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DEEP-RHYTHM FOR TEMPO ESTIMATION AND RHYTHM PATTERN RECOGNITION

Abstract : 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.
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https://hal.archives-ouvertes.fr/hal-02457638
Contributor : Hadrien Foroughmand <>
Submitted on : Tuesday, January 28, 2020 - 11:25:12 AM
Last modification on : Wednesday, October 14, 2020 - 3:58:48 AM
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  • HAL Id : hal-02457638, version 1

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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⟩

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