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33 results for “loudness”
Recordings for loudness analysis of the music mixes for comparison of wave field synthesis, surround, and stereo
<p>Mat files of live recordings of the music mixes for the comparison of wave field synthesis, surround, and stereo listening test. The recordings were performed at different levels and were analyzed with a loudness model afterwards for adjusting the levels. For details, see</p> <p>C. Hold, H. Wierstorf, A. Raake, The Difference Between Stereophony and Wave Field Synthesis in the Context of Popular Music, in 140th AES Convention, 2016.</p>
Vehicle driving actions for loudness and annoyance perception
<p>This dataset contains 360º videos of 36 driving actions. The videos are organized by vehicles: a white car (Opel Corsa 2016), a dark red motorbike (Suzuki VX 800 800cc 1994), a dark blue van (Fort Transit FT100 1999) and a street sweeper (Kärcher MC 50).</p> <p>The recordings were done with a 360º camera (Xiami Mi Sphere Camera) and a tethraedral microphone (Core Sound TetraMic). The microphone recordings were synthesized to stereo recordings (as if the microphones were pointing at +-60º azimuth) with VVMic from VVAudio. The sound pressure level was measured with a level meter.</p> <p>The driving actions are the following. The sound pressure level was calculated as the fast maximum level (maximum dB SPL in windows of 125ms).</p> <ul> <li>Car <ol> <li><a href="https://www.youtube.com/watch?v=zvmhiE3NXx8&t=0s">00:00</a> Scene 1 - Stand by (close) - 72.5 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=zvmhiE3NXx8&t=18s">00:18</a> Scene 2 - Accelerate (close) LR - 84.9 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=zvmhiE3NXx8&t=36s">00:36</a> Scene 3 - 30 km/h (far) RL - 70.4 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=zvmhiE3NXx8&t=54s">00:54</a> Scene 4 - 50 km/h (close) LR - 81.0 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=zvmhiE3NXx8&t=72s">01:12</a> Scene 5 - Break and stop (far) RL - 79.8 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=zvmhiE3NXx8&t=90s">01:30</a> Scene 6 - Stand by (far) - 67.8 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=zvmhiE3NXx8&t=108s">01:48</a> Scene 7 - Accelerate (far) RL - 79.3 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=zvmhiE3NXx8&t=126s">02:06</a> Scene 8 - 30 km/h (close) LR - 82.4 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=zvmhiE3NXx8&t=144s">02:24</a> Scene 9 - 50 km/h (far) RL - 75.7 dB SP</li> <li><a href="https://www.youtube.com/watch?v=zvmhiE3NXx8&t=162s">02:42</a> Scene 10 - Break and stop (close) LR - 75.9 dB SPL</li> </ol> </li> <li>Motorbike <ol> <li><a href="https://www.youtube.com/watch?v=3wa6zeEbJ5w&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=2&t=0s">00:00</a> Scene 1 - Stand by (close) - 83.7 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=3wa6zeEbJ5w&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=2&t=18s">00:18</a> Scene 2 - Accelerate (close) LR - 92.5 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=3wa6zeEbJ5w&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=2&t=36s">00:36</a> Scene 3 - 30 km/h (far) RL - 83.2 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=3wa6zeEbJ5w&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=2&t=54s">00:54</a> Scene 4 - 50 km/h (close) LR - 90.2 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=3wa6zeEbJ5w&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=2&t=72s">01:12</a> Scene 5 - Break and stop (far) RL - 81.8 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=3wa6zeEbJ5w&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=2&t=90s">01:30</a> Scene 6 - Stand by (far) - 78.1 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=3wa6zeEbJ5w&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=2&t=108s">01:48</a> Scene 7 - Accelerate (far) RL - 86.8 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=3wa6zeEbJ5w&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=2&t=126s">02:06</a> Scene 8 - 30 km/h (close) LR - 91.2 