· 2017
Audio Set: An ontology and human-labeled dataset for audio events
- 3,003citations
- 2017year
Short summary
A new large-scale dataset, Audio Set, has been created with 632 audio event classes, manually annotated from YouTube videos, to address the data gap in audio event recognition research.
AI-generated from the title and abstract; the full text is not read.
Key points
- Introduced Audio Set, a large-scale dataset for audio event recognition.
- Dataset comprises 632 audio classes organized in a hierarchical ontology.
- Data was collected via human annotation of 10-second YouTube video segments.
- Aims to bridge the data availability gap compared to image recognition datasets like ImageNet.
AI-generated from the title and abstract; the full text is not read.
Abstract
Audio event recognition, the human-like ability to identify and relate sounds from audio, is a nascent problem in machine perception. Comparable problems such as object detection in images have reaped enormous benefits from comprehensive datasets - principally ImageNet. This paper describes the creation of Audio Set, a large-scale dataset of manually-annotated audio events that endeavors to bridge the gap in data availability between image and audio research. Using a carefully structured hierarchical ontology of 632 audio classes guided by the literature and manual curation, we collect data from human labelers to probe the presence of specific audio classes in 10 second segments of YouTube videos. Segments are proposed for labeling using searches based on metadata, context (e.g., links), and content analysis. The result is a dataset of unprecedented breadth and size that will, we hope, substantially stimulate the development of high-performance audio event recognizers.
The authors' abstract, as published at the source., 2017 · DOI ↗
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Field: Signal Processing
Signal ProcessingComputer Science