Technology Category
Machine Learning: Hardware-aware MLCognitive Modeling & Cognitive Systems: Neural Spike CodingNatural Language Processing: Learning & Optimization for NLP
Acoustic neural networks: Identifying design principles and exploring physical feasibility
🤖 AI Summary
Current acoustic neural networks lack a systematic design framework that explicitly links learnable parameters to physically measurable acoustic properties—such as material attenuation and geometric configuration—hindering the deployment of low-power, passive acoustic computing in resource-constrained environments. To address this, we propose the first physics-aware digital twin training framework that incorporates hardware-imposed acoustic constraints—including non-negativity and zero bias—directly into network optimization, while establishing an explicit mapping from network weights to measurable acoustic parameters. Furthermore, we introduce the SincHSRNN, a hybrid model compatible with passive components, integrating acoustic waveguide modeling, intensity-based nonlinearity, learnable bandpass filtering, and hierarchical temporal processing. Evaluated on AudioMNIST, it achieves 95% accuracy—the first demonstration of efficient speech recognition using purely passive acoustic hardware—thereby unifying physical realizability with competitive computational performance.
Technology Category
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Developing systematic design framework for acoustic neural networks
Establishing physical feasibility through measurable acoustic properties
Enabling low-power computation with wave-based analog systems
Innovation
Methods, ideas, or system contributions that make the work stand out.
Designs acoustic neural networks using sound waves
Trains networks with physical constraints for feasibility
Learns parameters mapping to measurable acoustic properties
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