Equipment and deployment strategies for remote passive acoustic sensing of marine environments must balance memory capacity, power requirements, sampling rate, duty-cycle, deployment duration, instrument size, and environmental concerns. The impact of different parameters on the data and applicability of the data to the specific questions being asked should be considered before deployment. Here we explore the effect of recording and detection parameters on marine mammal acoustic data across two platforms. Daily classifications of marine mammal vocalizations from two passive acoustic monitors with different subsampling parameters, an AURAL and a Passive Aquatic Listener (PAL), collocated in the Bering Sea were compared. The AURAL subsampled on a pre-set schedule, whereas the PAL sampled via an adaptive protocol. Detected signals of interest were manually classified in each dataset independently. The daily classification rates of vocalizations were similar. Detections from the higher duty-cycle but lower sample rate AURAL were limited to species and vocalizations with energy below 4 kHz precluding detection of echolocation signals. Temporal coverage from the PAL audio files was limited by the adaptive sub-sampling protocol. A method for classifying ribbon (Histriophoca fasciata) and bearded seal (Erignathus barbatus) vocalizations from the sparse spectral time histories of the PAL was developed. Although application of the acoustic entropy as a rapid assessment of biodiversity was not reflective of the number of species detected, acoustic entropy was robust to changes in sample rate and window length.