An open access dataset for developing automated detectors of Antarctic baleen whale sounds and performance evaluation of two commonly used detectors

Abstract

Since 2001, hundreds of thousands of hours of underwater acoustic recordings have been made throughout the Southern Ocean south of 60° S. Detailed analysis of the occurrence of marine mammal sounds in these circumpolar recordings could provide novel insights into their ecology, but manual inspection of the entirety of all recordings would be prohibitively time consuming and expensive. Automated signal processing methods have now developed to the point that they can be applied to these data in a cost-effective manner. However training and evaluating the efficacy of these automated signal processing methods still requires a representative annotated library of sounds to identify the true presence and absence of different sound types. This work presents such a library of annotated recordings for the purpose of training and evaluating automated detectors of Antarctic blue and fin whale calls. Creation of the library has focused on the annotation of a representative sample of recordings to ensure that automated algorithms can be developed and tested across a broad range of instruments, locations, environmental conditions, and years. To demonstrate the utility of the library, we characterise the performance of two automated detection algorithms that have been commonly used to detect stereotyped calls of blue and fin whales. The availability of this library will facilitate development of improved detectors for the acoustic presence of Southern Ocean blue and fin whales. It can also be expanded upon to facilitate standardization of subsequent analysis of spatiotemporal trends in call-density of these circumpolar species.

Using a Novel Visualization Tool for Rapid Survey of Long-Duration Acoustic Recordings for Ecological Studies of Frog Chorusing

“…Automated species detection for environmental sound recordings is a rapidly advancing field, however, may not be feasible or practical for all acoustic studies. The most successful automated detection algorithms are for species with well-described calls which are distinct from the calls of other species (e.g., Walters et al, 2014 for bats;reviewed in Kowarski and Moors-Murphy, 2020 for fin and blue whales) or for which large sets of training data are available (e.g., Kahl et al, 2021;Miller et al, 2021). Nonetheless, the challenge of automating analysis of acoustic data is far from solved for many research questions.…”

“…The time and effort in developing automated species detection methods to create results of limited practical use means this approach is not feasible in many studies and monitoring programs. Thus, manual sound analysis continues to be used in the majority of ecological studies using acoustic methods (Sugai et al, 2019), while automated call detection methods continue to be developed and improved (e.g., Ovaskainen et al, 2018;Marsland et al, 2019;Brooker et al, 2020;Kahl et al, 2021;Miller et al, 2021).…”

Sounding the Call for a Global Library of Underwater Biological Sounds

“…The library itself can be of benefit as it provides the basis from which the AI datasets can be developed either through providing numerous examples for directly training models, or for facilitating event mining from previously collected data streams (Xie et al, 2008;Zhang et al, 2013). For example, Miller et al (2021) initiated an open-access library of annotated recordings to train and evaluate automated detectors of Antarctic blue whale and fin whale (B. physalus) calls. The library was designed to include recordings from a broad range of instruments, locations, environmental conditions, and years, to ensure that robust detectors can be developed and tested across a suite of recording conditions.…”

“…Creating a library with established metadata and information criteria will help standardize the format in which signals are reported, optimize use of the library, and ease future classifications of sounds (Frazao et al, 2019). It is important to provide guidelines on all the pertinent information that could be provided by a person collecting the original recording and that should be included when it is presented, for example, as a static spectrogram on a library website (Parsons, 2010;Warren et al, 2018;Frazao et al, 2019;Looby et al, 2021;Miller et al, 2021). Such metadata standardization can also build confidence in the library's utility and attract support from national bodies for its application, such as the ADEON noise reporting standards (e.g., Ainslie et al, 2017).…”

Quantifying Soundscapes in the Ross Sea, Antarctica Using Long-Term Autonomous Hydroacoustic Monitoring Systems

“…Despite the difficult approach and risks in the recovery of moorings due to sea ice, thousands of hours of underwater acoustic recordings have been made throughout the Southern Ocean south of 60 • S since 2001 (Miller et al, 2021). One of the main objectives of this study is to examine the distribution and behavior of endangered Antarctic blue whales (Balaenoptera musculus intermedia) and fin whales (B. physalus) Van Opzeeland et al, 2013).…”