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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...
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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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Summary

The You Described, We Archived (YuWA) dataset offers valuable audio description data for AI and machine learning research. This collection supports advancements in video understanding and accessibility for visually impaired individuals.

Keywords:
Artificial IntelligenceAudio DescriptionBlind and Low VisionMachine LearningSociolinguisticsVideo Accessibility

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Area of Science:

  • Computer Science
  • Human-Computer Interaction
  • Linguistics

Background:

  • The You Described, We Archived (YuWA) dataset is a unique collection of audio description (AD) data.
  • It was compiled through a collaboration between San Francisco State University and The Smith-Kettlewell Eye Research Institute.
  • Data spans from 2013-2022, sourced globally via the YouDescribe platform.

Purpose of the Study:

  • To present the You Described, We Archived (YuWA) dataset for research applications.
  • To highlight the utility of crowd-sourced audio descriptions for various AI and computational linguistics tasks.
  • To provide a rich resource for studying video understanding and accessibility.

Main Methods:

  • The YuWA dataset comprises audio description data collected via the YouDescribe platform.
  • YouDescribe facilitates user-generated audio descriptions for YouTube videos, involving volunteer describers and requests from blind and visually impaired (BVI) users.
  • Data includes audio description tracks, metadata on describers and viewers, and collection timelines.

Main Results:

  • The YouDescribe platform has engaged over 3,000 volunteer describers, creating more than 5,500 audio-described videos.
  • The YuWA dataset contains worldwide audio description data from 2013-2022.
  • The dataset is publicly available at https://youdescribe.org/.

Conclusions:

  • The YuWA dataset offers significant potential for research in artificial intelligence, machine learning, and natural language processing.
  • It serves as a valuable resource for advancing audio description technologies and video-language grounding.
  • The dataset supports interdisciplinary research spanning computer science, accessibility, and sociolinguistics.