The efficient coding hypothesis posits that biological sensory systems maximize information transfer to the brain while minimizing neural resources. Although extensively studied in humans, its role in non-human auditory perception remains relatively unexplored. Here, we apply sparse coding to bat echolocation calls to test whether their vocalizations are intrinsically optimized for efficient representation. Unlike prior bat studies using black-box models, our approach examines how acoustic selectivity can emerge in early auditory structures from call structure alone, independent of higher-level neural processing. The learned kernel representations are compact, sparse, and functionally specialized, with distinct activation profiles encoding specific call shapes. These findings suggest that bat auditory systems are tuned to conspecific vocalizations and underscore the advantages of sparse coding over traditional signal representations. They also improve the interpretability of animal auditory processing and provide a computational basis for modeling animal signals, supporting future research in interspecies communication and decoding animal vocalizations.