Modern ecological monitoring needs long-term, non-invasive observation of wildlife in remote areas, but traditional methods either store raw audio for offline analysis or depend on power-hungry, networked infrastructure. This creates three problems: high energy cost, high data volume and no real-time awareness in the field. This thesis presents ChirpAlert, a fully autonomous low-power acoustic sensor platform that records and classifies environmental sounds directly on embedded hardware. The system is built around a custom low-cost embedded platform node with an analog, ultrasonic capable microphone front-end, on-device machine learning inference, environmental sensing, GPS and long-range LoRa connectivity for transmitting live alerts. ChirpAlert runs an optimized audio event detection pipeline that converts raw audio into Mel-based time-frequency features and applies a quantized neural network for bird vocalization detection on the sensor itself. A training and deployment framework is introduced that automatically assembles a task-specific dataset for a target species and region, performs hyperparameter and preprocessing optimization and converts the trained model to be capable of running on embedded hardware. The final platform operates at approximately 56 mW active power while running on-device inference, enabling long-term deployment. Field tests confirm that ChirpAlert can recognize target bird sounds with 88.8% accuracy during deployment while correctly labeling 98.1% of the other sounds as not the target species, demonstrating the effectiveness of the proposed ChirpAlert hardware and audio event detection pipeline.