Prototype 0.0.1

In the early stages of developing Bird Song Logix, the primary focus was on creating a functional prototype that could process audio recordings and identify bird songs. I began by using BirdNETLib, a powerful library designed for birdsong identification, to bring the AI aspect to the project. The first step was integrating the Python-based BirdNET model into the system, which allowed me to analyse the audio files and identify bird species based on the sounds.

At this point, I wasn’t focused on graphing or complex data visualization. Instead, I relied on a straightforward approach—Excel. The results of the bird identification process were stored in a well-organized Excel file. Each recording was logged with its filename, bird species, occurrence details, and timestamps. Excel provided a simple way to track and review the data, even though it wasn’t the most efficient for scaling or handling large datasets.

During this phase, I was experimenting with different custom models and tweaking BirdNETLib to see if I could improve the accuracy of bird identification. I worked with various parameters, trying to find the best balance between sensitivity and confidence levels in the results. The Python file that powered the analysis became the backbone of the project, running the bird song recognition process for each recording.