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=3wa6zeEbJ5w&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=2&t=144s">02:24</a> Scene 9 - 50 km/h (far) RL - 84.3 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=3wa6zeEbJ5w&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=2&t=162s">02:42</a> Scene 10 - Break and stop (close) LR - 83.4 dB SPL</li> </ol> </li> <li>Van <ol> <li><a href="https://www.youtube.com/watch?v=DdaWvKfVILo&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=3&t=0s">00:00</a> Scene 1 - Stand by (close) - 84.4 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=DdaWvKfVILo&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=3&t=18s">00:18</a> Scene 2 - Accelerate (close) LR - 93.8 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=DdaWvKfVILo&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=3&t=36s">00:36</a> Scene 3 - 30 km/h (far) RL - 81.4 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=DdaWvKfVILo&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=3&t=54s">00:54</a> Scene 4 - 50 km/h (close) LR - 92.3 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=DdaWvKfVILo&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=3&t=72s">01:12</a> Scene 5 - Break and stop (far) RL - 81.4 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=DdaWvKfVILo&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=3&t=90s">01:30</a> Scene 6 - Stand by (far) - 79.8 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=DdaWvKfVILo&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=3&t=108s">01:48</a> Scene 7 - Accelerate (far) RL - 85.0 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=DdaWvKfVILo&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=3&t=126s">02:06</a> Scene 8 - 30 km/h (close) LR - 85.9 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=DdaWvKfVILo&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=3&t=144s">02:24</a> Scene 9 - 50 km/h (far) RL - 85.6 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=DdaWvKfVILo&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=3&t=162s">02:42</a> Scene 10 - Break and stop (close) LR - 83.1 dB SPL</li> </ol> </li> <li>Street sweeper <ol> <li><a href="https://www.youtube.com/watch?v=4ssqZk78JYc&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=4&t=0s">00:00</a> Scene 1 - Stand by (close) - max 81.9 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=4ssqZk78JYc&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=4&t=18s">00:18</a> Scene 2 - Sweeper on (close) - max 93.7 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=4ssqZk78JYc&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=4&t=36s">00:36</a> Scene 3 - Move forward (close) LR - max 94.6 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=4ssqZk78JYc&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=4&t=54s">00:54</a> Scene 4 - Stand by (far) - max 79.6 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=4ssqZk78JYc&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=4&t=72s">01:12</a> Scene 5 - Sweeper on (far) - max 84.4 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=4ssqZk78JYc&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=4&t=90s">01:30</a> Scene 6 - Move forward (far) RL - max 84.3dB SPL</li> </ol> </li> </ul> <p> </p> <p>You can also find the videos in <a href="https://www.youtube.com/playlist?list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB">Youtube</a>.</p> <p>Reference:</p> <p>Llorach, Gerard, Matthias Vormann, Volker Hohmann, Dirk Oetting, Christina Fitschen, Markus Meis, Melanie Krüger, and Michael Schulte. "Vehicle noise: Loudness ratings, loudness models and future experiments with audiovisual immersive simulations." In <em>INTER-NOISE and NOISE-CON Congress and Conference Proceedings</em>, vol. 259, no. 3, pp. 6752-6759. Institute of Noise Control Engineering, 2019.</p>
Properties of flat-spectrum radio-loud Narrow-Line Seyfert 1 Galaxies
<p>Numerical tables of the Spectral Energy Distributions (SEDs) of Figs. 8-13 of the paper http://arxiv.org/abs/1409.3716v2</p>
How loud are neutron star mergers?
