CI-P: Planning for AudioNet: A New Community Infrastructure for Audio Annotations for Acoustic Event Identification

CI-P：规划 AudioNet：用于声学事件识别的音频注释的新社区基础设施

• 批准号: 1629990
• 负责人: Gerald Friedland
• 金额: $ 10万
• 依托单位: International Computer Science Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1629990&HistoricalAwards=false
• 关键词: CI; Planning; AudioNet; New; Community

This effort lays the groundwork for AudioNet, a public-domain corpus of audio labels for the nearly 800,000 videos in the open-access YFCC100M dataset. Audio information provides an important complement to visual information in the automatic analysis of video data, allowing systems to detect situations that may not be clearly identifiable from the visual stream alone. However, there are as yet no truly large-scale labeled audio datasets of the kind needed as input to build flexible, accurate analysis systems. Creating such a large-scale corpus will serve as an impetus for better multimedia algorithms to be developed by more researchers and computer science students, translating into an impact on the everyday life of the public at large. Social media videos are increasingly used for scientific research, as they provide an opportunity to observe and model many phenomena in the social sciences, economics, meteorology, and medicine. New capabilities for content analysis will therefore impact many scientific fields. In addition, audio analysis could be used in real-time security surveillance and in robotics applications like autonomous vehicles and household robots to aid and monitor the elderly.AudioNet is part of a multi-institution collaboration, the Multimedia Commons initiative, which is developing a variety of resources around the YFCC100M dataset of Creative Commons-licensed photos and videos. AudioNet is annotating the audio tracks from the YFCC100M videos, focusing on audio concepts. Audio concepts can be thought of as acoustic "objects": concrete, localizable units of sound like "crowd cheering" or "fire alarm". The approach will be modeled on ImageNet, an image dataset labeled and organized using the WordNet hierarchy of synsets (groups of synonyms); ImageNet has enabled major enabled advances in image processing. However, while ImageNet focuses largely on entities (noun synsets), audio data is inherently temporal. The label set for AudioNet will therefore focus on events and actions, though similarly organized using semantic resources like WordNet.

这项工作为Audionet奠定了基础，Audionet是公共域音频标签，用于开放式YFCC100M数据集中的近80万视频。音频信息在视频数据的自动分析中提供了对视觉信息的重要补充，从而使系统可以检测到可能仅在视觉流中明确识别的情况。但是，目前尚无真正大规模标记的音频数据集，这些音频数据集是构建灵活，准确的分析系统所需的输入。创建这样的大型语料库将成为更多的研究人员和计算机科学专业的学生开发更好多媒体算法的动力，转化为对整个公众的日常生活的影响。社交媒体视频越来越多地用于科学研究，因为它们为社会科学，经济学，气象和医学中的许多现象提供了机会。因此，用于内容分析的新功能将影响许多科学领域。此外，音频分析可用于实时安全性监视和机器人应用程序（例如自动驾驶汽车和家用机器人），以帮助和监视老年人。Audionet是多机构合作的一部分，即多媒体Commons Initiative，Multimedia Commons Initiative，该计划正在开发围绕YFCC100MCONSET COMPORONS COMLONS COMPORING PATICSET围绕创意的照片和视频的照片。 Audionet正在注释YFCC100M视频的音轨，重点是音频概念。音频概念可以被认为是声学的“对象”：混凝土，可本质的声音单位，例如“人群欢呼”或“火警警报”。该方法将在图像数据集上建模，该图像数据集使用Synset的WordNet层次结构（同义词组）进行了标记和组织； Imagenet已启用了图像处理方面的主要进步。但是，虽然Imagenet主要集中在实体（名词合成器）上，但音频数据本质上是时间的。因此，Audionet设置的标签将重点放在事件和动作上，尽管使用WordNet等语义资源也类似地组织。