Our paper “A Dataset and Taxonomy for Urban Sound 
Research” has been accepted for publication at the 22nd ACM International Conference on Multimedia (ACM-MM’14).
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 au- dio 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 full paper is available on the Publications page. You may also wish to visit the UrbanSound dataset companion website.