Automated detection of broadband clicks of freshwater fish using spectro-temporal features.

Large scale networks of embedded wireless sensor nodes can passively capture sound for species detection. However, the acoustic recordings result in large amounts of data requiring in-network classification for such systems to be feasible. The current state of the art in the area of in-network bioacoustics classification targets narrowband or long-duration signals, which render it unsuitable for detecting species that emit impulsive broadband signals.

Autonomous surveillance for biosecurity.

The global movement of people and goods has increased the risk of biosecurity threats and their potential to incur large economic, social, and environmental costs. Conventional manual biosecurity surveillance methods are limited by their scalability in space and time. This article focuses on autonomous surveillance systems, comprising sensor networks, robots, and intelligent algorithms, and their applicability to biosecurity threats.

Using feature vectors to detect frog calls in wireless sensor networks.

A method for detecting vocalization of giant barred frogs (Mixophyes iteratus) in noisy audio is proposed. Audio recordings from remote wireless sensor nodes were segmented into individual sounds and from each sound a small set of features was extracted. Feature vectors were compared to those of example calls using a Euclidean distance formula as a detection system.