With only 74 Southern Resident killer whales (SRKWs) remaining in the waters of the eastern Pacific, understanding these marine mammals is vital to their conservation. Invasive techniques such as focal follows or tagging whales could introduce stress to SRKWs. Instead, passive acoustic monitoring that uses a network of hydrophones one preferred tracking method, particularly in their critical habitat that overlaps the shipping lanes in the Salish Sea. The acoustic data, if processed in real-time using modern deep learning methods, can be used to detect whales and alert ships of the potential for spatial overlap to reduce risks of collision. In this project, we consider the differences in SRKW whale call types from archived recordings of each of J, K, and L pods recorded on regional hydrophones. The whale calls are extracted as functional observations in the time and frequency space from underwater recordings using a particle filter. Functional data analysis (e.g., functional clustering) are performed to characterize the whale calls and represent the variations in call types. Such statistical insights between pod-level call types should be useful for improving machine learning whale detection algorithms, and for identifying SRKW movement in a high ship traffic region of their critical habitat.