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12 results for “Underwater sound”
SOUNDSCAPE North Adriatic Underwater Noise Sound Pressure Levels
<p>Within the Interreg Italy-Croatia <strong>SOUNDSCAPE</strong> <strong>project</strong> a basin-scale, cross-national, long-term underwater monitoring in the Northern Adriatic Sea was carried out (https://www.italy-croatia.eu/web/soundscape). A broad network of nine monitoring stations, characterized by different natural conditions and anthropogenic pressures, ensured acoustic data collection from March 2020 to June 2021, including the first full lockdown period related to the COVID-19 pandemic (March–April 2020). Calibrated stationary recorders featured with an omnidirectional Neptune Sonar D60 Hydrophone recorded continuously 24h a day (48 kHz, 16 bit).</p> <p>Data were analysed to Sound Pressure Levels (SPLs, dB re 1 uPa) that are here released as a dataset composed of 20 and 60 seconds averaged SPL output files for each station. Data are archived using structured hdf5 files, each one containing metadata and SPL data, according to ICES (International Council for the Exploration of the Sea) continuous noise data specification (https://www.ices.dk/data/data-portals/Pages/Continuous-Noise.aspx).</p> <p>A research object with a jupyter notebook developed to post process SPL data is available at https://doi.org/10.24424/hrhm-8849</p> <p>If data are used, please cite also Petrizzo, A., Barbanti, A., Barfucci, G. <em>et al.</em> Author Correction: First assessment of underwater sound levels in the Northern Adriatic Sea at the basin scale. <em>Sci Data</em> <strong>10</strong>, 179 (2023). https://doi.org/10.1038/s41597-023-02099-x</p>
Underwater sounds, including killer whale and humpback whale vocalizations, recorded in northern Norway in January 2023
<p>Dataset of underwater acoustic recordings obtained during the expedition “Orcalize” that took place in Skjervøy in northern Norway from 29<sup>th</sup> December 2022 till 6<sup>th</sup> January 2023. The data contains vocalizations from killer whales and songs from humpback whales which gather in the local fjords during the winter months to feed on herring. We recorded in the band of 20 Hz – 60 kHz with calibrated hydrophones arranged in a compact tetrahedral array that we deployed over board of a motorboat. In total we provide 16 files of continuous recordings with duration from several minutes to over one hour. The total dataset is about 7 hours 37 minutes long and the memory size is 62.8 GB. See the file info.pdf for more information.</p>
Test experiments with distributed acoustic sensing and hydrophone arrays for locating underwater sounds.
<p>Whales and dolphins rely on sound for navigation and communication, making them an intriguing subject for studying language evolution. Traditional hydrophone arrays have been used to record their acoustic behavior, but optical fibers have emerged as a promising alternative. This study explores the use of distributed acoustic sensing (DAS), a technique that detects local stress in optical fibers, for underwater sound recording. An experiment was conducted in Lake Zurich, where a fiber-optic cable and a self-made hydrophone array were deployed. A test signal was broadcasted at various locations, and the resulting data was synchronized and consolidated into files. Analysis revealed distinct frequency responses in the DAS channels and provided insights into sound propagation in the lake. Challenges related to cable sensitivity, sample rate, and broadcast fidelity were identified. This dataset serves as a valuable resource for advancing acoustic sensing techniques in underwater environments, especially for studying marine mammal vocal behavior.</p>
Underwater ambient sound in tropical cyclones
