While machine learning algorithms offer powerful tools for analyzing large datasets of bird vocalizations, there is indeed a risk that they may oversimplify the complexity of these sounds. Machine learning models operate based on patterns identified in data inputs; however, this approach may not always capture the nuanced characteristics present in bird vocalizations. Bird songs often contain intricate variations in pitch, rhythm, timbre, and other qualities that might be challenging for algorithms to fully comprehend. As a result, there is a possibility that machine learning algorithms could overlook subtle differences or misinterpret certain aspects of avian vocal communication. To mitigate this risk, researchers must carefully design their models to account for the diverse range of sound features exhibited by different species.