Bird species identification is a fundamental task in ornithology research; it can sometimes be a challenging and time-consuming process, particularly when dealing with large collections of audio signals. In this chapter, we propose a new visual analytic mechanism based on audio signal processing, machine learning, and visualization techniques to facilitate the identification of bird species. Our proposed mechanism involves a two-stage identification process. In the first stage, we constructed an ideal dataset of sound recordings of different bird species, which were subjected to various sound preprocessing techniques such as pre-emphasis, framing, silence removal, and reconstruction. Spectrograms were generated for each reconstructed sound clip, which served as input features for the second stage.