Find research datasets worth reusing

opencc-by-4.0Jul 2020View details →

Optimization of Convolution Reverberation (sound samples)

Sound samples of the paper "Optimization of Convolution Reverberation" by Sadjad Siddiq. To be published in the proceedings of DAFx20.

opencc-zeroMar 2021View details →

Self-organising cicada choruses respond to the local sound and light environment

1. Periodical cicadas exhibit an extraordinary capacity for self-organising spatially synchronous breeding behavior. The regular emergence of periodical cicada broods across the US is a phenomenon of longstanding public and scientific interest, as the cicadas of each brood emerge in huge numbers and briefly dominate their ecosystem. During the emergence, the 17-year periodical cicada species Magicicada cassini is found to form synchronised choruses, and we investigated their chorusing behavior from the standpoint of spatial synchrony. 2. Cicada choruses were observed to form in trees, calling regularly every five seconds. In order to determine the limits of this self-organising behaviour, we set out to quantify the spatial synchronisation between cicada call choruses in different trees, and how and why this varies in space and time. 3. We performed 20 simultaneous recordings in Clinton State Park, Kansas, in June 2015 (Brood IV) with a team of citizen-science volunteers using consumer equipment (smartphones). We use a wavelet approach to show in detail how spatially synchronous, self-organised chorusing varies across the forest. 4. We show how conditions that increase the strength of audio interactions between cicadas also increase the spatial synchrony of their chorusing. Higher forest canopy light levels increase cicada activity, corresponding to faster and higher-amplitude chorus cycling and to greater synchrony of cycles across space. We implemented a relaxation-oscillator-ensemble model of interacting cicadas, finding that a tendency to call more often, driven by light levels, results in all these effects. 5. Results demonstrate how the capacity to self-organise in ecology depends sensitively on environmental conditions. Spatially correlated modulation of cycling rate by an external driver can also promote self-organisation of phase synchrony.

opencc-zeroDec 2019View details →

A narrow ear canal reduces sound velocity to 1 create additional acoustic inputs in a micro-scale insect ear

Located in the forelegs, katydid ears are unique among arthropods in having outer, middle and inner component, analogous to the mammalian ear. Unlike mammals, sound is received externally, and internally via a narrow ear canal (EC) derived from the respiratory tracheal system. Inside the EC sound travels slower than in free air, causing temporal and pressure differences between external and internal inputs. The delay is suspected to arise as sound propagation changes from adiabatic to isothermal, imposed by EC geometry. If true, a reduction in sound velocity should persist independently of the gas composition in the EC. Integrating experimental (laser Doppler vibrometry, micro-CT) and numerical methods, we demonstrate that the narrow radius of the EC is the major cause of the signal time delay. Results imply that the EC is asymmetrically bifurcated, creating four notable auditory paths for each ear. Implication of methods and findings in avian hearing are discussed.

opencc-zeroAug 2020View details →

Auditory cortex shapes sounds responses in the inferior colliculus

The extensive feedback from the auditory cortex (AC) to the inferior colliculus (IC) supports critical aspects of auditory behavior, but has not been extensively characterized. Previous studies demonstrated that activity in IC is altered by focal electrical stimulation and pharmacological inactivation of AC, but these methods lack the ability to selectively manipulate projection neurons. We measured the effects of selective optogenetic modulation of cortico-collicular feedback projections on IC sound responses in mice. Activation of feedback increased spontaneous activity and decreased stimulus selectivity in IC, whereas suppression had no effect. To further understand how microcircuits in AC may control collicular activity, we optogenetically modulated different cortical neuronal subtypes, specifically parvalbumin-positive (PV) and somatostatin-positive (SOM) inhibitory interneurons. We found that modulating either type of interneuron did not affect IC sound-evoked activity. Combined, our results identify that activation of excitatory projections, but not inhibition-driven increases in cortical activity, affects collicular sound responses.

opencc-by-4.0Feb 2020View details →

SONYC Urban Sound Tagging (SONYC-UST): a multilabel dataset from an urban acoustic sensor network

