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8 results for “ambient sound”
Ambient sound spectrograms between 2015-01-01 and 2021-01-01 for OOI low-frequency hydrophones
<p>Ambient sound spectrograms calculated for the OOI low-frequency hydrophones. The spectrograms are PSD estimates using the welch method and median averaging with an averaging time of 15 minutes and 512 points per segment.</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>
"I made the recording because Iam an amateur recording engineer and also work for a radio station. At the time, Iwas researching for a religious programme, for the radio and by pure chance and good luck, Iwas in the centre of York at the time the street preacher was there. Iam building up a personal library of 'ambient sounds' to use on various radio shows as 'sound effects'. The recording was taken outside St Helen's Church in St Helen's Square, in the centre of York. There was a fairly large crowd walking about, shopping. It was a Saturday. Some people were standing and listening to the man, some were mocking him, others didn't even notice. It was a sunny day, with a slight wind. St Helen's square is a large 'meeting place' for people with seats, flowers and usually musicians. I live in the centre of York and hear a lot of very interesting sounds there, everything from busking musicians, to many foreign languages, church bells, animals and much more. Ireally liked the recording of the preacher as it is quite clear that he passionately believes what he is saying. He was unaware that Iwas recording him. Iwish Ihad captured his whole sermon. He, and other members of his church visit the centre of York quite often, and preach there. Idon't know the name of his church." [Jools/vedas]19 in Collecting Sounds. Online Sharing of Field Recordings as Cultural Practice
"I made the recording because Iam an amateur recording engineer and also work for a radio station. At the time, Iwas researching for a religious programme, for the radio and by pure chance and good luck, Iwas in the centre of York at the time the street preacher was there. Iam building up a personal library of 'ambient sounds' to use on various radio shows as 'sound effects'. The recording was taken outside St Helen's Church in St Helen's Square, in the centre of York. There was a fairly large crowd walking about, shopping. It was a Saturday. Some people were standing and listening to the man, some were mocking him, others didn't even notice. It was a sunny day, with a slight wind. St Helen's square is a large 'meeting place' for people with seats, flowers and usually musicians. I live in the centre of York and hear a lot of very interesting sounds there, everything from busking musicians, to many foreign languages, church bells, animals and much more. Ireally liked the recording of the preacher as it is quite clear that he passionately believes what he is saying. He was unaware that Iwas recording him. Iwish Ihad captured his whole sermon. He, and other members of his church visit the centre of York quite often, and preach there. Idon't know the name of his church." [Jools/vedas]19
Data from: Multiple signaling in a variable environment: expression of song and color traits as a function of ambient sound and light
Many animals communicate using more than one signal, and several hypotheses exist to explain the evolution of multiple signals. However, these hypotheses typically assume static selection pressures and previous work has not addressed how spatial and temporal environmental variation can shape variation in signaling systems. In particular, environmental variability, such as ambient lighting or noise, may affect efficacy (e.g. detectability/perception by receivers) of signals. To examine how signal expression varies intraspecifically as a function of habitat characteristics, we evaluated relationships between spatial environmental variation and song and plumage color expression in a tropical songbird, the red-throated ant-tanager (Habia fuscicauda) in Panama. We recorded male ant-tanager song, plucked feathers to measure coloration, and recorded the acoustic and light environments from each male's territory. In addition, we took several morphometric measurements from each male to assess the potential information content of song and plumage color. We found that males with redder and more saturated crowns occurred on darker territories, and males that sang shorter and lower frequency songs occurred on noisier territories. We also found that more colorful males tended to sing longer and lower frequency songs. Finally we found that song and color correlated similarly with male morphology (e.g. tarsus length, body mass). Altogether these results indicate that spatial variation in the environment is related to male coloration and song, and that males might be optimizing color and song expression for their particular territorial environment.
Data from: The influence of sea ice, wind speed and marine mammals on Southern Ocean ambient sound
This paper describes the natural variability of ambient sound in the Southern Ocean, an acoustically pristine marine mammal habitat. Over a 3-year period, two autonomous recorders were moored along the Greenwich meridian to collect underwater passive acoustic data. Ambient sound levels were strongly affected by the annual variation of the sea-ice cover, which decouples local wind speed and sound levels during austral winter. With increasing sea-ice concentration, area and thickness, sound levels decreased while the contribution of distant sources increased. Marine mammal sounds formed a substantial part of the overall acoustic environment, comprising calls produced by Antarctic blue whales (Balaenoptera musculus intermedia), fin whales (Balaenoptera physalus), Antarctic minke whales (Balaenoptera bonaerensis) and leopard seals (Hydrurga leptonyx). The combined sound energy of a group or population vocalizing during extended periods contributed species-specific peaks to the ambient sound spectra. The temporal and spatial variation in the contribution of marine mammals to ambient sound suggests annual patterns in migration and behaviour. The Antarctic blue and fin whale contributions were loudest in austral autumn, whereas the Antarctic minke whale contribution was loudest during austral winter and repeatedly showed a diel pattern that coincided with the diel vertical migration of zooplankton.
Ambient sound classification dataset - UC CRLAB
<p>This dataset contains three classes: Alarmed, Social, and Disengaged. It contains audio file of a number of 925 for Disengaged, 941 for Social, and 942 for Alarmed.</p>
Data from: Multiple signaling in a variable environment: expression of song and color traits as a function of ambient sound and light
Open the record for dataset details and reuse information.
Data from: The influence of sea ice, wind speed and marine mammals on Southern Ocean ambient sound
Open the record for dataset details and reuse information.
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