Work

Real-Time Bird Audio Detection with a CNN-RNN Model on a SoC-FPGA

Year: 2026

Type: article

Abstract: Monitoring wildlife has become increasingly important for understanding the evolution of species and ecosystem health. Acoustic monitoring offers several advantages over video-based approaches, enabling continuous 24/7 observation and robust detection under challenging environmental conditions. Deep learning models have demonstrated strong performance in audio classification. However, their computational complexity poses significant challenges for deployment on low-power embedded platforms. This paper presents a low-power embedded system for real-time bird audio detection. A hybrid CNN–RNN architecture is adopted, redesigned, and quantized to significantly reduce model complexity while preserving classification accuracy. To support efficient execution, a custom hardware accelerator was developed and integrated into a Zynq UltraScale+ ZU3CG FPGA. The proposed system achieves an accuracy of 87.4%, processes up to 5 audio samples per second, and operates at only 1.4 W, demonstrating its suitability for autonomous, energy-efficient wildlife monitoring applications.

Source: Electronics

DOI: 10.3390/electronics15020354

Authors: Rodrigo Lopes da Silva, Gustavo Jacinto, Mário Véstias, Rui Policarpo Duarte

Institutions: Universidade Politécnica de Lisboa, Instituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento, Instituto de Novas Tecnologias, Institute for Systems Engineering and Computers

Language: English

Topic: Music and Audio Processing

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: Fundação para a Ciência e a Tecnologia