A Data-Driven Approach to Violin Making

González, Sebastián; Salvi, Davide; Baeza, Daniel; Antonacci, Fabio; Sarti, A.

doi:10.48550/arxiv.2102.04254

Cited by 2 publications

(4 citation statements)

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“…The dataset is composed of 1568 different synthetic meshes of violin top plates with constant thickness and arching generated by the parametric model introduced in [29], [30]. The parameters defining the shapes are varied according to Gaussian distributions centered around the parameters of a reference violin, as described by the authors in [29], [30]. We computed the vibration and the radiated acoustic pressure of the plates through finite element analysis using COMSOL Multiphysics ® [31].…”

Section: A Simulation Setupmentioning

confidence: 99%

Near field Acoustic Holography on arbitrary shapes using Convolutional Neural Network

Olivieri

Pezzoli

Antonacci

et al. 2021

2021 29th European Signal Processing Conference (EUSIPCO)

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Near-field Acoustic Holography (NAH) is a wellknown problem aimed at estimating the vibrational velocity field of a structure by means of acoustic measurements. In this paper, we propose a NAH technique based on Convolutional Neural Network (CNN). The devised CNN predicts the vibrational field on the surface of arbitrary shaped plates (violin plates) with orthotropic material properties from a limited number of measurements. In particular, the architecture, named super resolution CNN (SRCNN), is able to estimate the vibrational field with a higher spatial resolution compared to the input pressure. The pressure and velocity datasets have been generated through Finite Element Method simulations. We validate the proposed method by comparing the estimates with the synthesized ground truth and with a state-of-the-art technique. Moreover, we evaluate the robustness of the devised network against noisy input data.

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Section: A Simulation Setupmentioning

confidence: 99%

Near field Acoustic Holography on arbitrary shapes using Convolutional Neural Network

Olivieri

Pezzoli

Antonacci

et al. 2021

2021 29th European Signal Processing Conference (EUSIPCO)

Self Cite

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“…The variation of each parameter is computed in a random way using a zero-mean Gaussian distribution. The full explanation of the dataset creation can be found in [4]. Depending on the number of aspects we decide to vary, we build several datasets, where a different number of parameters is needed to define every single mesh.…”

Section: Definition Of the Datasets And The Neural Networkmentioning

confidence: 99%

“…The network is then trained using the Levenberg-Marquardt training function, which is a combination of gradient descent and Newton's method and a variable number of epochs for each training, always below 100. All the networks we train have a coefficient of multiple determination R 2 that is greater than 0.9, so that we can consider our method as reliable and use it in the following studies [4].…”

Section: Definition Of the Datasets And The Neural Networkmentioning

confidence: 99%

“…With the method, we created a large set of plates that we used to train a neural network to predict the vibratory response of the plate. In [4] we mainly focused on its predictive power and the inherent correlations between geometry and vibrational response. In this manuscript we focus on several use cases of the network for violin design and optimization.…”

Section: Introductionmentioning

confidence: 99%

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Parametric Optimization of Violin Top Plates using Machine Learning

Salvi¹,

González²,

Antonacci³

et al. 2021

Preprint

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We recently developed a neural network that receives as input the geometrical and mechanical parameters that define a violin top plate and gives as output its first ten eigenfrequencies computed in free boundary conditions. In this manuscript, we use the network to optimize several error functions, with the goal of analyzing the relationship between the eigenspectrum problem for violin top plates and their geometry. First, we focus on the violin outline. Given a vibratory feature, we find which is the best geometry of the plate to obtain it. Second, we investigate whether, from the vibrational point of view, a change in the outline shape can be compensated by one in the thickness distribution and vice versa. Finally, we analyze how to modify the violin shape to keep its response constant as its material properties vary. This is an original technique in musical acoustics, where artificial intelligence is not widely used yet. It allows us to both compute the vibrational behavior of an instrument from its geometry and optimize its shape for a given response. Furthermore, this method can be of great help to violin makers, who can thus easily understand the effects of the geometry changes in the violins they build, shedding light on one of the most relevant and, at the same time, less understood aspects of the construction process of musical instruments.

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A Data-Driven Approach to Violin Making

Cited by 2 publications

References 0 publications

Near field Acoustic Holography on arbitrary shapes using Convolutional Neural Network

Near field Acoustic Holography on arbitrary shapes using Convolutional Neural Network

Parametric Optimization of Violin Top Plates using Machine Learning

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