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Related Concept Videos

Perception of Sound Waves01:01

Perception of Sound Waves

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The human ear is not equally sensitive to all frequencies in the audible range. It may perceive sound waves with the same pressure but different frequencies as having different loudness. Moreover, the perception of sound waves depends on the health of an individual's ears, which decays with age. The health of one's ears may also be affected by regular exposure to loud noises.
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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.
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Auditory Perception01:17

Auditory Perception

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The auditory system is essential for sound perception, utilizing various critical structures. When sound waves enter the outer ear, they travel through the ear canal and cause the eardrum to vibrate. These vibrations are then transmitted to the middle ear, where three tiny bones – the malleus, incus, and stapes – amplify the sound. This amplification is crucial, as it ensures that the sound vibrations are strong enough to be conveyed to the inner ear. These vibrations then reach the...
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Sensory Modalities01:15

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Sensation typically is the process by which the sensory receptors and sense organs detect stimuli from the internal and external environment and transmit this information to the central nervous system for processing.
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Hearing

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When we hear a sound, our nervous system is detecting sound waves—pressure waves of mechanical energy traveling through a medium. The frequency of the wave is perceived as pitch, while the amplitude is perceived as loudness.
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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Multimodal Environmental Sensing Using AI & IoT Solutions: A Cognitive Sound Analysis Perspective.

Alexandros Emvoliadis1, Nikolaos Vryzas1, Marina-Eirini Stamatiadou1

  • 1Multidisciplinary Media & Mediated Communication Research Group (M3C), Aristotle University, 54636 Thessaloniki, Greece.

Sensors (Basel, Switzerland)
|May 11, 2024
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Summary

This study introduces efficient audio compression for environmental monitoring, significantly reducing data rates for edge devices without compromising air pollution analysis accuracy. The method balances low bit rates with reliable data reconstruction and classification.

Keywords:
Internet of Thingsaudio encodingdeep learningenvironmental monitoringenvironmental sound classificationmulti-modal sensingresource-constrained environments

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Area of Science:

  • Environmental Science
  • Data Science
  • Signal Processing

Background:

  • Audio data is vital for environmental monitoring, but high bit rates pose challenges in resource-limited settings.
  • Existing compression methods may sacrifice accuracy or incur high processing costs.
  • Multi-modal data processing pipelines require efficient handling of diverse data types, including audio.

Purpose of the Study:

  • To develop a novel audio compression technique for environmental monitoring.
  • To enable efficient data transfer and storage under resource constraints.
  • To maintain accuracy for air pollution analysis while minimizing costs.

Main Methods:

  • A hybrid approach combining a Deep-Learning model optimized for edge devices with conventional audio coding.
  • Data compression at the edge, followed by cloud-based decoding, reconstruction, and classification.
  • Focus on reducing bit rates for efficient transmission.

Main Results:

  • Substantial decrease in bit rates achieved.
  • Minor reduction in accuracy for air pollution analysis, even at very low bit rates.
  • Demonstrated robustness in classifying data outside the training set.

Conclusions:

  • The proposed audio compression technique is effective for resource-constrained environmental monitoring.
  • The method offers a practical solution for efficient multi-modal data processing.
  • Accurate environmental analysis is achievable with significantly reduced data handling requirements.