<p>We release neutron star merger waveforms computed using fully general relativistic simulations of equal and unequal-mass binaries drawn from the galactic population. The simulations employ finite-temperature microphysical equations of state (LS220, DD2, and SFHo) and neutrino cooling. Please, see</p> <p>http://arxiv.org/abs/1512.06397</p> <p>for details.</p> <p> </p> <p>Each tarball refers to a simulation and contains</p> <ul> <li>Curvature multipolar waveform <span class="math-tex">\(\psi^{(4)}_{\ell m}\)</span></li> <li>Metric multipolar waveform <span class="math-tex">\(h_{\ell m}\)</span></li> <li>Radiated energy and angular momentum</li> </ul> <p>Files:</p> <ul> <li><em>waveforms/Psi4_l?_m?_r200.txt </em> <ul> <li>Columns: <span class="math-tex">\(t,\ \Re{(\psi^{(4)}_{\ell m})},\ \Im{(\psi^{(4)}_{\ell m})} \)</span></li> </ul> </li> <li><em>waveforms/Rh_l?_m?_r200.txt</em> <ul> <li>Columns: <span class="math-tex">\(u/M,\ \Re{(h_{\ell m})}/M,\ \Im{(h_{\ell m})}/M,\ \Re{(\dot{h}_{\ell m})},\ \Im{(\dot{h}_{\ell m}}),\ M\omega_{\ell m},\ A_{\ell m}/M,\ \phi_{\ell m},\ t \)</span></li> </ul> </li> <li><em>waveforms/Ej_r200.txt</em> <ul> <li>Columns: <span class="math-tex">\(E_b,\ j,\ E_\text{rad},\ J_\text{rad},\ t \)</span></li> </ul> </li> </ul> <p>where</p> <ul> <li><span class="math-tex">\(t\)</span> simulation time</li> <li><span class="math-tex">\(u\)</span> retarded time</li> <li><span class="math-tex">\(M\)</span> binary mass</li> <li><span class="math-tex">\(\omega_{\ell m}\)</span> wave frequency</li> <li><span class="math-tex">\(A_{\ell m}\)</span> wave amplitude</li> <li><span class="math-tex">\(\phi_{\ell m}\)</span> wave phase</li> <li><span class="math-tex">\(E_\text{GW}\)</span> radiated energy</li> <li><span class="math-tex">\(J_\text{GW}\)</span> radiated angular momentum</li> <li><span class="math-tex">\(E_b\)</span> binary energy</li> <li><span class="math-tex">\(j\)</span> binary specific angular momentum</li> </ul> <p>Please refer to the paper and references therein for the definition of the different quantities.</p> <p>Units <span class="math-tex">\(c=G=M_\text{Sun}=1\)</span></p>
PsPM-SCRV2: Skin conductance responses to loud sounds at different inter trial intervals.
<p>This dataset includes skin conductance response (SCR) measurements, keypress responses and keypress response times to acoustic stimuli for each of 24 healthy unmedicated participants (12 males and 12 females aged 24+/-4.6 years, identical sample as in SCRV_1). The stimuli are 1s long white noise bursts at 95dB presented over headphones. Mean ISI is varied in a block-wise fashion (3s, 9s, 19s). The experiment follows a single factorial design with three levels of the mean ISI.</p>
PsPM-SCRV10: Skin conductance responses to loud sounds, simultanously recorded from palm, fingers and foot
<p>This dataset includes skin conductance response (SCR) measurements recorded from 3 locations (see section 'Data files' below) for each of 26 healthy unmedicated participants (12 males and 14 females aged 24.4+/-4.9 years) in response to an auditory stimulus (single white noise bursts, 1s length; 10ms ramp; ~85dB). Participants are asked to press a foot pedal upon hearing a stimulus.</p>
PsPM-SCRV3: Skin conductance responses to loud sounds and aversive/neutral pictures.
