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Related Concept Videos

Korotkoff Sounds01:12

Korotkoff Sounds

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Korotkoff sounds are the specific sounds heard while measuring blood pressure using a sphygmomanometer, typically with a stethoscope or a Doppler device. They are named after Russian physician Nikolai Korotkov, who first described them in 1905. These sounds correspond to turbulent blood flow in the artery as the blood pressure cuff is gradually released after inflation.
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The soundness of cement refers to the ability of cement paste to retain its volume after setting. Unsound cement can lead to expansion and structural damage due to the presence of free lime, magnesia, and calcium sulfate. Free lime hydrates very slowly, expanding and causing unsoundness, which is difficult to detect because it intercrystallizes with other compounds. Magnesia also reacts with water, forming crystals that can disrupt the cement's structure. Calcium sulfate can create...
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Sound Waves01:01

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Sound waves can be thought of as fluctuations in the pressure of a medium through which they propagate. Since the pressure also makes the medium's particles vibrate along its direction of motion, the waves can be modeled as the displacement of the medium's particles from their mean position.
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Sound Intensity00:58

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The loudness of a sound source is related to how energetically the source is vibrating, consequently making the molecules of the propagation medium vibrate. To measure the loudness of a source, the physical quantity of interest is the intensity. This is defined as the energy emitted per unit of time per unit of area perpendicular to the sound wave's propagation direction. Since the total energy is greater if the source vibrates for a longer duration and over a larger area, dividing the...
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Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution
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A Spiking Neural Network Framework for Robust Sound Classification.

Jibin Wu1, Yansong Chua2, Malu Zhang1

  • 1Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.

Frontiers in Neuroscience
|December 5, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a low-power automatic sound classification (ASC) framework using self-organizing maps (SOM) and spiking neural networks (SNN). The biologically inspired SOM-SNN model achieves competitive accuracy and early decision-making for efficient audio analysis.

Keywords:
automatic sound classificationmaximum-margin Tempotron classifiernoise robust multi-condition trainingself-organizing mapspiking neural network

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Area of Science:

  • Artificial Intelligence
  • Computational Neuroscience
  • Signal Processing

Background:

  • Automatic sound classification (ASC) systems have advanced with deep learning but face high computational costs and power consumption challenges for mobile deployment.
  • Human auditory systems exhibit efficient, low-power analysis of complex acoustic scenes, offering a biological inspiration for ASC.
  • Existing ASC models often lack robustness to noise and efficient early decision-making capabilities.

Purpose of the Study:

  • To propose a biologically plausible and power-efficient ASC framework inspired by human auditory processing.
  • To develop a novel framework combining self-organizing maps (SOM) for feature representation and spiking neural networks (SNN) for classification.
  • To evaluate the performance, robustness, and early decision-making capabilities of the proposed SOM-SNN framework.

Main Methods:

  • The proposed SOM-SNN framework utilizes unsupervised self-organizing maps (SOM) to represent frequency content in acoustic signals.
  • An event-based spiking neural network (SNN) is employed for spatiotemporal spiking pattern classification following SOM processing.
  • The framework was evaluated on the RWCP environmental sound and TIDIGITS spoken digits datasets, including multi-condition training for noise robustness.

Main Results:

  • The SOM-SNN framework achieved competitive classification accuracies compared to other deep learning and SNN-based models.
  • The model demonstrated high robustness to corrupting noise when trained under multi-condition settings.
  • The framework exhibited early decision-making capabilities, enabling accurate classification from partial input presentation.

Conclusions:

  • The SOM-SNN framework offers a power-efficient and biologically plausible approach to automatic sound classification.
  • The proposed method achieves strong performance, noise robustness, and efficient early decision-making, suitable for mobile and wearable devices.
  • This research highlights the potential of integrating SOM and SNN for advanced, low-power audio analysis systems.