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.

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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.

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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

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