Detection of rain in acoustic recordings of the environment

Ferroudj, Meriem, Truskinger, Anthony, Towsey, Michael, Zhang, Jinglan, Roe, Paul, & Zhang, Liang (2014) Detection of rain in acoustic recordings of the environment. In PRICAI 2014: Trends in Artificial Intelligence: 13th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2014, Proceedings [Lecture Notes in Computer Science, Volume 8862], Springer International Publishing Switzerland, Gold Coast, Australia, pp. 104-116.

View at publisher

Abstract

Environmental monitoring has become increasingly important due to the significant impact of human activities and climate change on biodiversity. Environmental sound sources such as rain and insect vocalizations are a rich and underexploited source of information in environmental audio recordings. This paper is concerned with the classification of rain within acoustic sensor re-cordings. We present the novel application of a set of features for classifying environmental acoustics: acoustic entropy, the acoustic complexity index, spectral cover, and background noise. In order to improve the performance of the rain classification system we automatically classify segments of environmental recordings into the classes of heavy rain or non-rain. A decision tree classifier is experientially compared with other classifiers. The experimental results show that our system is effective in classifying segments of environmental audio recordings with an accuracy of 93% for the binary classification of heavy rain/non-rain.

Impact and interest:

0 citations in Scopus
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

83 since deposited on 07 Oct 2014
17 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 76561
Item Type: Conference Paper
Refereed: No
Additional URLs:
Keywords: Audio classification, Audio features, Feature extraction, Feature selection, Environmental sound sources, Regression
DOI: 10.1007/978-3-319-13560-1_9
ISBN: 978-3-319-13559-5
Divisions: Past > Schools > Computer Science
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Deposited On: 07 Oct 2014 23:50
Last Modified: 08 May 2015 16:51

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page