Oscillator-Based Processing Unit for Formant Recognition

Oscillatory neural networks have so far been successfully applied to a number of computing problems, such as associative memories, or to handle computationally hard tasks. In this paper, we show how to use oscillators to process time-dependent waveforms with minimal or no preprocessing. Since preprocessing and first-layer processing are often the most power-hungry steps in neural networks, our findings may open new doors to simple and power-efficient edge-AI devices.

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Published
2025-07-16
DOI
https://doi.org/10.3390/info16070611
Primary Topic
Neural Networks and Reservoir Computing
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article
Field-Weighted Citation Impact
0.00

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article

Oscillator-Based Processing Unit for Formant Recognition

György Csaba, Wolfgang Porod, Tamás Rudner-Halász
Information
Neural Networks and Reservoir Computing
article

Oscillator-Based Processing Unit for Formant Recognition

György Csaba, Wolfgang Porod, Tamás Rudner-Halász
article en

Abstract

Oscillatory neural networks have so far been successfully applied to a number of computing problems, such as associative memories, or to handle computationally hard tasks. In this paper, we show how to use oscillators to process time-dependent waveforms with minimal or no preprocessing. Since preprocessing and first-layer processing are often the most power-hungry steps in neural networks, our findings may open new doors to simple and power-efficient edge-AI devices.

InformationVol. 16(7)
University of Notre Dame (US), Pázmány Péter Catholic University (HU)
Intel Corporation, European Commission
Openalex Percentile: Top 91%
Neural Networks and Reservoir Computing
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