This record represents humpback whale sound production detected from raw passive acoustic data. All continuous acoustic data were divided into 30 s wav file subsamples and processed using a machine learning humpback whale detector model developed and provided by Google (GoogleAI; Matt Harvey). The presence of humpback whale song is scored by the model and, compared to a choosen threshold, was assigned a binary 0/1 designation indicating absence (0) or presence (1). The percentage of files with humpback whale presence per hour was calculated.These data were recorded at SanctSound Site PM02_02 between October 01, 2020 and July 10, 2021.

This record represents humpback whale sound production detected from raw passive acoustic data. The Low Frequency Detection and Classification System (LFDCS) call library for humpback whales contains 9 call types with exemplars taken from song notes spanning 2006-2017 (except 2007). All humpback whale detections with a MD of 3.0 or less were manually screened for the daily presence of true humpback whale vocalizations. A day was then marked as present for humpback whales if one true detection was found within at least three humpback whale vocalizations, occurring over a 10 min window. The 10 min window was deemed sufficient to clearly distinguish putative humpback whale vocalizations from those of other species.These data were recorded at SanctSound Site SB02_02 between January 28, 2019 and April 02, 2019.

This record represents manual detection of humpback whale sounds. Humpback presence was determined by manually scanning long-term spectral averages (LTSAs) in the Triton MATLAB software package. Acoustic files were decimated to a sample rate of 4 kHz before generating 5 s, 1 Hz LTSAs. The LTSA was scanned by a trained analyst in hourly bins for visual evidence of song and non-song humpback vocalizations. Detections were aurally confirmed. These data were recorded at SanctSound Site MB01_09 between August 11, 2021 and November 21, 2021.

This record represents humpback whale sound production detected from raw passive acoustic data.All continuous acoustic data were divided into 30 s wav file subsamples and processed using a machine learning humpback whale detector model developed and provided by Google (GoogleAI; Matt Harvey). The presence of humpback whale song is scored by the model and, compared to a choosen threshold, was assigned a binary 0/1 designation indicating absence (0) or presence (1). The percentage of files with humpback whale presence per hour was calculated.These data were recorded at SanctSound Site HI04_01 between November 25, 2018 and February 01, 2019.

This record represents manual detection of humpback whale sounds. Humpback presence was determined by manually scanning long-term spectral averages (LTSAs) in the Triton MATLAB software package. Acoustic files were decimated to a sample rate of 4 kHz before generating 5 s, 1 Hz LTSAs. The LTSA was scanned by a trained analyst in hourly bins for visual evidence of song and non-song humpback vocalizations. Detections were aurally confirmed. These data were recorded at SanctSound Site CI04_05 between June 04, 2020 and October 22, 2020.

This record represents manual detection of humpback whale sounds. Humpback presence was determined by manually scanning long-term spectral averages (LTSAs) in the Triton MATLAB software package. Acoustic files were decimated to a sample rate of 4 kHz before generating 5 s, 1 Hz LTSAs. The LTSA was scanned by a trained analyst in hourly bins for visual evidence of song and non-song humpback vocalizations. Detections were aurally confirmed. These data were recorded at SanctSound Site CI02_06 between October 22, 2020 and March 02, 2021.

This record represents manual detection of humpback whale sounds. Humpback presence was determined by manually scanning long-term spectral averages (LTSAs) in the Triton MATLAB software package. Acoustic files were decimated to a sample rate of 4 kHz before generating 5 s, 1 Hz LTSAs. The LTSA was scanned by a trained analyst in hourly bins for visual evidence of song and non-song humpback vocalizations. Detections were aurally confirmed. These data were recorded at SanctSound Site CI02_04 between February 05, 2020 and June 04, 2020.

This record represents manual detection of humpback whale sounds. Humpback presence was determined by manually scanning long-term spectral averages (LTSAs) in the Triton MATLAB software package. Acoustic files were decimated to a sample rate of 4 kHz before generating 5 s, 1 Hz LTSAs. The LTSA was scanned by a trained analyst in hourly bins for visual evidence of song and non-song humpback vocalizations. Detections were aurally confirmed. These data were recorded at SanctSound Site CI01_08 between July 08, 2021 and September 11, 2021.

