Machine learning aided near-field acoustic holography based on equivalent source method
S. K. Chaitanya, Siddharth Sriraman, Srinath Srinivasan, K. Srinivasan
Abstract
In recent times, equivalent source method-based near-field acoustic holography methods have been extensively applied in sound source localization and characterization. The most commonly used equivalent sources are spherical harmonics. In a non-reverberant environment with no reflections, these equivalent sources could be the best choice since spherical harmonics are derived for the Sommerfeld boundary condition. However, these methods are not the best fit for reverberating environments. In such cases, a new relationship can be calculated between the field weights and the measured pressure with enough training examples. The proposed machine learning models include linear regression (LR) with adaptive moment estimation (Adam), LR with limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS), and multi-layer perceptron with one and two hidden layers. These methods are tested for multiple monopoles and vibrating plate simulations in a room with different wall absorption coefficients. The data-driven methods are also studied on loudspeakers numerically and experimentally in a free field environment. The results from these methods are compared with the results of one norm convex optimization (L1CVX). LR with L-BFGS performed the best among all the methods studied and performed better than L1CVX for less absorption coefficient for geometrically separable sources. LR with L-BFGS also has much faster inference times.