Near field Acoustic Holography on arbitrary shapes using Convolutional Neural Network

Olivieri, Marco; Pezzoli, Mirco; Antonacci, Fabio; Sarti, Augusto

doi:10.23919/eusipco54536.2021.9616334

Cited by 11 publications

(8 citation statements)

References 24 publications

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“…displacement at a point, stiffness of the resulting geometry). We have not studied the prediction of the spatial behavior of the modes, but research in our own group has shown that this can be predicted with convolutional and autoencoders networks [13]. The path towards optimization of sound seems finally within reach, more than 300 years after Stradivari built the instruments that inspired this study.…”

Section: Discussionmentioning

confidence: 99%

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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Section: Discussionmentioning

confidence: 99%

Parametric Optimization of Violin Top Plates using Machine Learning

Salvi¹,

González²,

Antonacci³

et al. 2021

Preprint

Self Cite

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show abstract

“…We are certain that this method can be easily applied to other domains of FEM simulation and maybe used to speed up the computation with simple geometries in current FEM solvers. Furthermore, recent experiments in learning the modal response of plates seem to indicate that this approach could also be used to predict the acoustic directional response of the modes as well as their frequency 18 . A number of luthier-specific conclusions can be drawn from study.…”

Section: Discussionmentioning

confidence: 99%

“…Machine intelligence has been successfully applied to physical systems of all sorts 10 , including spin phase transitions [11][12][13] ; quantum topological transitions 14 ; and even physical problems as simple as the pendulum 15 . In computational acoustics, NN's have been employed in a wide range of tasks 16 , including the localisation of acoustic sources 17 ; nearfield holography 18 ; and acoustic scene classification 19 . To the best of our knowledge, however, AI has not yet been applied to the problem of eigenfrequencies of plates, let alone the prediction of the acoustic behaviour of violin tops.…”

Section: Introductionmentioning

confidence: 99%

A data-driven approach to violin making

González

Salvi

Baeza

et al. 2021

Sci Rep

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Of all the characteristics of a violin, those that concern its shape are probably the most important ones, as the violin maker has complete control over them. Contemporary violin making, however, is still based more on tradition than understanding, and a definitive scientific study of the specific relations that exist between shape and vibrational properties is yet to come and sorely missed. In this article, using standard statistical learning tools, we show that the modal frequencies of violin tops can, in fact, be predicted from geometric parameters, and that artificial intelligence can be successfully applied to traditional violin making. We also study how modal frequencies vary with the thicknesses of the plate (a process often referred to as plate tuning) and discuss the complexity of this dependency. Finally, we propose a predictive tool for plate tuning, which takes into account material and geometric parameters.

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“…A third category comprising deep learning emerged as an alternative approach for sound field reconstruction and a wide range of problems in the field of acoustics [23][24][25][26][27]. In [28], a convolutional neural network (CNN) has been proposed for the reconstruction of room transfer functions.…”

Section: Introductionmentioning

confidence: 99%

Implicit Neural Representation With Physics-Informed Neural Networks For The Reconstruction Of The Early Part Of Room Impulse Responses

Pezzoli,

Antonacci,

Sarti

2024

Proceedings of the 10th Convention of the European Acoustics Association Forum Acusticum 2023

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Recently, deep learning and machine learning approaches have been widely employed for various applications in acoustics. Nonetheless, in the area of sound field processing and reconstruction, classic methods based on the solutions of the wave equation are still widespread. Lately, physics-informed neural networks have been proposed as a deep learning paradigm for solving partial differential equations that govern physical phenomena, bridging the gap between purely data-driven and model-based methods. In this study, we exploit physics-informed neural networks to reconstruct the early part of missing room impulse responses in a uniform linear array. This methodology allows us to leverage the underlying law of acoustics, i.e., the wave equation, forcing the neural network to generate physically meaningful solutions given only a limited number of data points. The results from real measurements show that the proposed model achieves accurate reconstruction and performance in line with state-of-the-art deep learning and compressive sensing techniques while maintaining a lightweight architecture.

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Near field Acoustic Holography on arbitrary shapes using Convolutional Neural Network

Cited by 11 publications

References 24 publications

Parametric Optimization of Violin Top Plates using Machine Learning

Parametric Optimization of Violin Top Plates using Machine Learning

A data-driven approach to violin making

Implicit Neural Representation With Physics-Informed Neural Networks For The Reconstruction Of The Early Part Of Room Impulse Responses

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