A review of big data analysis methods for baleen whale passive acoustic monitoring

• Published in: Marine Mammal Science
• Main Authors: Kowarski, Katie A., Moors‐Murphy, Hilary
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2020
• Subjects: Aquatic Science; Ecology, Evolution, Behavior and Systematics; baleen whale
• Online Access: http://dx.doi.org/10.1111/mms.12758; https://onlinelibrary.wiley.com/doi/pdf/10.1111/mms.12758; https://onlinelibrary.wiley.com/doi/full-xml/10.1111/mms.12758

Abstract Many organizations collect large passive acoustic monitoring (PAM) data sets that need to be efficiently and reliably analyzed. To determine appropriate methods for effective analysis of big PAM data sets, we undertook a literature review of baleen whale PAM analysis methods. Methodologies from 166 studies (published between 2000–2019) were summarized, and a detailed review was performed on the 94 studies that recorded more than 1,000 hr of acoustic data (“big data”). Analysis techniques for extracting baleen whale information from PAM data sets varied depending on the research observed. A spectrum of methodologies was used and ranged from manual analysis of all acoustic data by human experts to completely automated techniques with no manual validation. Based on this assessment, recommendations are provided to encourage robust research methods that are comparable across studies and sectors, achievable across research groups, and consistent with previous work. These include using automated techniques when possible to increase efficiency and repeatability, supplementing automation with manual review to calculate automated detector performance, and increasing consistency in terminology and presentation of results. This work can be used to facilitate discussion for minimum standards and best practices to be implemented in the field of marine mammal PAM.