<p>This dataset includes skin conductance response (SCR) measurements, keypress responses and keypress response times to acoustic and visual stimuli for each of 22 healthy unmedicated participants (11 males and 11 females aged 22+/-4.8 years. The auditory stimuli are 1s long white noise bursts at 95dB presented over headphones. Visual stimuli are aversive pictures drawn from the International Affective Picture System. ITI are selected randomly on each trial from 29s, 34s or 39s.</p>
Vocal imitation of synthesised sounds varying in pitch, loudness and spectral centroid
<p>Dataset from the vocal imitation (production) task. Includes the audio stimuli, extracted audio features (for both stimuli and imitations) and extracted parameters, along with participant metadata. Please see the paper for further details. </p> <p>Details of fields in parameter_data.csv:</p> <p>Participant: index from 0-18</p> <p>sex: male/female</p> <p>singer: 1 if participant had been singer for > 5 years, 0 if not</p> <p>feature: feature that the parameter data was extracted for</p> <p>envelope: up/down for ramps, fast(5Hz)/slow(2Hz) for modulations</p> <p>fail: instances where the imitation failed to meet the criteria (see paper for details)</p> <p>rate: ratio of the modulation rate</p> <p>extent: extent of the modulation</p> <p>range: range of the ramp</p> <p>slope: slope of the ramp</p> <p>stimtype: type of stimulus, where single = single features, pitchamps = pitch & loudness combinations, pitchspecs = pitch & spectral centroid combinations</p> <p>stimlabel: label for each stimulus. The letter indicates the feature (p=pitch, a=loudness, s=spectral centroid) and the number indicates the envelope (1 = ramp down, 2 = ramp up, 3 = 5Hz modulation, 4 = 2Hz modulation)</p>
"I'm something of an untrained, unofficial cultural anthropologist myself. Ihave a business interviewing people to capture their personal histories. I'm always interested how people fit into their world and how they affect their world. I'm a graphic designer who works in the same building as the printing presses that I recorded. Iwalk past the presses every day on my way to talk to the folks in the prepress department. I'm on friendly but not drinking terms with the pressmen. I'm a friend with the prepress manager. Three Heidelberg presses are installed side by side in an open warehouse-like room. The presses are about twenty feet long and about five feet high. With their series of four humps or mounds where each printing cylinder is located, the presses remind one of giant, gray, mechanical caterpillars. Each press has a cyan cylinder, a magenta cylinder, a yellow cylinder and a black cylinder – so the humps are brightly colored. The presses are well lit by banks of fluorescent lights hanging from the ceiling over each press. When you walk into the press room you hear the sound of rock music blaring from a boom box radio mixed with the general din of the presses. It is only when you walk up to a press like Idid for the recordings that you really start to hear the individual strains of clicking, clacking and mechanical, syncopated chattering. When I made my recordings I was intrigued by the subtle variations in the sounds produced by these machines that aren't apparent when you first walk through the door. The pressmen were kind enough to allow me to walk right up to the presses and poke my microphone quite close to the rotating press cylinders. Iuse a Danish Pro Audio microphone about the size of a pencil eraser. An extremely sensitive mic with the capacity for capturing loud sounds such as the presses up close. Rotating the mic to one side or the other focused on the unique sounds coming from one cylinder or the other." [Kevin/KMerrell]18 in Collecting Sounds. Online Sharing of Field Recordings as Cultural Practice
