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Automatic algorithms for the detection and classification of sound are essential to the analysis of acoustic datasets with long duration. Metrics are needed to assess the performance characteristics of these algorithms. Four metrics for performance evaluation are discussed here: receiver-operating-characteristic (ROC) curves, detection-error-trade-off (DET) curves, precision-recall (PR) curves, and cost curves. These metrics were applied to the generalized power law detector for blue whale D calls [Helble, Ierley, D'Spain, Roch, and Hildebrand (2012). J. Acoust. Soc. Am. 131(4), 2682-2699] and the click-clustering neural-net algorithm for Cuvier's beaked whale echolocation click detection [Frasier, Roch, Soldevilla, Wiggins, Garrison, and Hildebrand (2017). PLoS Comp. Biol. 13(12), e1005823] using data prepared for the 2015 Detection, Classification, Localization and Density Estimation Workshop. Detection class imbalance, particularly the situation of rare occurrence, is common for long-term passive acoustic monitoring datasets and is a factor in the performance of ROC and DET curves with regard to the impact of false positive detections. PR curves overcome this shortcoming when calculated for individual detections and do not rely on the reporting of true negatives. Cost curves provide additional insight on the effective operating range for the detector based on the a priori probability of occurrence. Use of more than a single metric is helpful in understanding the performance of a detection algorithm.

Eastern North Pacific gray whales make one of the longest annual migrations of any mammal, traveling from their summer feeding areas in the Bering and Chukchi Seas to their wintering areas in the lagoons of Baja California, Mexico. Although a significant body of knowledge on gray whale biology and behavior exists, little is known about their vocal behavior while migrating. In this study, we used a sparse hydrophone array deployed offshore of central California to investigate how gray whales behave and use sound while migrating. We detected, localized, and tracked whales for one full migration season, a first for gray whales. We verified and localized 10,644 gray whale M3 calls and grouped them into 280 tracks. Results confirm that gray whales are acoustically active while migrating and their swimming and acoustic behavior changes on daily and seasonal time scales. The seasonal timing of the calls verifies the gray whale migration timing determined using other methods such as counts conducted by visual observers. The total number of calls and the percentage of calls that were part of a track changed significantly over both seasonal and daily time scales. An average calling rate of 5.7 calls/whale/day was observed, which is significantly greater than previously reported migration calling rates. We measured a mean speed of 1.6 m/s and quantified heading, direction, and water depth where tracks were located. Mean speed and water depth remained constant between night and day, but these quantities had greater variation at night. Gray whales produce M3 calls with a root mean square source level of 156.9 dB re 1 μPa at 1 m. Quantities describing call characteristics were variable and dependent on site-specific propagation characteristics.

Fin whale song is a ubiquitous low-frequency pulsing that has been recorded in every ocean basin. In recent years, the complexity of fin whale song has been realized. In the North Atlantic, this song is made up of two low-frequency note types (A and B notes) and one higher-frequency note type (upsweeps). The A and B notes are produced with both singlet and doublet inter-note intervals. These song patterns are interwoven throughout an individual's song and have been observed to shift over time, both gradually and suddenly. Like many baleen whales, the population size of fin whales is poorly defined. Passive acoustic monitoring has been suggested as a way to estimate abundance if a cue rate or calling rate can be determined. In this study, we used recordings from 119 fin whale tracks from 2013-2023 generated from passive acoustic recorders in the North Atlantic to define fin whale song patterns in this ocean basin, identify changes in these song patterns over time, and calculate cue rates. The fin whales recorded sang in 4 distinct patterns. Inter-note intervals were 25 s for A-A singlets, 13 s for B-B singlets, 12/20 s for B-B doublets, and 10/11 s for A-B doublets. B-B doublet inter-note intervals significantly increased from May 2019--April 2023 and A-B inter-note intervals showed an intra-annual increasing trend that reset each summer. Fin whale song in the North Atlantic also often includes higher frequency upsweep notes immediately prior to B notes. While A and B note peak frequency was steady over these years, upsweep peak frequency significantly decreased, continuing a similar trend that has been observed over 30 years. Despite these changes, the dominant song pattern remained consistent and cue rate for actively singing whales was relatively stable, which is in contrast to patterns observed in the North Pacific. Many of these nuances in fin whale singing behavior are newly described herein, and these findings are vital for monitoring baseline behavior, behavioral trends over time, and ultimately estimating abundance of a poorly understood species. [ABSTRACT FROM AUTHOR]

This paper demonstrates the importance of accounting for environmental effects on passive underwater acoustic monitoring results. The situation considered is the reduction in shipping off the California coast between 2008-2010 due to the recession and environmental legislation. The resulting variations in ocean noise change the probability of detecting marine mammal vocalizations. An acoustic model was used to calculate the time-varying probability of detecting humpback whale vocalizations under best-guess environmental conditions and varying noise. The uncorrected call counts suggest a diel pattern and an increase in calling over a two-year period; the corrected call counts show minimal evidence of these features.

Passive acoustic monitoring of marine mammal calls is an increasingly important method for assessing population numbers, distribution, and behavior. A common mistake in the analysis of marine mammal acoustic data is formulating conclusions about these animals without first understanding how environmental properties such as bathymetry, sediment properties, water column sound speed, and ocean acoustic noise influence the detection and character of vocalizations in the acoustic data. The approach in this paper is to use Monte Carlo simulations with a full wave field acoustic propagation model to characterize the site specific probability of detection of six types of humpback whale calls at three passive acoustic monitoring locations off the California coast. Results show that the probability of detection can vary by factors greater than ten when comparing detections across locations, or comparing detections at the same location over time, due to environmental effects. Effects of uncertainties in the inputs to the propagation model are also quantified, and the model accuracy is assessed by comparing calling statistics amassed from 24,690 humpback units recorded in the month of October 2008. Under certain conditions, the probability of detection can be estimated with uncertainties sufficiently small to allow for accurate density estimates.