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Sound as Pressure Waves01:17

Sound as Pressure Waves

2.6K
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.
The pressure fluctuation depends on the difference in displacements between the successive points in the...
2.6K
Sound Intensity00:58

Sound Intensity

4.2K
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...
4.2K
Heart Sounds01:15

Heart Sounds

2.3K
Heart sounds are generated by the turbulence in blood flow due to the closing of heart valves. These sounds are best perceived slightly away from the valves, where the blood flow disseminates the sound.
Auscultation is the process of listening to these internal body sounds using a stethoscope. The heart produces four types of sounds, but only two—S1 and S2—can usually be heard with a stethoscope.
S1, also known as the "lub" sound, is caused by the closure of atrioventricular (A-V)...
2.3K
Perception of Sound Waves01:01

Perception of Sound Waves

4.7K
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...
4.7K
Sound Intensity Level00:53

Sound Intensity Level

4.3K
Humans perceive sound by hearing. The human ear helps sound waves reach the brain, which then interprets the waves and creates the perception of hearing. The loudness of the environment in which a person is located determines whether they can distinguish between different sound sources.
The human ear can perceive an extensive range of sound intensity, necessitating the use of the logarithmic scale to define a physical quantity—the intensity level. It is a ratio of two intensities and...
4.3K
Sound Waves01:01

Sound Waves

9.5K
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.
Sound waves are longitudinal in most fluids because fluids cannot sustain any lateral pressure. In solids, however, shear forces help in propagating the disturbance in the lateral direction as well....
9.5K

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Related Experiment Video

Updated: Sep 14, 2025

Assessment of Audio-Tactile Sensory Substitution Training in Participants with Profound Deafness Using the Event-Related Potential Technique
11:39

Assessment of Audio-Tactile Sensory Substitution Training in Participants with Profound Deafness Using the Event-Related Potential Technique

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EPIC-SOUNDS: A Large-Scale Dataset of Actions That Sound.

Jaesung Huh, Jacob Chalk, Evangelos Kazakos

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |July 18, 2025
    PubMed
    Summary
    This summary is machine-generated.

    We introduce EPIC-SOUNDS, a large dataset for audio event and action recognition in egocentric videos. This dataset aids in developing models that understand actions purely from sound, advancing audio-visual research.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Audio Signal Processing

    Background:

    • Egocentric videos offer rich, first-person perspectives.
    • Understanding actions from audio cues is a challenging but crucial area in AI.

    Purpose of the Study:

    • Introduce EPIC-SOUNDS, a large-scale dataset for egocentric audio-visual action recognition.
    • Provide temporal annotations and class labels for audio events and associated actions.
    • Facilitate research into audio-only and audio-visual action understanding.

    Main Methods:

    • Developed an annotation pipeline for temporal labeling of audio segments and action descriptions.
    • Grouped free-form audio descriptions into 44 distinct action classes.
    • Collected and verified material annotations for object-collision sounds.

    Main Results:

    • EPIC-SOUNDS contains 78.4k categorized and 39.2k non-categorized audio segments.
    • Evaluated state-of-the-art audio recognition and detection models on the dataset.
    • Analyzed temporal correlations, modality interactions, and model limitations.

    Conclusions:

    • EPIC-SOUNDS enables robust training and evaluation of audio-based action recognition models.
    • Highlights the potential and limitations of current models in understanding sound-driven actions.
    • Provides a valuable resource for advancing audio-visual perception research.