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Perceiving Loudness, Pitch, and Location01:21

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The human brain perceives pitch through two primary mechanisms reflected in place theory and frequency theory. Each mechanism describes how sound waves are interpreted as specific pitches by the brain, offering insights into the intricate processes of auditory perception.
Place theory, or place coding, suggests that different pitches are heard because various sound waves activate specific locations along the cochlea's basilar membrane. The brain determines the pitch of a sound by...
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Related Experiment Video

Updated: May 28, 2025

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
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Low-power Spiking Neural Network audio source localisation using a Hilbert Transform audio event encoding scheme.

Saeid Haghighatshoar1, Dylan Richard Muir2

  • 1SynSense, Zürich, Switzerland.

Communications Engineering
|February 11, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient sound source localization method using a Hilbert transform and event-based encoding for ultra-low-power spiking neural networks (SNNs). This approach achieves high accuracy while significantly reducing power consumption for IoT devices.

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Area of Science:

  • Signal Processing
  • Computational Neuroscience
  • Acoustics

Background:

  • Sound source localization is crucial for noise reduction in consumer electronics.
  • Traditional beamforming methods require dense filters and are computationally intensive, limiting their use in low-power devices.
  • Spiking neural networks (SNNs) offer potential for efficient audio processing but require specialized algorithms.

Purpose of the Study:

  • To develop an efficient sound source localization method suitable for ultra-low-power spiking neural networks (SNNs).
  • To reduce the computational demands of traditional beamforming techniques.
  • To enable high-accuracy audio source separation in power-constrained Internet of Things (IoT) devices.

Main Methods:

  • Utilized a Hilbert transform to process wideband audio without dense band-pass filters.
  • Developed an event-based encoding method to capture the phase of the complex analytic signal.
  • Implemented the localization method on arbitrary microphone arrays for efficient SNN processing.

Main Results:

  • Achieved high accuracy in sound source localization comparable to traditional super-resolution beamforming methods.
  • Demonstrated significantly lower power consumption when deployed on low-power SNN inference hardware.
  • Validated the effectiveness of co-designing signal processing with SNNs for improved power efficiency.

Conclusions:

  • The proposed Hilbert-transform-based beamforming method offers a computationally efficient and accurate solution for sound source localization.
  • This approach is well-suited for ultra-low-power SNNs, enabling advanced audio processing in edge devices.
  • The method also shows potential for enhancing the efficiency of traditional digital signal processing.