Work

Experimental validity of using a ResNet to predict sound absorption coefficients of finite samples

Year: 2024

Type: conference-paper

Abstract: The validity of using a neural network to predict sound absorption coefficients of finite porous materials is tested with experimental data with a flush-mounted glass wool sample on a baffle. The network is pre-trained and validated with numerical simulations of flushed-mounted finite absorbers using a Delany-Bazley-Miki model. The experimental setup consists of a 12 x 12 microphone array placed above the absorber and a sound source placed at angles of 0, 40, and 75 degrees with respect to the normal of the sample. The sound absorption coefficients predicted at normal incidence by the network are compared with the impedance tube method as a reference result.

Source: NOISE-CON proceedings

DOI: 10.3397/in_2024_4035

Authors: Patrik ASTE, Eric BRANDãO, Jacques CUENCA, Mélanie NOLAN, U. Peter SVENSSON, Elias ZEA

Institutions: KTH Royal Institute of Technology, Universidade Federal de Santa Maria, Technical University of Denmark, Norwegian University of Science and Technology

Language: English

Topic: Acoustic Wave Phenomena Research

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4403266307

FWCI: 0.981

Cites:

Cited by: 1

Related to: 10