NOAA and the U.S. Navy are working to better understand underwater sound within the U.S. National Marine Sanctuary System. From 2018 to 2021, these agencies will work with numerous scientific partners to study sound within seven national marine sanctuaries and one marine national monument, which includes waters off Hawai'i and the east and west coasts. Standardized measurements will assess sounds produced by marine animals, physical processes (e.g., wind and waves), and human activities. Collectively, this information will help NOAA and the Navy measure sound levels and baseline acoustic conditions in sanctuaries. This work is a continuation of ongoing Navy and NOAA research, including efforts by NOAA's Office of National Marine Sanctuaries This dataset represents the derived products from the raw acoustic data that are archived at NOAA National Centers for Environmental Information. abstract=This record represents humpback whale sound production detected from raw passive acoustic data. All continuous acoustic data were divided into 30 s wav file subsamples and processed using a machine learning humpback whale detector model developed and provided by Google (GoogleAI; Matt Harvey). The presence of humpback whale song is scored by the model and, compared to a choosen threshold, was assigned a binary 0/1 designation indicating absence (0) or presence (1). The percentage of files with humpback whale presence per hour was calculated.These data were recorded at SanctSound Site PM01_02 between October 01, 2020 and July 09, 2021. acknowledgement=This project received funding from the U.S. Navy. cdm_data_type=TimeSeries citation=Cite as: NOAA Office of National Marine Sanctuaries and U.S Navy. 2021. Humpback Whale Sound Production Recorded at SanctSound Site PM01_02, SanctSound Data Products. NOAA National Centers for Environmental Information. Accessed [date]. DOI: https://doi.org/http://doi.org/10.25921/msqd-1g21 comment=Data quality: Quality data were recorded for the duration of the deployment. contributor_name=Simone Baumann-Pickering, Scripps Institution of Oceanography; Leila Hatch, NOAA Stellwagen Bank National Marine Sanctuary; John Joseph, U.S. Naval Postgraduate School; Anke Kuegler, Hawai'i Institute of Marine Biology, University of Hawai'i at Manoa; Marc Lammers, NOAA Hawaiian Islands Humpback Whale National Marine Sanctuary; Tetyana Margolina, U.S. Naval Postgraduate School; Karlina Merkens, NOAA Pacific Islands Fisheries Science Center; Lindsey Peavey Reeves, NOAA Channel Islands National Marine Sanctuary; Timothy Rowell, NOAA Northeast Fisheries Science Center; Jenni Stanley, Woods Hole Oceanographic Institution; Alison Stimpert, Moss Landing Marine Laboratories; Sofie Van Parijs, NOAA Northeast Fisheries Science Center; Eden Zang,NOAA Hawaiian Islands Humpback Whale National Marine Sanctuary contributor_role=Principal Investigator Conventions=COARDS, CF-1.6, ACDD-1.3 featureType=TimeSeries geospatial_bounds=POINT (22.662033 -161.042183) history=All continuous acoustic data were divided into 30 s wav file subsamples. All 30 s recordings were processed using a machine learning humpback whale detector model developed and provided by Google (GoogleAI; Matt Harvey), which steps through each recording at 1.1 s intervals. At each step, the presence of humpback whale song is scored by the model, resulting in 27 scores for each file. The mean out of the 27 scores was calculated and upon meeting a choosen threshold of 0.25 was assigned a binary 0/1 designation indicating absence (0) or presence (1) of humpback whale song within the file. The percentage of files with humpback whale presence per hour was calculated. Data were processed with GoogleAI id=http://doi.org/10.25921/msqd-1g21 infoUrl=https://ncei.noaa.gov institution=NOAA instrument=SoundTrap ST500 keywords_vocabulary=GCMD Science Keywords naming_authority=NOAA-Navy project=NOAA-Navy Sanctuary Soundscape Monitoring Project sourceUrl=(local files) standard_name_vocabulary=CF Standard Name Table v55