A 30 μW Embedded Real-Time Cetacean Smart Detector

Abstract

Cetacean monitoring is key to their protection. Understanding their behavior relies on multi-channel and high-sampling-rate underwater acoustic recordings for identifying and tracking them in a passive way. However, a lot of energy and data storage is required, requiring frequent human maintenance operations. To cope with these constraints, an ultra-low power mixed-signal always-on wake-up is proposed. Based on pulse-pattern analysis, it can be used for triggering a multi-channel high-performance recorder only when cetacean clicks are detected, thus increasing autonomy and saving storage space. This detector is implemented as a mixed architecture making the most of analog and digital primitives: this combination drastically improves power consumption by processing high-frequency data using analog features and lower-frequency ones in a digital way. Furthermore, a bioacoustic expert system is proposed for improving detection accuracy (in ultra-low-power) via state machines. Power consumption of the system is lower than 30 μW in always-on mode, allowing an autonomy of 2 years on a single CR2032 battery cell with a high detection accuracy. The receiver operating characteristic (ROC) curve obtained has an area under curve of 85% using expert rules and 75% without it. This implementation provides an excellent trade-off between detection accuracy and power consumption. Focused on sperm whales, it can be tuned to detect other species emitting pulse trains. This approach facilitates biodiversity studies, reducing maintenance operations and allowing the use of lighter, more compact and portable recording equipment, as large batteries are no longer required. Additionally, recording only useful data helps to reduce the dataset labeling time.

Keywords

• embedded artificial intelligence
• ultra-low power
• pattern detection
• expert system
• biosonar
• always-on wake-up