wav2shape: Hearing the Shape of a Drum Machine

• URL: http://arxiv.org/abs/2007.10299v1
• Date: Mon, 20 Jul 2020 17:35:24 GMT
• Title: wav2shape: Hearing the Shape of a Drum Machine
• Authors: Han Han and Vincent Lostanlen
• Abstract summary: Disentangling and recovering physical attributes from a waveform is a challenging inverse problem in audio signal processing. We propose to address this problem via a combination of time--frequency analysis and supervised machine learning.
• Score: 4.283530753133897
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Disentangling and recovering physical attributes, such as shape and material, from a few waveform examples is a challenging inverse problem in audio signal processing, with numerous applications in musical acoustics as well as structural engineering. We propose to address this problem via a combination of time--frequency analysis and supervised machine learning. We start by synthesizing a dataset of sounds using the functional transformation method. Then, we represent each percussive sound in terms of its time-invariant scattering transform coefficients and formulate the parametric estimation of the resonator as multidimensional regression with a deep convolutional neural network. We interpolate scattering coefficients over the surface of the drum as a surrogate for potentially missing data, and study the response of the neural network to interpolated samples. Lastly, we resynthesize drum sounds from scattering coefficients, therefore paving the way towards a deep generative model of drum sounds whose latent variables are physically interpretable.

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