SONYC Urban Sound Tagging (SONYC-UST): a multilabel dataset from an urban acoustic sensor network Version 2.3, September 2020   Created by Mark Cartwright (1,2,3), Jason Cramer (1), Ana Elisa Mendez Mendez (1), Yu Wang (1), Ho-Hsiang Wu (1), Vincent Lostanlen (1,2,4), Magdalena Fuentes (1), Graham Dove (2), Charlie Mydlarz (1,2), Justin Salamon (5), Oded Nov (6), Juan Pablo Bello (1,2,3) <ol> <li>Music and Audio Research Lab, New York University</li> <li>Center for Urban Science and Progress, New York University</li> <li>Department of Computer Science and Engineering, New York University</li> <li>Cornell Lab of Ornithology</li> <li>Adobe Research</li> <li>Department of Technology Management and Innovation, New York University</li> </ol>   Publication If using this data in an academic work, please reference the DOI and version, as well as cite the following paper, which presented the data collection procedure and the first version of the dataset: Cartwright, M., Cramer, J., Mendez, A.E.M., Wang, Y., Wu, H., Lostanlen, V., Fuentes, M., Dove, G., Mydlarz, C., Salamon, J., Nov, O., Bello, J.P. SONYC-UST-V2: An Urban Sound Tagging Dataset with Spatiotemporal Context. In Proceedings of the Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), 2020. <a href="https://arxiv.org/abs/2009.05188">[pdf]</a>   Description SONYC Urban Sound Tagging (SONYC-UST) is a dataset for the development and evaluation of machine listening systems for realistic urban noise monitoring. The audio was recorded from the <a href="https://wp.nyu.edu/sonyc">SONYC</a> acoustic sensor network. Volunteers on the  <a href="https://zooniverse.org">Zooniverse</a> citizen science platform tagged the presence of 23 classes that were chosen in consultation with the New York City Department of Environmental Protection. These 23 fine-grained classes can be grouped into 8 coarse-grained classes. The recordings are split into three sets: training, validation, and test. The training and validation sets are disjoint with respect to the sensor from which each recording came, and the test set is displaced in time. For increased reliability, three volunteers annotated each recording. In addition, members of the SONYC team subsequently created a subset of verified, ground-truth tags using a two-stage annotation procedure in which two annotators independently tagged and then collectively resolved any disagreements. This subset of recordings with verified annotations intersects with all three recording splits. All of the recordings in the test set have these verified annotations.  In v2 version of this dataset, we have also included coarse spatiotemporal context information to aid in tag prediction when time and location is known. For more details on the motivation and creation of this dataset see the <a href="http://dcase.community/challenge2020/task-urban-sound-tagging-with-spatiotemporal-context">DCASE 2020 Urban Sound Tagging with Spatiotemporal Context Task website</a>.   Audio data The provided audio has been acquired using the SONYC acoustic sensor network for urban noise pollution monitoring. Over 60 different sensors have been deployed in New York City, and these sensors have collectively gathered the equivalent of over 50 years of audio data, of which we provide a small subset. The data was sampled by selecting the nearest neighbors on VGGish features of recordings known to have classes of interest. All recordings are 10 seconds and were recorded with identical microphones at identical gain settings. To maintain privacy, we quantized the spatial information to the level of a city block, and we quantized the temporal information to the level of an hour. We also limited the occurrence of recordings with positive human voice annotations to one per hour per sensor.   Label taxonomy The label taxonomy is as follows: <ol> <li>engine 1: small-sounding-engine 2: medium-sounding-engine 3: large-sounding-engine X: engine-of-uncertain-size</li> <li>machinery-impact 1: rock-drill 2: jackhammer 3: hoe-ram 4: pile-driver X: other-unknown-impact-machinery</li> <li>non-machinery-impact 1: non-machinery-impact</li> <li>powered-saw 1: chainsaw 2: small-medium-rotating-saw 3: large-rotating-saw X: other-unknown-powered-saw</li> <li>alert-signal 1: car-horn 2: car-alarm 3: siren 4: reverse-beeper X: other-unknown-alert-signal</li> <li>music 1: stationary-music 2: mobile-music 3: ice-cream-truck X: music-from-uncertain-source</li> <li>human-voice 1: person-or-small-group-talking 2: person-or-small-group-shouting 3: large-crowd 4: amplified-speech X: other-unknown-human-voice</li> <li>dog 1: dog-barking-whining</li> </ol> The classes preceded by an <code>X</code> code indicate when an annotator was able to identify the coarse class, but couldn’t identify the fine class because either they were uncertain which fine class it was or the fine class was not included in the taxonomy. <code>dcase-ust-taxonomy.yaml</code> contains this taxonomy in an easily machine-readable form.   Data splits This release contains a training subset (13538 recordings from 35 sensors), and validation subset (4308 recordings from 9 sensors), and a test subset (669 recordings from 48 sensors). The training and validation subsets are disjoint with respect to the sensor from which each recording came. The sensors in the test set will not disjoint from the training and validation subsets, but the test recordings are displaced in time, occurring after any of the recordings in the training and validation subset. The subset of recordings with verified annotations (1380 recordings) intersects with all three recording splits.  All of the recordings in the test set have these verified annotations.   