<p>Underwater ambient sound measurements were made in three tropical cyclones: Hurricane Gustav (2008) in the Gulf of Mexico, and Typhoons Fanapi (September 2010) and Megi (October 2010) in the western Pacific Ocean as part of the ITOP (Impact of Typhoons on the Ocean in the Pacific) program. Measurements were made from eight Lagrangian floats, each equipped with one hydrophone, air deployed ahead of these storms by WC-130J aircraft operated by the U.S. Air Force Reserve 53rd Weather Reconnaissance squadron Hurricane Hunters. Floats 50 and 51 were in Gustav, 60, 61 and 62 in Fanapi, and 66, 67 and 68 in Megi. After the storm passage, the Lagrangian floats were recovered by a research vessel. Float positions were determined by interpolating between a few GPS positions taken during the storm passage guided by time-integrated velocity measurements from Electromagnetic Autonomous Profiling Explorer (EM-APEX) floats deployed at the same time.</p> <p>During the passage of tropical cyclones, the hydrophone switched between the work and sleep modes every 30 minutes due to limited data storage. In the work mode, the hydrophones sampled underwater ambient sound twice per second. The sound measurements thus are in 30-min segments. There are 39 (50), 38 (51), 60 (60), 56 (61), 56 (62), 55 (66), 53 (67) and 51 (68) segments (float serial numbers are in brackets), respectively, giving a total of 408 data segments and about 190 hours of sound measurements. Each raw time series has been Fourier transformed to obtain a power spectrum from 40 Hz to 50 kHz, with a spectral resolution of 40 Hz. The sound pressure level (SPL) in decibels (dB) is defined as <span class="math-tex">\(\textrm{SPL} = 20\cdot \textrm{log}(\textrm{P}/\textrm{P}_\textrm{ref}),\)</span> where <span class="math-tex">\(\textrm{P}\)</span> is the hydrophone measured sound pressure, and <span class="math-tex">\(\textrm{P}_\textrm{ref}\)</span> is the reference pressure 1 <span class="math-tex">\(\mu \textrm{Pa}^{2} \textrm{Hz}^{-2}\)</span>. The hydrophones were inter-calibrated in laboratory before and after the deployments, and agreed with a root-mean-square difference of 1–2 dB. The raw sound measurements are labeled SpdbP_raw.</p> <p>Each Lagrangian float carried a variety of instruments including a pumped CTD (conductivity, temperature and depth) sensor, a pumped GTD (gas tension device), a motor to control drogue, and another motor to control the float's buoyancy. These instruments generated noise of different temporal and spectral features. Sound measurements contaminated by noise were removed as described in <em>Zhao et al. (2014 JPO)</em>. One exception is GTD, which ran for 90% of the time for floats 50 and 51 (Gustav) and 66, 67, and 68 (Megi), and caused significant contamination on the < 5-kHz sound data. However, the > 5-kHz sound measurements are not affected by the GTD noise, and thus kept for future studies (detect rain events and breaking waves). The cleaned sound measurements are labeled SpdbP_clean.</p> <p>We decomposed the underwater ambient sound into three components according to their time scales. First, we calculate background sound, defined as the mean of the lowest 10% sound level over the 30 min period. The background sound generally rises and falls with increasing/decreasing wind speed and the presence of bubble clouds. Second, sound fluctuation is obtained by removing the background sound from the original data. Third, the sound fluctuation is divided into two components using two matched temporal filters. The second-scale fluctuation is obtained by high-pass filtering the sound fluctuation using 20-second running mean. The minute-scale fluctuation is obtained by low-pass filtering the sound fluctuation. By this method, the original underwater ambient sound is decomposed into three components: background (SpdbP_background), minute-scale fluctuation (SpdbP_MidFreq), and second-scale fluctuation (SpdbP_HighFreq). We applied the above decomposition method to all 408 30-min sound segments from eight Lagrangian floats, and created 408 figures with the same format. We share here the raw, de-noised, and decomposed sound data for all eight Lagrangian floats (eight Matlab data files) and demonstrate their decomposed sound data (eight PDF files). <a href="/api/files/d9886aea-3829-43d2-b10e-d57bdd77ae7a/Fig-5-Q101623.pdf?versionId=fee16fdd-adef-4419-98f7-3f05188065dd">Fig-5-Q101623.pdf </a> and <a href="/api/files/d9886aea-3829-43d2-b10e-d57bdd77ae7a/Fig-6-J091801.pdf?versionId=1607d29d-cb3c-4cbb-bb58-12c09b3a788c">Fig-6-J091801.pdf </a>are Figures 5 and 6 in a recent paper (<a href="https://journals.ametsoc.org/view/journals/atot/aop/JTECH-D-22-0078.1/JTECH-D-22-0078.1.xml">https://journals.ametsoc.org/view/journals/atot/aop/JTECH-D-22-0078.1/JTECH-D-22-0078.1.xml</a>). </p>
Compilation of existing underwater PAM repositories, libraries, and applications for sound processing
