Robustness analysis and experimental validation of a deep neural network for acoustic source imaging

Li，Qing1; Arcondoulis，Elias J.G.1,2; Wei，Sheng1; Xu，Pengwei1; Liu，Yu1

2024-07-01

10.1016/j.ymssp.2024.111477

Mechanical Systems and Signal Processing   影响因子和分区

0888-3270

1096-1216

Deep Neural Network (DNN) models offer an attractive alternative to existing acoustic source imaging techniques, such as acoustic beamforming, due to their ever-growing potential with increasing computational power. Source resolution of acoustic beamforming methods is limited at lower frequencies and their source maps may possess sidelobes at higher frequencies. However, acoustic beamforming methods are typically robust over a wide range of simulation and experimental conditions, such as (i) the number of sources present, (ii) source frequency and (ii) extraneous noise sources. The performance of DNN models, when these conditions are varied from their specific design criteria, is yet to be investigated and much work is needed in this area before DNN models can be utilized in experiments, such as wind tunnel tests. Furthermore, few studies have been conducted on experimental validation of DNN models, predominately due to the difficulty of large sets of experimentally obtained data needed for DNN model training and the sensitivity of DNN model performance when any of the aforementioned experimental conditions are varied. In this paper, a series of studies on the robustness of DNN models based on numerical data and experimental data are presented. Numerical DNN (NDNN) models are trained using in-phase and random-phase pressure data generated from six sources over design frequencies from 500 Hz to 20,000 Hz. The robustness of the NDNN models is tested via (1) inclusion of extraneous Gaussian white noise, (2) inclusion of extraneous tonal noise near the design frequency, (3) using source frequencies that slightly differ from the design frequencies and (4) using a number of sources that differs from the design source number. DNN model performance metrics are introduced that present a promising framework for future DNN model studies and bridging the gap between NDNN and experimentally trained DNN models. A preliminary experimental validation was conducted using a single speaker that was systematically placed over a speaker grid to generate training data via acoustic superposition, from which an experimentally trained DNN (EDNN) model was produced. The EDNN model yields exceptional noise source localization capability of the DNN model, revealing a promising start for a more sophisticated EDNN model.

Acoustic source imaging Deep learning Deep neural networks Microphone array

SCI ; EI

英语

第一 ; 通讯

20241916034917

Acoustic noise ; Acoustic noise measurement ; Beamforming ; Microphone array ; Neural network models ; White noise ; Wind tunnels

Ergonomics and Human Factors Engineering:461.4 ; Wind Tunnels:651.2 ; Electromagnetic Waves in Relation to Various Structures:711.2 ; Artificial Intelligence:723.4 ; Acoustic Noise:751.4 ; Acoustic Devices:752.1 ; Acoustic Variables Measurements:941.2

ENGINEERING

2-s2.0-85192104806

Scopus

1.Department of Mechanics and Aerospace Engineering,Southern University of Science and Technology,Shenzhen,Guangdong,518055,China
2.Faculty of Engineering,University of Bristol,Bristol,BS8 1TR,United Kingdom

Li，Qing,Arcondoulis，Elias J.G.,Wei，Sheng,et al. Robustness analysis and experimental validation of a deep neural network for acoustic source imaging[J]. Mechanical Systems and Signal Processing,2024,216.

Li，Qing,Arcondoulis，Elias J.G.,Wei，Sheng,Xu，Pengwei,&Liu，Yu.(2024).Robustness analysis and experimental validation of a deep neural network for acoustic source imaging.Mechanical Systems and Signal Processing,216.

Li，Qing,et al."Robustness analysis and experimental validation of a deep neural network for acoustic source imaging".Mechanical Systems and Signal Processing 216(2024).