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Related Concept Videos

Auditory Perception01:17

Auditory Perception

288
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...
288
Perceiving Loudness, Pitch, and Location01:21

Perceiving Loudness, Pitch, and Location

167
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...
167
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

483
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
483
Perception of Sound Waves01:01

Perception of Sound Waves

4.4K
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...
4.4K
Factors Affecting Perception01:25

Factors Affecting Perception

1.4K
Perception is influenced by perceptual set, context, motivation, and emotion. Perceptual set, or perceptual expectancy, refers to the tendency to perceive things in a particular way, influenced by previous experiences and expectations. This phenomenon affects the interpretation of stimuli, creating a set of mental tendencies and assumptions that impact sensory perceptions of sound, taste, touch, and sight.
An illustrative example of a perceptual set is the scenario where an airline pilot told...
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Related Experiment Video

Updated: May 16, 2025

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
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How Does Audio Influence Visual Attention in Omnidirectional Videos? Database and Model.

Yuxin Zhu, Huiyu Duan, Kaiwei Zhang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |May 14, 2025
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    Summary

    This study introduces a new audio-visual saliency database for omnidirectional videos (ODVs) and a novel prediction network (OmniAVS). The findings show that audio significantly influences visual attention in ODVs, improving prediction accuracy.

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    Area of Science:

    • Computer Vision
    • Human-Computer Interaction
    • Multimedia Systems

    Background:

    • Viewer attention in omnidirectional videos (ODVs) is vital for virtual and augmented reality engagement.
    • Limited research exists on audio-visual saliency prediction in ODVs due to a lack of large-scale datasets and analyses.
    • Existing models often fail to effectively integrate audio and visual cues for ODV saliency.

    Purpose of the Study:

    • To investigate audio-visual attention in ODVs from subjective and objective viewpoints.
    • To introduce a novel audio-visual saliency database for ODVs (AVS-ODV) and analyze audio's influence on visual attention.
    • To develop an advanced audio-visual saliency prediction model for ODVs.

    Main Methods:

    • Collected eye-tracking data from 60 subjects across 162 ODVs under varying audio conditions (mute, mono, ambisonics) to create the AVS-ODV database.
    • Analyzed the impact of audio on visual attention within ODVs using the new database.
    • Developed and evaluated the OmniAVS network, a U-Net based model, for hierarchical audio-visual feature fusion.

    Main Results:

    • The AVS-ODV database provides a comprehensive resource for studying audio-visual attention in ODVs.
    • Audio significantly influences visual attention patterns in ODVs.
    • The proposed OmniAVS model achieved superior performance compared to state-of-the-art models in ODV and traditional AVS prediction tasks.

    Conclusions:

    • The AVS-ODV database and OmniAVS model represent significant advancements in audio-visual saliency prediction for ODVs.
    • Effective fusion of audio and visual information is key to accurate attention prediction in immersive environments.
    • This research paves the way for more engaging and realistic virtual and augmented reality experiences.