Annotation data The annotation data are contained in <code>annotations.csv</code>, and encompass the training, validation, and test subsets. Each row in the file represents one multi-label annotation of a recording—it could be the annotation of a single citizen science volunteer, a single SONYC team member, or the agreed-upon ground truth by the SONYC team (see the annotator_id column description for more information).  Note that since the SONYC team members annotated each class group separately, there may be multiple annotation rows by a single SONYC team annotator for a particular audio recording.     Columns split The data split. (train, validate, test) sensor_id The ID of the sensor the recording is from. audio_filename The filename of the audio recording annotator_id The anonymous ID of the annotator. If this value is positive, it is a citizen science volunteer from the Zooniverse platform. If it is negative, it is a SONYC team member. If it is <code>0</code>, then it is the ground truth agreed-upon by the SONYC team. year The year the recording is from. week The week of the year the recording is from. day The day of the week the recording is from, with Monday as the start (i.e. <code>0</code>=Monday). hour The hour of the day the recording is from borough The NYC borough in which the sensor is located (<code>1</code>=Manhattan, <code>3</code>=Brooklyn, <code>4</code>=Queens). This corresponds to the first digit in the 10-digit NYC parcel number system known as Borough, Block, Lot (BBL). block The NYC block in which the sensor is located. This corresponds to digits 2—6 digit in the 10-digit NYC parcel number system known as Borough, Block, Lot (BBL). latitude The latitude coordinate of the block in which the sensor is located. longitude The longitude coordinate of the block in which the sensor is located. <coarse_id>-<fine_id>_<fine_name>_presence Columns of this form indicate the presence of fine-level class. <code>1</code> if present, <code>0</code> if not present. If <code>-1</code>, then the class was not labeled in this annotation because the annotation was performed by a SONYC team member who only annotated one coarse group of classes at a time when annotating the verified subset. <coarse_id>_<coarse_name>_presence Columns of this form indicate the presence of a coarse-level class. <code>1</code> if present, <code>0</code> if not present. If <code>-1</code>, then the class was not labeled in this annotation because the annotation was performed by a SONYC team member who only annotated one coarse group of classes at a time when annotating the verified subset. These columns are computed from the fine-level class presence columns and are presented here for convenience when training on only coarse-level classes. <coarse_id>-<fine_id>_<fine_name>_proximity Columns of this form indicate the proximity of a fine-level class. After indicating the presence of a fine-level class, citizen science annotators were asked to indicate the proximity of the sound event to the sensor. Only the citizen science volunteers performed this task, and therefore this data is not included in the verified annotations. This column may take on one of the following four values: (<code>near</code>, <code>far</code>, <code>notsure</code>, <code>-1</code>). If <code>-1</code>, then the proximity was not annotated because either the annotation was not performed by a citizen science volunteer, or the citizen science volunteer did not indicate the presence of the class.   Conditions of use Dataset created by Mark Cartwright, Jason Cramer, Ana Elisa Mendez Mendez, Yu Wang, Ho-Hsiang Wu, Vincent Lostanlen, Magdalena Fuentes, Graham Dove, Charlie Mydlarz, Justin Salamon, Oded Nov, and Juan Pablo Bello The SONYC-UST dataset is offered free of charge under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license: <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a> The dataset and its contents are made available on an “as is” basis and without warranties of any kind, including without limitation satisfactory quality and conformity, merchantability, fitness for a particular purpose, accuracy or completeness, or absence of errors. Subject to any liability that may not be excluded or limited by law, New York University is not liable for, and expressly excludes all liability for, loss or damage however and whenever caused to anyone by any use of the SONYC-UST dataset or any part of it.   Feedback Please help us improve SONYC-UST by sending your feedback to: <ul> <li>Mark Cartwright: <a href="mailto:mcartwright@gmail.com">mcartwright@gmail.com</a></li> </ul> In case of a problem, please include as many details as possible.   Acknowledgments We would like to thank all the Zooniverse volunteers who continue to contribute to our project. This work is supported by <a href="https://www.nsf.gov/awardsearch/showAward?AWD_ID=1544753">National Science Foundation award 1544753</a>.   Change log <ul> <li>2.3 Added the ground truth annotations for the test set, and regrouped the audio files for upload to Zenodo.</li> <li>2.2 Added the audio for the test set (audio-eval.tar.gz).</li> <li>2.1 The DCASE 2020 development dataset. 14778 new recordings added along with coarse spatiotemporal context information.</li> <li>1.0 Data is the same as v0.4. Publication added to README.</li> <li>0.4 Fixed error in annotations. Previously, the coarse class "machinery-impact" was accidentally indicated as present whenever "non-machinery-impact" was present regardless of the presence of "machinery-impact". This error has been fixed.</li> <li>0.3 Test set annotations added</li> <li>0.2 Test set audio files added</li> </ul>