"I'm something of an untrained, unofficial cultural anthropologist myself. Ihave a business interviewing people to capture their personal histories. I'm always interested how people fit into their world and how they affect their world. I'm a graphic designer who works in the same building as the printing presses that I recorded. Iwalk past the presses every day on my way to talk to the folks in the prepress department. I'm on friendly but not drinking terms with the pressmen. I'm a friend with the prepress manager. Three Heidelberg presses are installed side by side in an open warehouse-like room. The presses are about twenty feet long and about five feet high. With their series of four humps or mounds where each printing cylinder is located, the presses remind one of giant, gray, mechanical caterpillars. Each press has a cyan cylinder, a magenta cylinder, a yellow cylinder and a black cylinder – so the humps are brightly colored. The presses are well lit by banks of fluorescent lights hanging from the ceiling over each press. When you walk into the press room you hear the sound of rock music blaring from a boom box radio mixed with the general din of the presses. It is only when you walk up to a press like Idid for the recordings that you really start to hear the individual strains of clicking, clacking and mechanical, syncopated chattering. When I made my recordings I was intrigued by the subtle variations in the sounds produced by these machines that aren't apparent when you first walk through the door. The pressmen were kind enough to allow me to walk right up to the presses and poke my microphone quite close to the rotating press cylinders. Iuse a Danish Pro Audio microphone about the size of a pencil eraser. An extremely sensitive mic with the capacity for capturing loud sounds such as the presses up close. Rotating the mic to one side or the other focused on the unique sounds coming from one cylinder or the other." [Kevin/KMerrell]18
Oenothera affinis Penny ex Loud. (BR0000012159635)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Raw data: Effect of the Relative Timing between Same-Polarity Pulses on Thresholds and Loudness in Cochlear Implant Users
<p>Raw values in dB re. 1 µV of the thresholds and loudness-balanced levels at MCL from:</p> <p>Guérit, F., Marozeau, J., Epp, B., & Carlyon, R. P. (2020). Effect of the Relative Timing between Same-Polarity Pulses on Thresholds and Loudness in Cochlear Implant Users. <em>Journal of the Association for Research in Otolaryngology</em>, 1–14. doi:10.1007/s10162-020-00767-y</p>
A labelled dataset of the loud calls of four vertebrates collected using passive acoustic monitoring in Malaysian Borneo
<p><em>Passive acoustic monitoring data collection</em></p> <p>We collected data using first generation Swift autonomous recording units (ARUs) (Koch et al. 2016) with a microphone sensitivity of −44 (+/−3) dB re 1 V/Pa. The microphone frequency response was not measured but is assumed to be flat (+/− 2 dB) in the frequency range 100 Hz to 7.5 kHz. The analog signal was amplified by 40 dB and digitized (16-bit resolution) using an analog-to-digital converter (ADC) with a clipping level of −/+ 0.9 V. We collected acoustic data from one primary conservation area in Sabah, Malaysia: Danum Valley Conservation Area (with 11 recording units from March to July 2018). Danum Valley covers an area of roughly 440 km², and is characterized by lowland dipterocarp forest. Unlike many tropical forest regions, this area is considered 'aseasonal' due to its lack of clearly differentiated wet and dry seasons (Walsh and Newbery 1999). In Danum Valley, the ARUs recorded at a sampling rate of 16 kHz. All recordings were saved in waveform audio (.wav) format, with files of 2-hr duration. We affixed each recording unit to trees approximately 2-m above the ground and recorded continuously over 24 hours. We set the units on a 750 m grid structure, and preliminary field tests indicate that with these recording settings the detection range of gibbon vocalizations is ~ 400 m.