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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.
The pitch of a sound depends on the frequency and the pressure amplitude of the source. Two sounds of the same...
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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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Sound as Pressure Waves01:17

Sound as Pressure Waves

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Sound waves, which are longitudinal waves, can be modeled as the displacement amplitude varying as a function of the spatial and temporal coordinates. As a column of the medium is displaced, its successive columns are also displaced. As the successive displacements differ relatively, a pressure difference with the surrounding pressure is created. The gauge pressure varies across the medium.
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Intensity and Pressure of Sound Waves01:05

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The intensity of sound waves can be related to displacement and pressure amplitudes by using their wave expressions and the definition of intensity. The critical step to achieve this is to write the power delivered by the particles on the wave as the product of force and velocity and simplify the force per unit area as the pressure. The velocity of the medium's particles can be derived from the displacement.
Unlike the time average of a sinusoidal term, which is zero since it is positive...
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Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
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Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

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Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
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Updated: Mar 7, 2026

A Method to Study Adaptation to Left-Right Reversed Audition
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Physics-aware graph learning for sound-field reconstruction from sparse measurements.

Fangchao Chen1, Youhong Xiao1, Liang Yu2,3,4

  • 1College of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, China.

The Journal of the Acoustical Society of America
|March 6, 2026
PubMed
Summary

A novel graph neural network (GNN) framework accurately reconstructs room acoustics sound fields from limited microphone data. This physics-informed approach offers superior performance compared to traditional methods, especially in challenging sparse sampling scenarios.

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Area of Science:

  • Acoustics
  • Machine Learning
  • Signal Processing

Background:

  • Reconstructing sound fields from sparse measurements is crucial for applications like virtual acoustics and audio source localization.
  • Traditional methods often struggle with accuracy and robustness under sparse sampling and high frequencies.
  • Graph neural networks (GNNs) offer a promising avenue for learning complex spatial relationships in acoustic data.

Purpose of the Study:

  • To develop and evaluate a GNN framework for accurate room-acoustic sound field reconstruction.
  • To incorporate geometric priors and physics-aware features into the GNN for improved performance.
  • To compare the GNN approach against established methods like cylindrical harmonics and plane wave expansion.

Main Methods:

  • Representing microphones, sources, and field points as a graph.
  • Utilizing node and edge embeddings to encode geometric and wave propagation information.
  • Employing a principal neighbourhood aggregation architecture for message passing and acoustic pressure estimation.

Main Results:

  • The GNN framework demonstrated robust sound field reconstruction across various sampling densities and frequencies.
  • The GNN consistently achieved lower reconstruction error and higher spatial correlation than traditional methods.
  • Performance gains were most significant under very sparse sampling and at higher frequencies.

Conclusions:

  • Graph-based learning, incorporating geometric and physics-aware representations, is an effective approach for sound field reconstruction.
  • The proposed GNN framework provides a physically consistent and accurate solution for room acoustics.
  • This method shows potential for advancing applications requiring detailed acoustic environment modeling.