opencc-by-4.0Nov 2019View details →

OPEN-WINDOW: SOUND EVENT DATABASE FOR RESEARCH AND DEVELOPMENT

(1) Background: Situated in the domain of urban sound scene classification by humans and machines, the research in this project will be a first step towards mapping urban noise pollution experienced indoors and finding ways to reduce its negative impact in peoples' homes. The acoustic distinction between outdoor and indoor scenes is an active research field and can be automated with some success. A much subtler difference is the change in the indoor soundscape induced by an open window. Being able to determine this, however, would allow applications in warning systems and be a prerequisite for an app-based urban sound mapping project. Acoustic detection requires neither line of sight nor sensors at the window frame or knowledge of the number of windows or their size. The task, however, varies substantially in difficulty with the amount of sound inside and outside. From the point of machine classification, the lack of specificity is the most problematic aspect: Very few sounds if any can be assumed to originate exclusively from outside and be present at all times to aid automatic detection. The required generalisation ability, however, can be assumed for humans, who might also use very subtle cues in the change of reverberations.   (2) Dataset (a) Recording locations The recordings have been made at three different locations.  <ul> <li>Farm: A farm in Brook, Surrey, United Kingdom. The recordings were made in an open-plan studio flat area in the centre of the farm. The recordings in this location have the lowest levels of background noise, due mainly to a quiet environmental surrounding.</li> <li>Office 1: An office at the University of Surrey, Guildford, United Kingdom. The recordings were made in an open-plan office located on the first floor, at the Centre for Vision, Speech and Signal Processing (CVSSP). Since this office accommodates 16 researchers, recordings in this location have the highest level of background noise</li> <li>Office 2: An office at the University of Surrey, Guildford, United Kingdom. The recordings were made in a small size open-plan office at the CVSSP. This office accommodates 8 researchers and the recordings made in this office considered to have a medium level of background noise.</li> </ul> (b) Recording equipment The recordings made at the two offices and a studio flat in a farm used a dedicated laptop, Focusrite Clarett 4pre USB external sound card (44,100 Hz sample rate at 16 bits per sample) 1, and a Behringer ECM 8000 microphone. (c) Recording setup The Behringer ECM 8000 microphone is connected to the External Line Return (XLR) input of the Focusrite Clarett external sound card via an XLR cable. The external sound card is connected to the dedicated laptop and controlled using Ableton Live 10 software for setting configurations and exporting the recorded audio files. The microphone is located approximately 10 cm away from the window and fixed using a microphone holder. At each location 90 audio sessions are recorded; 60 one minute recordings for static state setup and 30 fifteen seconds recordings for transitional state setup. (d) File naming conventions The naming convention for audio recording is as follows: [Location] [State] [Time] [IDX] [State] will be one of the following: “O stands for open, C stands for Close, OC means a transition from Open to Close and CO stands for a transition from Close to Open.” [Time] stamp will be one of the following: “AM stands for morning between 9:00 to 12:00, N stands for noon which is between 13:00 to 15:00 and PM which stands for an afternoon which is between 17:00 to 20:00.” [IDX] is representing the file ID number. For example, “Farm C PM 01.wav”, means this file is recorded at the farm and in the afternoon when the window is closed and the file ID is 01. (e) Dataset acquisition: A recording kit consisting of a dedicated laptop and microphone will be given to volunteers. Custom-programmed software will remind the user to specify the window state (establishing the so-called ground truth). (f) Specifications   - Open-Window contains 270 audio recordings totalling 3.37 hours of audio.   - Each audio recording belongs to one of the four classes representing the window states; two stationary states (Open, Close) and two transitional states (Open-Close, Close-Open).   - The recordings were carried out in different locations and at different times of the day.       - Three locations: Office1, Office2, Farm       - Three periods of the day: Morning, Afternoon, Evening   - The recordings are split into six-folds.       - Fold 1 is the test set.       - Fold 2 is the validation set.       - Folds 3-6 comprise the training set.     Each fold is balanced in terms of the class and location distribution.   - The annotations/metadata can be found in annotations.csv.   - The recordings for the stationary states are approximately 60 seconds, while the recordings for the transitional states are approximate 15 seconds.   - The format of the recordings is 2-channel 16-bit PCM sampled at 44.1 kHz.