</p> <p> </p> <p><em>Acoustic data processing </em></p> <p>We randomly chose approximately 500 h of recordings from Danum Valley Conservation Area to use to create a training dataset. We used a band-limited energy detector (BLED) to identify potential sounds of interest in the gibbon frequency range. For the BLED detector, we convert the 2-hr recordings into a spectrogram using a 1,600-point (100 ms) Hamming window (3 dB bandwidth = 13 Hz) with 0% overlap and a 2,048-point DFT, with the "seewave" package (Sueur et al. 2008). We then filtered the spectrogram to focus on the desired frequency range, specifically 0.5–1.6 kHz for Northern grey gibbons. For each unique time window in the recording, we determined the total energy across frequency bins which gave a single value for every 100 ms interval. Utilizing the "quantile" function in base R, we established the threshold to delineate signal from noise. Preliminary tests with varied quantile values revealed that the 15th quantile led to optimized recall for our target signal. This approach resulted in 1,439 unique sound events. The sound events were then annotated by a single observer (DJC) using a custom-written function in R to visualize the spectrograms into the following categories: great argus pheasant (<em>Argusianus argus</em>) long and short calls (Clink et al. 2021), helmeted hornbills (<em>Rhinoplax vigil</em>), rhinoceros hornbills (<em>Buceros rhinoceros</em>), female gibbons (<em>Hylobates funereus</em>) and a catch-all “noise” category. </p> <p><em>Update Version 5 and later</em></p> <p>Includes labeled test clips from a second conservation area, Maliau Basin Conservation Area, Sabah, Malaysia recorded during August 2019. The ARUs recorded at a sampling rate of 16 kHz. All recordings were saved in waveform audio (.wav) format, with files of 2-hr duration. We affixed each recording unit to trees approximately 2-m above the ground and recorded continuously over 24 hours.</p> <p> </p> <p><strong>References</strong></p> <p>Clink, D. J., Groves, T., Ahmad, A. H., & Klinck, H. (2021). Not by the light of the moon: Investigating circadian rhythms and environmental predictors of calling in Bornean great argus. <em>PloS one</em>, <em>16</em>(2), e0246564.</p> <p>Koch, R., Raymond, M., Wrege, P., & Klinck, H. (2016). SWIFT: A small, low-cost acoustic recorder for terrestrial wildlife monitoring applications. In <em>North American Ornithological Conference</em> (p. 619). Washington, D.C.</p> <p>Sueur, J., Aubin, T., & Simonis, C. (2008). Seewave: a free modular tool for sound analysis and synthesis. <em>Bioacoustics</em>, <em>18</em>, 213–226.</p> <p>Walsh, R. P., & Newbery, D. M. (1999). The ecoclimatology of Danum, Sabah, in the context of the world’s rainforest regions, with particular reference to dry periods and their impact. <em>Philosophical transactions of the Royal Society of London. Series B, Biological sciences</em>, <em>354</em>(1391), 1869–83. https://doi.org/10.1098/rstb.1999.0528</p> <p>Webb, C. O., & Ali, S. (2002). Plants and vegetation of the Maliau Basin Conservation Area, Sabah, East Malaysia. <em>Final Report to Maliau Basin Management Committee</em>.</p> <p> </p>
Loudness and annoyance ratings of vehicle noise
<p>Loudness and annoyance ratings of the field experiment described in Llorach, Gerard; Oetting, Dirk; Krüger, Melanie; Vormann, Matthias; Fitschen, Christina; Schulte, Michael; Hohmann, Volker; Meis, Markus (2019, September). Vehicle noise: Loudness ratings, loudness models and future experiments with audiovisual immersive simulations. In <em>INTER-NOISE and NOISE-CON Congress and Conference Proceedings</em> (Vol. 259, No. 3, pp. 6752-6759). Institute of Noise Control Engineering. <a href="https://doi.org/10.5281/zenodo.4276090">https://doi.org/10.5281/zenodo.4276090</a>,</p> <p>and of the laboratory experiment in the process of publication.</p> <p>Do not hesitate to contact the main author for more information.</p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>This work received funding from the EU’s H2020 research and innovation program under the MSCA GA 675324 (ENRICH), from the Deutsche Forschungsgemeinschaft (DFG, Cluster of Excellence EXC 1077/1 “Hearing4all”, and SFB1330 Projects B1 and C4).</p>
Investigating the neural basis of the loud-first principle of the Iambic-Trochaic law
Open the record for dataset details and reuse information.
Fig. 1 in Main Functions Of Loud Vocalization In Populations Of Edible Dormouse Glis Glis
Fig. 1. Mean number of "performances" (a) and loud calls (b) found in all studied edible
What distinguishes the host galaxies of radio-loud and radio-quiet AGNs?
