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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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Two key frameworks are employed to analyze mass, energy, and momentum transfer: the control volume approach and the system approach. These frameworks offer different perspectives, depending on whether the focus is on a specific region in space (control volume approach) or a defined mass of fluid (system approach).
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Information enters the brain through encoding, which is the input of information into the memory system. Once sensory information is received from the environment, the brain labels or codes it. The information is then organized with similar information and connected to existing concepts. Encoding occurs through automatic processing and effortful processing.
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Related Experiment Video

Updated: Oct 15, 2025

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
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Computer Vision System for Expressing Texture Using Sound-Symbolic Words.

Koichi Yamagata1, Jinhwan Kwon2, Takuya Kawashima1

  • 1Graduate School of Informatics and Engineering, The University of Electro Communications, Chofu, Japan.

Frontiers in Psychology
|October 25, 2021
PubMed
Summary

This study introduces a computer vision method using deep convolutional neural networks (DCNNs) to describe material textures. The system generates Japanese sound-symbolic words, achieving approximately 80% accuracy in texture description.

Keywords:
image databasesonomatopoeiasound-symbolic wordstactile sensationtexture

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Human-Computer Interaction
  • Natural Language Processing

Background:

  • Texture research in computer vision aims to model and process texture, simulating human visual perception.
  • Deep convolutional neural networks (DCNNs) have advanced material recognition but struggle with subjective texture description.
  • Human texture perception is nuanced and lacks definitive correct/incorrect answers, posing a challenge for AI.

Purpose of the Study:

  • To develop a novel computer vision method capable of expressing material textures.
  • To leverage Japanese sound-symbolic words, known for fine-grained texture sensation descriptions.
  • To bridge the gap between AI's objective analysis and human subjective experience of texture.

Main Methods:

  • Utilized deep convolutional neural networks (DCNNs) for image analysis.
  • Focused on Japanese sound-symbolic words and their phonemic structure for texture categorization.
  • Developed a method to generate sound-symbolic words probabilistically corresponding to image features.

Main Results:

  • The developed computer vision system successfully generated sound-symbolic words to describe material textures.
  • The generated sound-symbolic words achieved an approximate 80% accuracy rate in evaluations.
  • Demonstrated a novel approach to quantify and express subjective texture qualities using linguistic elements.

Conclusions:

  • The study presents a viable computer vision approach for expressing material texture using sound-symbolic language.
  • The findings suggest that linguistic elements, like sound-symbolic words, can capture nuanced sensory information.
  • This research opens new avenues for AI in understanding and communicating subjective sensory experiences.