opencc-by-4.0Nov 2020View details →

Test dataset for separation of speech, traffic sounds, wind noise, and general sounds

The dataset was generated as part of the paper: Deep Complex U-Net Ensemble for Outdoor Urban Sound Source Separation, K. Arendt, A. Szumaczuk, B. Jasik, P. Masztalski, K. Piaskowski, M. Matuszewski, K. Nowicki, P. Zborowski. It contains various sounds from the Audio Set [1] and spoken utterances from VCTK [2] and DNS [3] datasets. Contents: sr_8k/     mix_clean/     s1/     s2/     s3/     s4/ sr_16k/     mix_clean/     s1/     s2/     s3/     s4/ sr_48k/     mix_clean/     s1/     s2/     s3/     s4/ Each directory contains 512 audio samples in different sampling rate (sr_8k - 8 kHz, sr_16k - 16 kHz, sr_48k - 48 kHz). The audio samples for each sampling rate are different as they were generated randomly and separately. Each directory contains 5 subdirectories: - mix_clean - mixed sources, - s1 - source #1 (general sounds), - s2 - source #2 (speech), - s3 - source #3 (traffic sounds), - s4 - source #4 (wind noise). The sound mixtures were generated by adding s2, s3, s4 to s1 with SNR ranging from -10 to 10 dB w.r.t. s1. REFERENCES: [1] Jort F. Gemmeke, Daniel P. W. Ellis, Dylan Freedman,     Aren Jansen, Wade Lawrence, R. Channing Moore,     Manoj Plakal, and Marvin Ritter, “Audio set: An ontology     and human-labeled dataset for audio events,” in     Proc. IEEE ICASSP 2017, New Orleans, LA, 2017. [2] Christophe Veaux, Junichi Yamagishi, and Kirsten Mac-     Donald, “CSTR VCTK corpus: English multi-speaker     corpus for CSTR voice cloning toolkit, [sound],”     https://doi.org/10.7488/ds/1994, University of Edinburgh.     The Centre for Speech Technology Research     (CSTR). 2017. [3] Chandan K. A. Reddy, Ebrahim Beyrami, Harishchandra     Dubey, Vishak Gopal, Roger Cheng, Ross Cutler,     Sergiy Matusevych, Robert Aichner, Ashkan Aazami,     Sebastian Braun, Puneet Rana, Sriram Srinivasan, and     Johannes Gehrke, “The interspeech 2020 deep noise     suppression challenge: Datasets, subjective speech     quality and testing framework,” 2020.

FIGURE 7 in Biology, sounds and vibratory signals of hooded katydids (Orthoptera: Tettigoniidae: Phyllophorinae)

FIGURE 7. Vibratory (A–H, K) and sound (I–J) signals of male (A–F, H–K) and female (G) S. grandis: territorial signals of two males (A) and rhythmic (C) signals, territorial signal (top beam) and electromyogram (bottom beam), (B). For explanation see text.

