DEEP-RHYTHM FOR TEMPO ESTIMATION AND RHYTHM PATTERN RECOGNITION

Hadrien Foroughmand (1) , Geoffroy Peeters (2, 3, 4)

1 Analyse et synthèse sonores [Paris]
2 IP Paris - Institut Polytechnique de Paris
3 LTCI - Laboratoire Traitement et Communication de l'Information
4 S2A - Signal, Statistique et Apprentissage

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.

Domaines: Informatique [cs]; Intelligence artificielle [cs.AI]; Réseau de neurones [cs.NE]; Son [cs.SD]; Traitement du signal et de l'image [eess.SP]; Musique, musicologie et arts de la scène

Hadrien Foroughmand, Geoffroy Peeters. DEEP-RHYTHM FOR TEMPO ESTIMATION AND RHYTHM PATTERN RECOGNITION. International Society for Music Information Retrieval (ISMIR), Nov 2019, Delft, Netherlands. ⟨hal-02457638⟩