AI-Based Bird Sound Detection and Classification Using Deep Learning

Abstract

Automated monitoring of bird species through acoustic analysis has gained significant importance in biodiversity conservation and ecological research. Traditional bird sound identification methods rely heavily on manual inspection of large audio datasets, which is time-consuming and inefficient, especially in noisy real-world environments. This paper presents an AI-based bird sound detection and classification system using deep learning techniques to accurately identify bird species from environmental audio recordings. The proposed approach follows a two-stage framework. In the first stage, a binary deep learning model detects the presence of bird vocalizations and filters out non-bird sounds such as wind, traffic, and human noise. In the second stage, the detected bird sound segments are transformed into spectrogram representations and classified into specific bird species using a convolutional neural network with transfer learning. This staged architecture reduces unnecessary processing of irrelevant audio segments and improves robustness in noisy conditions. Experimental evaluation demonstrates improved performance compared to single-stage classification systems, making the proposed system suitable for real-world wildlife monitoring and biodiversity assessment applications.