<p>Resources for passive acoustic monitoring (PAM) are continuously expanding and being developed, yet a major challenge for users is staying up-to-date and finding the best software or application for their acoustics investigation. We expand on previous efforts (Rhinehart & Nicholson, 2022; Felgate, 2023) with the aim of providing a current, comprehensive list of 1) underwater sound repositories of raw sound data without significant processing, 2) biological sound reference libraries, with species or taxa identification, and 3) sound processing tools for visualization, annotation, or analysis. This spreadsheet contains three pages, one dedicated to each of the aforementioned items, along with some descriptive information to help users identify the best resources for their needs.</p> <p>This work was done to support the Global Library of Underwater Biological Sounds (GLUBS) project and funded in part by the Richard Lounsbery Foundation and from funding to SCOR WG #169 (GLUBS) provided by national committees of the Scientific Committee on Oceanic Research (SCOR) and from a grant to SCOR from the US National Science Foundation (OCE--2140395), with support from the International Quiet Ocean Experiment.</p> <p> </p>
Underwater-sound records in glacier fjords (Inglefield Bredning, Baffin Bay, NW Greenland, Denmark), 19-28 July 2019
<p>Acoustic data (.wav) recorded by 2 hydrophones suspended from boats in Inglefield Bredning and Bowdoin fjords (Baffin Bay, NW Greenland, Denmark) in July 2019 for underwater soundscape documentation (narwhal vocalizations and environmental sources). </p> <p>*******************</p> <p>First set-up had a hydrophone AQH-020 by AquaSound Inc. (20Hz – 20kHz) connected to Amplifier Aquafeeler III (SQE-1001B, 50dB gain) by AquaSound Inc. and a recorder PCM-M10 by Sony (44.1 kHz, 16 bit, auto-mode). Recording depth was about 6.6 m, except a record collected on July 27, 2019 at 15:56:33 (depth was about 0.5 m).</p> <p>Channels: 2 (but records are only at “Left”/1 channel; the “Right”/2 is electric noise).</p> <p>File name: sony.YYMMDDhhmmss.wav</p> <p>Note that the strongest regularly-spaced impulsive sounds in two files (sony.190719130533.WAV, sony.190719132214.WAV) are seemingly not due to a whale nearby, but due to repetitive impacts of the hydrophone with a ballast-rope in strong current. This issue was fixed by adjusting the rope length, after which the sound was gone.</p> <p>*******************</p> <p>Second set-up had a hydrophone SoundTrap SD3000 by Ocean Instruments NZ (20Hz – 60kHz), integrated with amplifier and recorder, sampling at 96 kHz, 16 bit. Signal-to-pressure conversion constant was 176.2 dB for this particular device (ID number 5146, at High-Gain mode). Recording depth was about 10.8 m, except records collected on July 20, 2019 between 00:44:47 and 08:44:47 (depth was about 1 m). </p> <p>Channels: 1</p> <p>File name format: 5146.YYMMDDhhmmss.wav</p> <p>*******************</p> <p>Coordinates for each record by each set-up are shown below.</p> <p> </p> <p>Geographic position of each measurement with <strong>SoundTrap</strong> is as the following:</p> <p>Date, Record Start Time(UTC), lon, lat,</p> <p> </p> <p>19 July 2019, 12:55:17, 77.474752, -68.660610 </p> <p>19 July 2019, 13:19:53, 77.488215, -68.597227 </p> <p>19 July 2019, 14:19:53, 77.485103, -68.574985 </p> <p>19 July 2019, 16:19:41, 77.523033, -68.403958 </p> <p>19 July 2019, 19:06:57, 77.618543, -68.564536 </p> <p> </p> <p>20 July 2019, 00:44:47, 77.548649, -68.550752 </p> <p>20 July 2019, 13:02:59, 77.527026, -68.534285</p> <p>20 July 2019, 14:30:26, 77.487211, -68.483834</p> <p>20 July 2019, 15:10:13, 77.495954, -68.658084 </p> <p> </p> <p>*******************</p> <p>Geographic position of each measurement with <strong>Sony-AquaSound</strong> is as the following:</p> <p>Date, Record Start Time(UTC), lon, lat,</p> <p> </p> <p>19 July 2019, 13:05:33, 77.474752, -68.660610 </p> <p>19 July 2019, 13:22:14, 77.488215, -68.597227 </p> <p>19 July 2019, 16:11:54, 77.523033, -68.403958 </p> <p>19 July 2019, 19:09:11, 77.618543, -68.564536</p> <p> </p> <p>20 July 2019, 13:04:42, 77.527026, -68.534285</p> <p>20 July 2019, 14:01:42, 77.505033, -68.553648</p> <p>20 July 2019, 15:11:10, 77.495954, -68.658084 </p> <p> </p> <p>21 July 2019, 22:17:04, 77.675334, -68.636040 </p> <p>21 July 2019, 22:19:53, 77.671904, -68.639090</p> <p> </p> <p>22 July 2019, 00:12:34, 77.525553, -68.442136 </p> <p> </p> <p>27 July 2019, 13:28:58, 77.617588, -68.597946</p> <p>27 July 2019, 14:13:59, 77.667788, -68.643976 </p> <p>27 July 2019, 15:56:33, 77.665487, -68.778195 </p> <p>27 July 2019, 17:03:04, 77.672426, -68.658150 </p> <p>27 July 2019, 17:22:16, 77.669874, -68.657587 </p> <p>27 July 2019, 17:59:39, 77.676941, -68.664817 </p> <p>27 July 2019, 19:44:32, 77.668669, -68.656365 </p> <p>27 July 2019, 21:57:07, 77.628218, -68.637347</p> <p>27 July 2019, 23:07:12, 77.625571, -68.616875</p> <p> </p> <p>28 July 2019, 00:02:41, 77.619299, -68.595757</p>