<p>Data tables with the physical parameters for the RL and RQ samples defined in the paper.</p>
Annotated audio of the loud nest calls of brooding female rooks in five colonies with individual-level identification
<p>This data accompanies our article entitled '<strong>Individual, but not nest cluster or colonial, vocal signatures in the nest call of female rooks (Corvus frugilegus)</strong>'.</p> <p>The dataset.zip archive contains audio and annotations obtained from recording five different colonies of rooks during the breeding season. The audio was compressed losslessly to .flac files from the original wav format to save storage space. The annotations are the corresponding .txt files containing timestamps and individual identity for each call used in the article, intended for viewing in Audacity (open the flac files, then import the annotation file with the same name). Finally, the tabelau_vocs.tsv file is essentially all the .txt files aggregated for the analysis. </p> <p>Note that to run the analysis, either the .flac audio files must be converted back to .wav files, or the tableau_vocs.tsv file should be edited to point at the .flac files instead of the original .wav files.</p> <p>The intermediate_results.zip archive contains all intermediate results used in the analysis. These are meant to be used with the provided .R scripts in the same archive, which reproduce the statistical analysis and the figures in the article and supplementary.</p> <p>Finally, the supplementary.zip archive contains the supplementary materials provided with the article, including the supplementary.pdf file containing additional analyses, and the example_nest_calls folder containing example of nest calls from various females in the five colonies.</p> <p>The example nest calls are provided at various stages of processing: raw audio extracted from the spectrogram (the .wav files without suffixes), high-pass filter above 100 Hz (.wav files with the 'filtered' suffix), audio with all the processing steps including the spectral gating-based denoising, pre-amplification, and centering (.wav files with the 'processed' suffix), and spectrograms used as inputs for the analysis after dB-scaling and Mel frequenct-scaling (.png files).</p> <p> </p> <p>The rook_vocal_signatures-main.zip archive contains all the code as of the date of submission (August 21st, 2024) used in the article. This was added purely to anonymise the submission as much as possible. Up-to-date code should still be consulted from the link above (will be added after the paper is accepted.</p>
Dataset: "Increasing loudness in audio signals: a perceptually motivated approach to preserve audio quality"
<p>This archive contains the audio files that were used to test the algorithms presented in the paper entitled: "Increasing loudness in audio signals: a perceptually motivated approach to preserve audio quality".</p> <p>Listening is to the samples is interesting, however, as the samples are quite short, the listening experience might strongly differ depending on the software and hardware used. We recommend to also look at the waveforms (using Audacity or others) and spectrum to see how the algorithm perform masking in order to increase the headroom of the samples.</p> <p> </p> <p>The archive is composed as follow:</p> <p>In the folder "Original" are the original kick drums used numbered from 1 to 16.</p> <p>The folder "perceptual_alt" contains the samples modified according to Eq(3). Each subfolder number corresponds to the number of the original sample used. The naming is "x_a_c"+lambda+".wav". Lambda ranges from 0 to 0.95.</p> <p>The folder "perceptual" contains the samples modified according to Eq(2). The naming is "x_pc_c"+c+".wav". c ranges from 0 to 40.</p> <p>The folder "compressor", "hardclip", "softclip" contains samples modified according to, respectively, the compressor, hardclipper and softclipper detailled in the paper. The naming is "x_"+{"c_c","hc_c","sc_c"}+c+".wav", where in this case c ranges from 0 to 40 and correspond to the threshold needed to get the same peak value as the perceptual algorithm (2) for a given perceptual constant c.</p> <p> </p> <p>The samples are taken from https:/ /www.musicradar.com/news/drums/1000-free-drum-samples, the names of each individual sample is:</p> <p>1: CYCdh_AcouKick-19.wav</p> <p>2: CyCdh_K3Snr-10.wav</p> <p>3: CyCdh_K3Tom-05.wav</p> <p>4: CYCdh_K1close_Flam-05.wav</p> <p>5: CYCdh_K1close_Kick-08.wav</p> <p>6: CYCdh_K1close_Rim-07.wav</p> <p>7: CYCdh_K1close_SdSt-07.wav</p> <p>8: CYCdh_K1close_SnrOff-08.wav</p> <p>9: CYCdh_K4-Kick05.wav</p> <p>10: CYCdh_K5-Kick93.wav</p> <p>11: CYCdh_KesKick-08.wav</p> <p>12: CYCdh_LooseKick-08.wav</p> <p>13: CYCdh_VinylK3-Kick01.wav</p> <p>14: CYCdh_VinylK3-Kick02.wav</p> <p>15: CYCdh_VinylK3-Snr02.wav</p> <p>16: CYCdh_VinylK4-Tom03.wav</p>
The Effect of LSVT LOUD for Mandarin-speaking Individuals With Hypokinetic Dysarthria Due to Parkinson's Disease
ClinicalTrials.gov study NCT02506205. IPD Sharing: Not stated. Countries: 1. Publications: 6.
Determining Change In Blood Pressure Due to Environment and Loudness
ClinicalTrials.gov study NCT05394376. IPD Sharing: Not stated. Countries: 1. Publications: 1.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.