The conservation of bird biodiversity relies on accurately identifying and classifying species, which is often time-consuming and requires specialized knowledge. Recent advances in deep learning, particularly in convolutional neural networks (CNNs), have made it possible to detect species passively from acoustic signals, even in challenging environments. This paper presents a high-performance deep convolutional neural network (CNN) model using the VGG-16 architecture for the passive classification of bird sounds, using a remarkably accurate model of Short-Time Fourier Transform (STFT) that accounts for 97.31% of the BirdCLEF 2022 dataset and 98.41% for the Cornell Birdcall Identification dataset. The model discriminates between species, even in complex soundscapes with overlapping records. The framework also uses a tool-based consensus framework to enhance the focus on relevant features, improving classification accuracy for rare and endangered species. This method is highly effective in various phonological and language processing tasks and enhances the model’s robustness, making it suitable for real-world applications.