FIGURE 6 in Biology, sounds and vibratory signals of hooded katydids (Orthoptera: Tettigoniidae: Phyllophorinae)

FIGURE 6. Frequency spectra (in linear scale) of sound signals of P. kotoshoensis (A, B) and S. grandis (C-H): male protest stridulatory signals (A, C), female protest stridulatory signals (B, D), nymph protest stridulatory signals (E), male wing protest signal (F), female wing protest signal (G), male courtship stridulatory signal (H).

FIGURE 5 in Biology, sounds and vibratory signals of hooded katydids (Orthoptera: Tettigoniidae: Phyllophorinae)

FIGURE 5. Stridulatory (A–E) and wing (F, G) sound protest signals of hooded katydids: oscillograms of stridulatory sounds of: (A) male P. kotoshoensis, (B) female P. kotoshoensis, (C, F) male S. grandis, (D, G) female S. grandis, and (E) female nymph of S. grandis. Time scale 50 ms.

FIGURE 3 in Biology, sounds and vibratory signals of hooded katydids (Orthoptera: Tettigoniidae: Phyllophorinae)

FIGURE 3. Copulation (A–C), male genitalia (D) and spermatophore (E) of S. grandis. D: dl—dorsal phallic lobes, vl—ventral phallic lobes; sensu Ander, 1956 (see Chamorro-Rengifo & Lopes-Andrade (2014) for a more detailed nomenclature), c—cercus, scale bar 2 mm, E: n—neck, amp—sperm ampulla, scale bar 10 mm. Photos: O. Korsunovskaya (A–D), and M. Berezin (E).

FIGURE 4 in Biology, sounds and vibratory signals of hooded katydids (Orthoptera: Tettigoniidae: Phyllophorinae)

FIGURE 4. Coxosternal stridulatory apparatus of S. grandis (A, C–F) and P. kotoshoensis (B): A—ventral view of male with left stridulatory organ noted by square, B—right stridulatory organ under high magnification, C, E - SEM image of internal surface of male (C) and female (E) metasternal plate, D, F - SEM image of male (D) and female (F) metacoxa. Scales 10 mm (A) and 1 mm (B–F).

FIGURE 2 in Biology, sounds and vibratory signals of hooded katydids (Orthoptera: Tettigoniidae: Phyllophorinae)

FIGURE 2. Habitus (A), head and pronotum (B) of female P. kotoshoensis, habitus (C) and cerci and genital plate in dorsal view (left) and ventral view (right) (D) of male S. grandis, head and pronotum of second instar nymph (E) of S. grandis, note three dark denticle on each side of pronotum, egg of S. grandis (G, F). Scales: 1 cm (A–C), 5 mm (D), 2 mm (E, F), 100 µm (G). Photos: K.-G. Heller (A, B), O. Korsunovskaya (C, D), and M. Berezin (E, F).

opencc-zeroDec 2012View details →

Data from: Replicate divergence between and within sounds in a marine fish: the copper rockfish (Sebastes caurinus)

The evolution of population structure in marine organisms and its relevance to conservation has recently received increasing attention. We tested the degree of genetic subdivision among ten populations of copper rockfish (Sebastes caurinus) representing paired samples of outer coast and the heads of five replicate sounds on the west coast of Vancouver Island, British Columbia using 17 microsatellite DNA loci. Overall, subdivision (FST) was low (FST = 0.031), but consistently higher between paired coast and head of inlet sites (mean FST = 0.047) compared to among five coast sites (mean FST = -0.001) or among the five head of inlet sites (mean FST = 0.026). Heterozygosity, allelic richness, and estimates of effective population size were also consistently lower in head of inlet sites than in coast sites. Bayesian analysis of population structure identified two genetic groups across all samples, a single genetic group amongst only coast samples, two genetic groups amongst head of inlet samples, and two genetic groups within each sound analysed separately. Head of inlet copper rockfish were also consistently shorter with lower condition factors, and grew more slowly than fish collected from coast sites. Our results implicate coast- head of inlet habitat transitions in driving the evolution of population structure, likely resulting from reduced physical connectivity and selection against immigrants in contrasting environments. Coast sites appear to be well served by existing marine protected areas. By contrast, head of inlet sites may require more specific local conservation measures as they appear to be less well connected to adjacent coast sites as well as to each other.

