Downbeat Detection with Conditional Random Fields and Deep Learned Features

In this paper, we introduce a novel Conditional Random Field (CRF) system that detects the downbeat sequence of musical audio signals. Feature functions are computed from four deep learned representations based on harmony, rhythm, melody and bass content to take advantage of the high-level and multi-faceted aspect of this task. Downbeats being dynamic, the powerful CRF classification system allows us to combine our features with an adapted temporal model in a fully data-driven fashion. Some meters being under-represented in our training set, we show that data augmentation enables a statistically significant improvement of the results by taking into account class imbalance. An evaluation of different configurations of our system on nine datasets shows its efficiency and potential over a heuristic based approach and four downbeat tracking algorithms.