· 2014
A Dataset and Taxonomy for Urban Sound Research
- 1,292citations
- 2014year
Short summary
A new dataset, UrbanSound, with 18.5 hours of annotated sound events across 10 classes and a proposed taxonomy address key barriers in urban sound classification research.
AI-generated from the title and abstract; the full text is not read.
Key points
- Presents the UrbanSound dataset with 27 hours of audio and 18.5 hours of annotated sound events across 10 classes.
- Introduces a taxonomy for urban sounds to standardize research.
- Addresses the lack of large, annotated real-world data as a barrier to urban sound classification.
- Evaluates a baseline classification system on the new dataset.
AI-generated from the title and abstract; the full text is not read.
Abstract
Automatic urban sound classification is a growing area of research with applications in multimedia retrieval and urban informatics. In this paper we identify two main barriers to research in this area - the lack of a common taxonomy and the scarceness of large, real-world, annotated data. To address these issues we present a taxonomy of urban sounds and a new dataset, UrbanSound, containing 27 hours of audio with 18.5 hours of annotated sound event occurrences across 10 sound classes. The challenges presented by the new dataset are studied through a series of experiments using a baseline classification system.
The authors' abstract, as published at the source., 2014 · DOI ↗
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Field: Signal Processing
Signal ProcessingComputer Science