opencc-zeroDec 2016View details →

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.

opencc-zeroDec 2015View details →

Data from: Left cortical specialization for visual letter strings predicts rudimentary knowledge of letter-sound association in preschoolers

Reading, one of the most important cultural inventions of human society, critically depends on posterior brain areas of the left hemisphere in proficient adult readers. In children, this left hemispheric cortical specialization for letter strings is typically detected only after approximately 1 y of formal schooling and reading acquisition. Here, we recorded scalp electrophysiological (EEG) brain responses in 5-y-old (n = 40) prereaders presented with letter strings appearing every five items in rapid streams of pseudofonts (6 items per second). Within 2 min of recording only, letter strings evoked a robust specific response over the left occipito-temporal cortex at the predefined frequency of 1.2 Hz (i.e., 6 Hz/5). Interindividual differences in the amplitude of this electrophysiological response are significantly related to letter knowledge, a preschool predictor of later reading ability. These results point to the high potential of this rapidly collected behavior-free measure to assess reading ability in developmental populations. These findings were replicated in a second experiment (n = 26 preschool children), where familiar symbols and line drawings of objects evoked right-lateralized and bilaterally specific responses, respectively, showing the specificity of the early left hemispheric dominance for letter strings. Collectively, these findings indicate that limited knowledge of print in young children, before formal education, is sufficient to develop specialized left lateralized neuronal circuits, thereby pointing to an early onset and rapid impact of left hemispheric reentrant sound mapping on posterior cortical development.

opencc-zeroDec 2015View details →

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.

opencc-zeroDec 2016View details →

Data from: Species composition of First Nation whaling hunts in the Clayoquot Sound region of Vancouver Island as estimated through genetic analyses

Deepening our understanding of whale hunting practices is important from both cultural and biological perspectives. Many cultures practice whaling activities, including the Nuu-cha-nulth Nations of the Pacific Northwest. Nuu-cha-nulth cultural lifeways and laws include great care and respect for these animals that provide so much wealth to their communities. The disruption of this culture by colonial governments, combined with the decimation of whale populations through industrial whaling, led to the loss of traditional whaling activities and a gap between contemporary and historical knowledge and practices. From a scientific perspective, knowledge of current whale populations is compromised by lack of data with regards to abundance and distribution of these populations prior to colonial industrial whaling. Analysis of whale bones from First Nation whaling sites are valuable for addressing both these issues by identifying the species landed by communities in traditional hunts, and by providing a sample of the presence and distribution of whale species before colonial industrial whaling. Genetic analyses of 95 bones collected from 7 traditional whaling sites of Nuu-chah-nulth First Nations in the Pacific Northwest were conducted, as well as 11 bones from a colonial industrial whaling site in the area that operated from 1905 to 1918. Specifically, we sequenced a portion of the mitochondrial control region and cytochrome-b gene to identify what species were taken, and in what proportions. We found that 45.4% of the bones were from grey whales (Eschrictius robustus), 43.0% were from humpback whales (Megaptera novaeangliae), 8.1% were from North Pacific right whales (Eubalaena japonica), and the remaining 3.5% were from fin whales (Balaenoptera physalus). These results reveal catch compositions of historical hunts, and therefore help to inform our understanding of historic practices and preferences, and provide information on which species were present in these areas.

opencc-zeroDec 2017View details →

Data from: The Chirocopter: a UAV for recording sound and video of bats at altitude

1. Most recordings of bats are conducted with fixed equipment, which relies on opportunistic data collection. Unmanned aerial vehicles (such as drones) are considered inappropriate for recording bats due to ultrasound noise constraints. 2. We developed a UAV system that physically isolates UAV noise so we can record, with 3D maneuverability, ultrasonic audio and spatial thermal data of bat flight at altitude. 3. We tested the noise of our UAV with various payloads and microphone configurations to characterize the ultrasonic noise of our system, physically isolate drone noise from the microphone, and maximize UAV flight performance. 4. Over 84 minutes of recordings, we captured 3,847 echolocation signals from bats with corresponding thermal data of bat flight. Our system provides a feasible mechanism to capture both acoustic and video data of bats aloft at flexible locations and altitudes. 5. We include information on how to extend our method to apply to acoustic recordings in the audible (20 Hz-20 kHz) range for recording sounds of other taxa.