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1,300 results for “Sounds”
Fig. 6. Waveform a in Characterization of stridulatory structures and sounds of the larger Mexican pine beetle, Dendroctonus approximatus (Coleoptera: Curculionidae: Scolytinae)
Fig. 6. Waveform a), and spectrogram b) of a male interrupted chirp showing individual syllables within each chirp.
Fig. 5. Waveform a in Characterization of stridulatory structures and sounds of the larger Mexican pine beetle, Dendroctonus approximatus (Coleoptera: Curculionidae: Scolytinae)
Fig. 5. Waveform a), and spectrogram b) of female simple chirp in a disturbance context. The waveform shows the relative amplitude in generic units and the spectrogram shows frequency over time with darker shades indicating higher relative energy.
Fig. 4. Waveform a in Characterization of stridulatory structures and sounds of the larger Mexican pine beetle, Dendroctonus approximatus (Coleoptera: Curculionidae: Scolytinae)
Fig. 4. Waveform a), spectrogram b), and spectral profile c) of male simple chirp in a disturbance context. The waveform shows the relative amplitude in generic units and the spectrogram shows frequency over time with darker shades indicating higher relative energy. The spectral profile was taken at the midpoint of the first chirp, highlighted in a and b.
Fig. 2 in Characterization of stridulatory structures and sounds of the larger Mexican pine beetle, Dendroctonus approximatus (Coleoptera: Curculionidae: Scolytinae)
Fig. 2. File length was measured posteriorly from the most posterior ridge (see lower arrow) up anteriorly to the last well-developed ridge (top arrow). Ridges at the anterior edge become poorly developed (circle) and these were not measured. See Methods for further description.
Fig. 3 in Characterization of stridulatory structures and sounds of the larger Mexican pine beetle, Dendroctonus approximatus (Coleoptera: Curculionidae: Scolytinae)
Fig. 3. Temporal and spectral characteristics assessed for simple chirps made in a disturbance context. Train of individual pulses a), waveform b), and spectrogram c) from sample chirp produced by male in a disturbance context. The peak frequency (3.468 kHz) is noted for the first recorded chirp (occurring at 0.3 s).
Fig. 1 in Characterization of stridulatory structures and sounds of the larger Mexican pine beetle, Dendroctonus approximatus (Coleoptera: Curculionidae: Scolytinae)
Fig. 1. Phloem slide positioned under dissecting scope. The ultrasonic electronic insertion microphone is shown positioned in the center of the slide (see circle), inserted into the tunnel entrance. A small piezo transducer, shown at the far end of the phloem slide, was not used in recordings for this study.
TUT Rare sound events, Evaluation dataset
<p>TUT Rare Sound events 2017, evaluation dataset consists of source files for creating mixtures of rare sound events (classes baby cry, gun shot, glass break) with background audio, as well a set of readily generated mixtures and recipes for generating them.</p> <p>The "source" part of the dataset consists of two subsets:</p> <ul> <li>background recordings from 15 different acoustic scenes,</li> <li>recordings with the target rare sound events from three classes, accompanied by annotations of their temporal occurrences.</li> </ul> <p>The mixture set consists of two 1500 mixtures (500 per target class, with half of the mixtures not containing any target class events). </p> <p>The collection of the background recording data has been financially supported by European Research Council under the European Unions H2020 Framework Programme through ERC Grant Agreement 637422 EVERYSOUND.</p>
TUT Rare sound events, Development dataset
<p>TUT Rare Sound events 2017, development dataset consists of source files for creating mixtures of rare sound events (classes baby cry, gun shot, glass break) with background audio, as well a set of readily generated mixtures and recipes for generating them.</p> <p>The "source" part of the dataset consists of two subsets:</p> <ul> <li>background recordings from 15 different acoustic scenes,</li> <li>recordings with the target rare sound events from three classes, accompanied by annotations of their temporal occurrences,</li> <li>a set of meta files providing the cross-validation setup: lists of background and target event recordings split into training and test subsets (called "devtrain" and "devtest", respectively, indicating they are provided as the development dataset, as opposed to the evaluation dataset released separately). </li> </ul> <p>The mixture set consists of two subsets (training and testing), each containing ~1500 mixtures (~500 per target class in each subset, with half of the mixtures not containing any target class events). </p> <p> </p> <p>The collection of the background recording data has been financially supported by European Research Council under the European Unions H2020 Framework Programme through ERC Grant Agreement 637422 EVERYSOUND.</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>
