Abstract: In this study, we present a novel approach for recognizing bird species using Fast Fourier Transform (FFT) analysis of audio spectra. Traditional bird recognition methods often rely on complex feature extraction techniques and machine learning algorithms. Our method simplifies this process by leveraging the FFT to convert audio signals into frequency domain representations, which are then analyzed to identify distinct spectral patterns associated with different bird species.

We employ FFT to transform recorded bird songs into frequency spectra, which are then used to generate a comprehensive audio fingerprint for each species. This approach enables us to capture the unique frequency characteristics and temporal variations of bird calls with high precision. By comparing these fingerprints with a pre-established database of known bird calls, we are able to classify and recognize bird species with high accuracy.

Our system is tested across various environments and recording conditions, demonstrating robustness and reliability. The results indicate that FFT-based audio spectrum analysis is a powerful tool for avian acoustic monitoring and can be integrated into real-time bird recognition applications. This method not only streamlines the recognition process but also enhances the scalability and accessibility of avian monitoring systems, making it a valuable contribution to ornithology and bioacoustics research.

Keywords: Fast Fourier Transform (FFT), audio spectrum analysis, bird recognition, acoustic monitoring, avian bioacoustics.

| DOI: 10.17148/IARJSET.2024.11778