Gentoo penguins (Pygoscelis papua) react to underwater sounds
<p>Marine mammals and diving birds face several physiological challenges under water, affecting their thermoregulation and locomotion as well as their sensory systems. Therefore, marine mammals have modified ears for improved underwater hearing. <a name="_Hlk5740480">Underwater hearing in marine birds have been studied in a few species, but for the record-holding divers, such as penguins, there are no detailed data</a>. We played underwater noise bursts to gentoo penguins<i> (Pygoscelis papua)</i> in a large tank at sound pressure levels between 100 and 120 dB re 1 µPa rms. The penguins showed a graded reaction to the noise bursts, ranging from no reactions at 100 dB to strong reactions in more than 62% of the playbacks at 120 dB re 1 µPa. The responses were always directed away from the sound source. The fact that penguins can detect and react to underwater stimuli may indicate that they make use of sound stimuli for orientation and prey detection during dives. Further, it suggests that penguins may be sensitive to anthropogenic noise, like many species of marine mammals.</p>
Fig. 4. A in Identification of Sound-Producing Hydrophilid Beetles (Coleoptera: Hydrophilidae) in Underwater Recordings Using Digital Signal Processing
Fig. 4. A half-second vocalization by Tropisternus blatchleyi from 0–5,000 Hz changed into the frequency domain using the Fast Fourier Transformation. Active call frequency regions are at 1,100 Hz and 4,400 Hz. The first feature for T. blatchleyi divides the sum of the points in the active frequency band by the sum of the points in the inactive band, yielding a large number in T. blatchleyi exemplar calls.
Fig. 3 in Identification of Sound-Producing Hydrophilid Beetles (Coleoptera: Hydrophilidae) in Underwater Recordings Using Digital Signal Processing
Fig. 3. The classifier algorithm with two features shown in a two-dimensional graph. Beetle call data and noise data are classified based on two beetle call features: difference in active frequency range shape (x-axis and in Matlab™ as a template) and a ratio of amplitudes in an active and non-active frequency range (y-axis). The algorithm is shown as a solid black line. Most beetle calls fall within the correct classification in the lower left-hand corner, however, some fall outside the equation and are classified as noise. Likewise, noises are occasionally classified as beetles. As more features are added, the algorithm becomes multidimensional.
Fig. 2 in Identification of Sound-Producing Hydrophilid Beetles (Coleoptera: Hydrophilidae) in Underwater Recordings Using Digital Signal Processing
Fig. 2. Five distress calls by Berosus pantherinus transformed into the frequency domain using the fast fourier transformation from 0–12,000 Hz (x-axis). Wide active frequency bands can be seen from 1,500–6,000 Hz and 7,000–9,500 Hz. The feature for distress calls is the sum of the data points between 1,000–6,000 Hz with an amplitude (y-axis) greater than 2.
Gentoo penguins (Pygoscelis papua) react to underwater sounds
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Fig. 1 in Identification of Sound-Producing Hydrophilid Beetles (Coleoptera: Hydrophilidae) in Underwater Recordings Using Digital Signal Processing
Fig. 1. Each graph is a frequency plot of a one half-second exemplar sound clip. One exemplar call from each species and an exemplar distress call from an individual Tropisternus blatchleyi (that is indicative of all four beetles' distress calls) is shown transformed into the frequency domain using the fast fourier transformation with normalized amplitudes and plotted from 0–10,000 Hz.
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