Acoustic neural networks: Identifying design principles and exploring physical feasibility

TL;DR

This paper introduces a framework for designing acoustic neural networks that perform computation through sound wave propagation, demonstrating accurate speech classification while adhering to physical constraints for low-power, wave-based computing.

Contribution

It presents a systematic design framework connecting neural network components to physical acoustic properties, enabling realizable, low-power acoustic neural network systems.

Findings

Achieved up to 95% accuracy on AudioMNIST dataset

Demonstrated compatibility with passive acoustic components

Provided design principles linking network parameters to physical properties

Abstract

Wave-guide-based physical systems provide a promising route toward energy-efficient analog computing beyond traditional electronics. Within this landscape, acoustic neural networks represent a promising approach for achieving low-power computation in environments where electronics are inefficient or limited, yet their systematic design has remained largely unexplored. Here we introduce a framework for designing and simulating acoustic neural networks, which perform computation through the propagation of sound waves. Using a digital-twin approach, we train conventional neural network architectures under physically motivated constraints including non-negative signals and weights, the absence of bias terms, and nonlinearities compatible with intensity-based, non-negative acoustic signals. Our work provides a general framework for acoustic neural networks that connects learnable network components directly to physically measurable acoustic properties, enabling the systematic design of realizable acoustic computing systems. We demonstrate that constrained recurrent and hierarchical architectures can perform accurate speech classification, and we propose the SincHSRNN, a hybrid model that combines learnable acoustic bandpass filters with hierarchical temporal processing. The SincHSRNN achieves up to 95% accuracy on the AudioMNIST dataset while remaining compatible with passive acoustic components. Beyond computational performance, the learned parameters correspond to measurable material and geometric properties such as attenuation and transmission. Our results establish general design principles for physically realizable acoustic neural networks and outline a pathway toward low-power, wave-based neural computing.