A comparative analysis of the directional sound radiation of historical violins.

The directivity pattern of a musical instrument describes the sound energy radiation as a function of frequency and direction of emission. Violins exhibit a rather complex directivity pattern, which is known to show rapid variations across frequencies, and whose behavior cannot be easily predicted except in the lowest frequency range. The acoustic behavior of the violin is a fascinating research topic that has prompted numerous published works, but a thorough, comprehensive, and comparative analysis of violin directivity patterns is long overdue. In this article, we propose a set of metrics for characterizing the radiative behavior of musical instruments and, in particular, for comparing their directivity patterns. We apply such metrics for a comparative analysis of the directivity patterns of some of the most prestigious historical violins ever made, including grand masters such as Antonio Stradivari, Giuseppe Guarneri "del Gesú" and members of the Amati family. The instruments are preserved in the Violin Museum of Cremona, Italy, where our lab is located. The analysis methodology introduced in this work allowed us to quantitatively evaluate the similarity of directivity patterns of such extraordinary instruments and draw some interesting conclusions.

Deep Prior Approach for Room Impulse Response Reconstruction.

In this paper, we propose a data-driven approach for the reconstruction of unknown room impulse responses (RIRs) based on the deep prior paradigm. We formulate RIR reconstruction as an inverse problem. More specifically, a convolutional neural network (CNN) is employed prior, in order to obtain a regularized solution to the RIR reconstruction problem for uniform linear arrays. This approach allows us to avoid assumptions on sound wave propagation, acoustic environment, or measuring setting made in state-of-the-art RIR reconstruction algorithms. Moreover, differently from classical deep learning solutions in the literature, the deep prior approach employs a per-element training. Therefore, the proposed method does not require training data sets, and it can be applied to RIRs independently from available data or environments. Results on simulated data demonstrate that the proposed technique is able to provide accurate results in a wide range of scenarios, including variable direction of arrival of the source, room T60, and SNR at the sensors. The devised technique is also applied to real measurements, resulting in accurate RIR reconstruction and robustness to noise compared to state-of-the-art solutions.

A Physics-Informed Neural Network Approach for Nearfield Acoustic Holography.

In this manuscript, we describe a novel methodology for nearfield acoustic holography (NAH). The proposed technique is based on convolutional neural networks, with autoencoder architecture, to reconstruct the pressure and velocity fields on the surface of the vibrating structure using the sampled pressure soundfield on the holographic plane as input. The loss function used for training the network is based on a combination of two components. The first component is the error in the reconstructed velocity. The second component is the error between the sound pressure on the holographic plane and its estimate obtained from forward propagating the pressure and velocity fields on the structure through the Kirchhoff-Helmholtz integral; thus, bringing some knowledge about the physics of the process under study into the estimation algorithm. Due to the explicit presence of the Kirchhoff-Helmholtz integral in the loss function, we name the proposed technique the Kirchhoff-Helmholtz-based convolutional neural network, KHCNN. KHCNN has been tested on two large datasets of rectangular plates and violin shells. Results show that it attains very good accuracy, with a gain in the NMSE of the estimated velocity field that can top 10 dB, with respect to state-of-the-art techniques. The same trend is observed if the normalized cross correlation is used as a metric.