An Always-On tinyML Acoustic Classifier for Ecological Applications

Long-term monitoring and tracking of wildlife and endangered species in their natural environment is challenging due to human factors and logistical limitations. We present a light-weight, always-on acoustic classification system that can identify the density of specific wildlife species in an ecological environment where human presence may be undesirable. The system uses a template-based support-vector-machine (SVM) classifier that combines acoustic filtering and classification into an in-filter computing and a hardware-friendly platform. We demonstrate the system's capabilities for identifying the density of different bird species using ARM Cortex-M4 based AudioMoth hardware. The embedded software, designed specifically for the AudioMoth hardware, can generate the programmable parameters, given limited training samples corresponding to different wildlife species. We show that the system can identify four different bird species with an accuracy of more than 95% and consumes a memory footprint of 14 KB SRAM and 149 KB Flash memory that can run for 48 days on battery without any human intervention.