Acoustic neural networks: Identifying design principles and exploring physical feasibility

• URL: http://arxiv.org/abs/2511.21313v1
• Date: Wed, 26 Nov 2025 11:59:29 GMT
• Title: Acoustic neural networks: Identifying design principles and exploring physical feasibility
• Authors: Ivan Kalthoff, Marcel Rey, Raphael Wittkowski,
• Abstract summary: We introduce a framework for designing and simulating acoustic neural networks, which perform through the propagation of sound waves.<n>Our work provides a general framework for acoustic neural networks that connects learnable network components directly to physically measurable acoustic properties.<n>Our results establish general design principles for physically realizable acoustic neural networks and outline a pathway toward low-power, wave-based neural computing.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Neuromorphic Wireless Split Computing with Resonate-and-Fire Neurons [69.73249913506042]
  This paper investigates a wireless split computing architecture that employs resonate-and-fire (RF) neurons to process time-domain signals directly.<n>By resonating at tunable frequencies, RF neurons extract time-localized spectral features while maintaining low spiking activity.<n> Experimental results show that the proposed RF-SNN architecture achieves comparable accuracy to conventional LIF-SNNs and ANNs.
  arXiv  Detail & Related papers  (2025-06-24T21:14:59Z)
• Port-Hamiltonian Neural Networks with Output Error Noise Models [2.144088660722956]
  Hamiltonian neural networks (HNNs) represent a promising class of physics-informed deep learning methods.<n>Their direct application to engineering systems is often challenged by practical issues, including the presence of external inputs, dissipation, and noisy measurements.<n>This paper introduces a novel framework that enhances the capabilities of HNNs to address these real-life factors.
  arXiv  Detail & Related papers  (2025-02-20T10:33:21Z)
• Graph Neural Networks for Learning Equivariant Representations of Neural Networks [55.04145324152541]
  We propose to represent neural networks as computational graphs of parameters. Our approach enables a single model to encode neural computational graphs with diverse architectures. We showcase the effectiveness of our method on a wide range of tasks, including classification and editing of implicit neural representations.
  arXiv  Detail & Related papers  (2024-03-18T18:01:01Z)
• A Hybrid Neural Coding Approach for Pattern Recognition with Spiking Neural Networks [53.31941519245432]
  Brain-inspired spiking neural networks (SNNs) have demonstrated promising capabilities in solving pattern recognition tasks. These SNNs are grounded on homogeneous neurons that utilize a uniform neural coding for information representation. In this study, we argue that SNN architectures should be holistically designed to incorporate heterogeneous coding schemes.
  arXiv  Detail & Related papers  (2023-05-26T02:52:12Z)
• Contrastive-Signal-Dependent Plasticity: Self-Supervised Learning in Spiking Neural Circuits [61.94533459151743]
  This work addresses the challenge of designing neurobiologically-motivated schemes for adjusting the synapses of spiking networks. Our experimental simulations demonstrate a consistent advantage over other biologically-plausible approaches when training recurrent spiking networks.
  arXiv  Detail & Related papers  (2023-03-30T02:40:28Z)
• Training neural networks with structured noise improves classification and generalization [0.0]
  We show how adding structure to noisy training data can substantially improve the algorithm performance. We also prove that the so-called Hebbian Unlearning rule coincides with the training-with-noise algorithm when noise is maximal.
  arXiv  Detail & Related papers  (2023-02-26T22:10:23Z)
• ETLP: Event-based Three-factor Local Plasticity for online learning with neuromorphic hardware [105.54048699217668]
  We show a competitive performance in accuracy with a clear advantage in the computational complexity for Event-Based Three-factor Local Plasticity (ETLP) We also show that when using local plasticity, threshold adaptation in spiking neurons and a recurrent topology are necessary to learntemporal patterns with a rich temporal structure.
  arXiv  Detail & Related papers  (2023-01-19T19:45:42Z)
• Simple initialization and parametrization of sinusoidal networks via their kernel bandwidth [92.25666446274188]
  sinusoidal neural networks with activations have been proposed as an alternative to networks with traditional activation functions. We first propose a simplified version of such sinusoidal neural networks, which allows both for easier practical implementation and simpler theoretical analysis. We then analyze the behavior of these networks from the neural tangent kernel perspective and demonstrate that their kernel approximates a low-pass filter with an adjustable bandwidth.
  arXiv  Detail & Related papers  (2022-11-26T07:41:48Z)
• Spiking Neural Networks for Frame-based and Event-based Single Object Localization [26.51843464087218]
  Spiking neural networks have shown much promise as an energy-efficient alternative to artificial neural networks. We propose a spiking neural network approach for single object localization trained using surrogate gradient descent. We compare our method with similar artificial neural networks and show that our model has competitive/better performance in accuracy, against various corruptions, and has lower energy consumption.
  arXiv  Detail & Related papers  (2022-06-13T22:22:32Z)
• Noise mitigation strategies in physical feedforward neural networks [0.0]
  Physical neural networks are promising candidates for next generation artificial intelligence hardware. We introduce connectivity topologies, ghost neurons as well as pooling as noise mitigation strategies. We demonstrate the effectiveness of the combined methods based on a fully trained neural network classifying the MNIST handwritten digits.
  arXiv  Detail & Related papers  (2022-04-20T13:51:46Z)
• Physical Constraint Embedded Neural Networks for inference and noise regulation [0.0]
  We present methods for embedding even--odd symmetries and conservation laws in neural networks. We demonstrate that it can accurately infer symmetries without prior knowledge. We highlight the noise resilient properties of physical constraint embedded neural networks.
  arXiv  Detail & Related papers  (2021-05-19T14:07:20Z)
• Multi-Tones' Phase Coding (MTPC) of Interaural Time Difference by Spiking Neural Network [68.43026108936029]
  We propose a pure spiking neural network (SNN) based computational model for precise sound localization in the noisy real-world environment. We implement this algorithm in a real-time robotic system with a microphone array. The experiment results show a mean error azimuth of 13 degrees, which surpasses the accuracy of the other biologically plausible neuromorphic approach for sound source localization.
  arXiv  Detail & Related papers  (2020-07-07T08:22:56Z)
• AutoSpeech: Neural Architecture Search for Speaker Recognition [108.69505815793028]
  We propose the first neural architecture search approach approach for the speaker recognition tasks, named as AutoSpeech. Our algorithm first identifies the optimal operation combination in a neural cell and then derives a CNN model by stacking the neural cell for multiple times. Results demonstrate that the derived CNN architectures significantly outperform current speaker recognition systems based on VGG-M, ResNet-18, and ResNet-34 back-bones, while enjoying lower model complexity.
  arXiv  Detail & Related papers  (2020-05-07T02:53:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.