Common Sounds in Bedrooms (CSIBE) Corpora
<p>These audio corpora can be used for domestic sound event recognition. The current sound events in the datasets: baby cry, bell, cat meow, cicada, dog bark, fart, guitar, laugh, parrot, piano, speech, traffic, vacuum cleaner and the background sounds. This latter class can model the ambient sounds which are probably not important for a robot or their short audition is not sufficient for a human without visual cue: door, drawer, keys, knock, pen, chair, cup, keyboard, breathing, throat, cough, microwave, steps and zip.</p> <p>Currently, there are two parts:</p> <p>- <strong> CSIBE-RAW:</strong> Human speech and other events were collected from the internet in this dataset, complemented with new recordings.The samples had excellent and clear sound quality, they were stored in mono WAV format with 16 bit depth, 44.1 kHz sampling rate. All files were labeled according to the sound event type.</p> <p>- <strong>CSIBE-AIBO: </strong>CSIBE-RAW was recorded again with a robot. The original sounds were played back on a high-quality speaker and the result was recorded with the stereo microphones of a Sony ERS-7 robot in a silent room. Four configurations were used relative to the robot:</p> <p>1. Reverberant room, speaker was 1 meter away, 30⁰ counterclockwise to the head.<br> 2. Non-reverberant room, speaker was 1 meter away, 30⁰ counterclockwise to the head.<br> 3. Reverberant room, speaker was 3 meters away, 1 meter high, 180⁰ clockwise to the head.<br> 4. Non-reverberant room, speaker was 3 meters away, 1 meter high, 180⁰ clockwise to the head.</p> <p>These conditions were varied to get such sound recordings which contains dynamics of the robot microphones, different reverberant conditions and various positions of the sound sources.</p> <p>All licenses of the original sounds are included in the dataset.</p>
Classification of Phonocardiograms with Convolutional Neural Networks-Figure 2. PCGs for sample sound file of 103-1305031931979-B
<p>Heart sounds that provide valuable diagnostic information in clinical examinations are among the most important physiological signals in the human body. However, heart sounds include noise, such as external sounds and lung sounds, caused by signal recording conditions. Noisy heart sound signal negatively affects the diagnosis of the doctor (Denga & Hanb, 2018). Digital filters are often used to filter biomedical signal. Digital filtering is defined as the acquisition of desired frequency values according to the characterization of the desired filter in order to improve the signal according to the intended use (Shenoi, 2005; Thede, 1995). Based on the experience gained from previous studies, an elliptic filter was used in this study (Deperlioglu, 2018; Guraksin et. al., 2009) . The heart sound signal covering the first two steps is given in Figure 2 for sample sound file of 103_1305031931979_B in the PASCAL Btraining data set.</p>
Data for "Sounding out Ecoacoustic Metrics: Avian species richness is predicted by acoustic indices in temperate but not tropical habitats"
<p>This deposit contains the data for the paper <strong>A Multi-habitat, Comparative Evaluation of Ecoacoustic Indices for Biodiversity Monitoring: Acoustic Indices Predict Avian Species Richness in Temperate but not Tropical Habitats. (Ecological Indicators) </strong>The dataset contains a series of 1 min wav files recorded across UK and Ecuadorian habitats. Each one has 26 acoustic indices calculated on it, and a full list of avian species and abundances and GPS data for each sample site.</p> <p>Abstract</p> <p>Affordable, autonomous recording devices facilitate large scale acoustic monitoring and Rapid Acoustic Survey is emerging as a cost-effective approach to ecological monitoring; the success of the approach rests on the development of computational methods by which biodiversity metrics can be automatically derived from remotely collected audio data. Dozens of indices have been proposed to date, but systematic validation against classical, in situ diversity measures. This study conducted the most comprehensive comparative evaluation to date of the relationship between avian species diversity and a suite of acoustic indices across a wide range of ecological conditions. Acoustic surveys were carried out across habitat gradients in temperate and tropical biomes. Baseline avian species richness and subjective multi-taxa biophonic density estimates were established through aural counting by expert ornithologists. 