2-3. Detection and classification of leopard seal vocalizations
During the analysis of the underwater soundscape, we confirmed that leopard seal vocalizations were prominent and persistent throughout the recording period, as evidenced by spectrograms from randomly selected WAV files. Consequently, we focused on detecting and classifying these vocalizations. All acoustic data files, recorded in 10-minute durations, were subjected to the detection process on both a waveform and a spectrogram in 1-minute intervals. Call signals, which are stronger than the background noise level and whose features could be clearly distinguished in the spectrogram, were manually detected, and then classified into HDT, MST, LDT, HST, DT, and AT based on the call types in previous studies (Stirling and Siniff, 1979; Rogers, 2007; Klinck, 2008). The call patterns of the five types were mostly consistent with those reported in previous studies except for AT, and the acoustic characteristics of each call type were extracted by a process that considered the detailed features of the signals. For low-frequency vocalizations, uncertainty was accounted for by re-detecting calls within 1-hour segments, counting the number of missed and false detections, and adjusting these values relative to the total recording times. Similarly, for HDT and MST, uncertainty was estimated by randomly extracting detected signals, counting the number of false detections, and adjusting these values relative to the corresponding time period.