Deep-Rhythm for Global Tempo Estimation in Music

It has been shown that the harmonic series at the tempo frequency of the onset-strength-function of an audio signal accurately describes its rhythm pattern and can be used to perform tempo or rhythm pattern estimation. Recently, in the case of multi-pitch estimation, the depth of the input layer of a convolutional network has been used to represent the harmonic series of pitch candidates. We use a similar idea here to represent the harmonic series of tempo candidates. We propose the Harmonic-Constant-Q-Modulation which represents, using a 4D-tensors, the harmonic series of modulation frequencies (considered as tempo frequencies) in several acoustic frequency bands over time. This representation is used as input to a convolutional network which is trained to estimate tempo or rhythm pattern classes. Using a large number of datasets, we evaluate the performance of our approach and compare it with previous approaches. We show that it slightly increases Accuracy-1 for tempo estimation but not the average-mean-Recall for rhythm pattern recognition.