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Using a Low-Power Spiking Continuous Time Neuron (SCTN) for Sound Signal Processing.

Moshe Bensimon1, Shlomo Greenberg1, Moshe Haiut2

  • 1School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beersheba 8400711, Israel.

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Summary

This study introduces a biologically inspired spiking neural network (SNN) for efficient sound classification. The novel approach integrates preprocessing and achieves 98.73% accuracy using low-power analog circuits.

Keywords:
LIF modelMFCCSCTNSNNSTDP learning ruledigital neuronsound feature extractionspiking neuron

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Area of Science:

  • Neuroscience and Artificial Intelligence
  • Analog Circuit Design
  • Signal Processing

Background:

  • Traditional sound classification often involves complex digital-to-analog conversions.
  • Spiking Neural Networks (SNNs) offer a biologically plausible alternative for signal processing.
  • Low-power analog implementations are crucial for efficient hardware.

Purpose of the Study:

  • To develop a novel, biologically inspired sound classification framework using SNNs.
  • To demonstrate an efficient hardware implementation of the SNN for sound analysis.
  • To integrate sound preprocessing directly into the SNN, reducing conversion costs.

Main Methods:

  • Utilized spiking neurons and Spike-Timing-Dependent Plasticity (STDP) for learning.
  • Implemented a low-power Spike Continuous Time Neuron (SCTN) for hardware realization.
  • Employed direct Pulse Density Modulation (PDM) interfacing for acoustic sensors.
  • Designed novel connectivity approaches for SNNs, treating SCTN as a modular analog building block.

Main Results:

  • Achieved a high sound classification accuracy of 98.73% on the RWCP database.
  • Demonstrated efficient sound feature extraction and classification using SCTN-based resonators.
  • Successfully integrated preprocessing within the SNN, bypassing analog-to-digital conversion.

Conclusions:

  • The proposed SCTN-based SNN offers an efficient and accurate solution for sound classification.
  • Biologically inspired SNNs with novel connectivity can enable the design of simple, low-power analog circuits.
  • This approach reduces hardware complexity and power consumption in acoustic signal processing.