Deep-Learning-Based detection of recreational vessels in an estuarine soundscape in the May River, South Carolina, USA.

Abstract

This paper presents a deep-learning-based method to detect recreational vessels. The method takes advantage of existing underwater acoustic measurements from an Estuarine Soundscape Observatory Network based in the estuaries of South Carolina (SC), USA. The detection method is a two-step searching method, called Deep Scanning (DS), which includes a time-domain energy analysis and a frequency-domain spectrum analysis. In the time domain, acoustic signals with higher energy, measured by sound pressure level (SPL), are labeled for the potential existence of moving vessels. In the frequency domain, the labeled acoustic signals are examined against a predefined training dataset using a neural network. This research builds training data using diverse vessel sound features obtained from real measurements, with a duration between 5.0 seconds and 7.5 seconds and a frequency between 800 Hz to 10,000 Hz. The proposed method was then evaluated using all acoustic data in the years 2017, 2018, and 2021, respectively; a total of approximately 171,262 2-minute.wav files at three deployed locations in May River, SC. The DS detections were compared to human-observed detections for each audio file and results showed the method was able to classify the existence of vessels, with an average accuracy of around 99.0%.