  In this paper, we present a novel state of the art system for automatic
downbeat tracking from music signals. The audio signal is first segmented in
frames which are synchronized at the tatum level of the music. We then extract
different kind of features based on harmony, melody, rhythm and bass content to
feed convolutional neural networks that are adapted to take advantage of each
feature characteristics. This ensemble of neural networks is combined to obtain
one downbeat likelihood per tatum. The downbeat sequence is finally decoded
with a flexible and efficient temporal model which takes advantage of the
metrical continuity of a song. We then perform an evaluation of our system on a
large base of 9 datasets, compare its performance to 4 other published
algorithms and obtain a significant increase of 16.8 percent points compared to
the second best system, for altogether a moderate cost in test and training.
The influence of each step of the method is studied to show its strengths and
shortcomings.
