demonstrate that these results generalize to a set of Ballroom dances. Furthermore, the incorporation of neural network output allows a computationally efficient inference. We expect the combination of learned signal representations through CNNs and higher-level temporal modeling to be applicable to all styles of metered music, provided the availability of sufficient training data.