Classification of bird species from chirping aids biodiversity monitoring, conservation, and ecological research. In our research work, we delve into the effectiveness and utility of Convolutional Neural Network (CNN) in this endeavour, incorporating with the layers of a well-established transfer learning model, InceptionResNetV2. Our research is based on the ‘Bird Song’ dataset, which we obtained from Xeno-Canto on Kaggle, and the “British Bird Song” dataset. We use the Short-time Fourier transform (STFT) to extract key auditory properties from these datasets. The audio data may be subjected to image-based categorization approaches by transforming the audio files into chromagrams. With our custom CNN architecture, we have outperformed a number of current methods, attaining accuracy rates of 94.46% and 97.02% for the corresponding datasets. Our research provides important information on the effectiveness and applicability of bird sound classification. Moreover, the accomplishments of our customized architecture demonstrate the possibility for customized solutions in this domain. The results pave the way for future developments in birdsong ambiance research and have implications for understanding ecosystems, identifying bird species, monitoring the environment, and protecting animals.