Abstract

People's behaviors are usually dictated by their surroundings. The surrounding environment affects the character and disposition of the people within it. The goal of our work is to automatically recognize the type of environments one is in. In this paper, we introduce a hierarchical structure to recognize environments using the surrounding audio. We can use this structure to discover high-level representations for different acoustic environments in a data-driven fashion. Being able to perform such function would allow us to better understand how we could utilize such information to assist in predicting a person's emotion or behavior. To accurately make an informative decision about behaviors or emotions, it is important to have the ability to differentiate between different types of environments. Environmental sound contains large variances even within a single environment and is constantly changing. These changes and …

Date

2012

Authors

Selina Chu, Shrikanth Narayanan, C-C Jay Kuo

Conference

Proceedings of The 2012 Asia Pacific Signal and Information Processing Association Annual Summit and Conference

Pages

1-4

Publisher

IEEE