26 acoustic indices were calculated and compared to observed variations in species diversity. Five acoustic diversity indices (Bioacoustic Index, Acoustic Diversity Index, Acoustic Evenness Index, Acoustic Entropy, and the Normalised Difference Sound Index) were assessed as well as three simple acoustic descriptors (root-mean-square, spectral centroid and zero-crossing rate). Highly significant correlations, of up to 65%, between acoustic indices and avian species richness were observed across temperate habitats, supporting the use of automated acoustic indices in biodiversity monitoring where a single vocal taxon dominates. Significant, weaker correlations were observed in neotropical habitats which host multiple non-avian vocalizing species. Multivariate classification analyses suggest that AIs also track observed differences in habitat-dependent community composition and that each habitat has a distinct soundscape. Multivariate analyses of the relative predictive power of AIs show that compound indices are more powerful predictors of avian species richness than any single index and simple descriptors contribute to predicting avian diversity in multi-taxa tropical environments. Our results support the use of community level acoustic indices as a proxy for species richness and point to the potential for tracking of habitat-dependent changes in community composition. Recommendations for the design of compound indices for multi-taxa community composition appraisal are put forward, with consideration for the requirements of next generation, low power remote monitoring networks.</p> <p> </p> <p><strong>Sampling Methods (extract from paper)</strong></p> <p>Acoustic surveys were carried out along a gradient of habitat degradation (1 forested, 2 regenerating forest and 3 agricultural land) in South East (SE) England and North Western (NW) Ecuador. The six sites (UK1, UK2, UK3, EC1, EC2, EC3) were sampled consecutively from May 6th - Aug 25th 2015.</p> <p>All UK sites were in the county of Sussex, in SE England, an area of weald clays (Fig. 2, left) and included ancient woodland (UK1), regenerating farmland with patches of woodland (UK2) and a downland barley farm (UK3).1 min mono audio recordings made every 15 minutes at three different habitats in the UK</p> <p>Ten day acoustic surveys were carried out consecutively at each study site using 15 Wildlife Acoustics Song Meter audio field recorders. Sampling points were arranged in a grid at a minimum distance of 200 m to minimise pseudo replication (the sound of most species being attenuated over this distance in all biomes). Altitudinal range of sample points across sites was minimised in order to prevent introduction of extraneous, confounding gradients (UK varied between 10 m – 50 m and Ecuador 130 m – 390 m). Recording schedules captured 1 min every 15 min around the clock for 10 days at each site, resulting in 960 recordings at each of 15 sample points for 3 habitat types in 2 different climates (86,400 1 minute recordings in total). Data across the 15 sample points was pooled; inter-site variation was not explored in the current analyses. In the UK 3½ hours of each dawn chorus was sampled starting at 1 hour before sunrise. This range was determined to capture the onset, progression and peak of the dawn chorus, creating a temporal gradient. The equatorial dawn chorus is more compact and was sampled for 2¼ hours starting 15 mins before sunrise, capturing a comparable chorus onset and peak.</p> <p> </p> <p> </p>
Data: Effects of broadband sound exposure on the interaction between foraging crab and shrimp – a field study
<p>Data abstract:</p> <p>Data on foraging crabs and shrimps during trials with or without broadband sound exposures. Trials were conducted in situ using baited cameras.</p> <p> </p> <p>Paper abstract:</p> <p>Aquatic animals live in an acoustic world in which they often rely on sound detection and recognition for various aspects of life that may affect survival and reproduction. Human exploitation of marine resources leads to increasing amounts of anthropogenic sound underwater, which may affect marine life negatively. Marine mammals and fishes are known to use sounds and to be affected by anthropogenic noise, but relatively little is known about invertebrates such as decapod crustaceans. We conducted experimental trials in the natural conditions of a quiet cove. We attracted shore crabs (<em>Carcinus maenas</em>) and common shrimps (<em>Crangon crangon</em>) with an experimentally fixed food item and compared trials in which we started playback of a broadband artificial sound to trials without exposure. During trials with sound exposure, the cumulative count of crabs that aggregated at the food item was lower, while variation in cumulative shrimp count could be explained by a negative correlation with crabs. These results suggest that crabs may be negatively affected by artificially elevated noise levels, but that shrimps may indirectly benefit by competitive release. Eating activity for the animals present was not affected by the sound treatment in either species. Our results show that moderate changes in acoustic conditions due to human activities can affect foraging interactions at the base of the marine food chain.</p> <p> </p> <p>Reference:</p> <p>Hubert, J., Campbell, J., van der Beek, J.G., den Haan, M.F., Verhave, R., Verkade L.S., and Slabbekoorn H. (2018) Effects of broadband sound exposure on the interaction between foraging crab and shrimp - a field study. Environ. Pollut. 243, 1923–1929. DOI:10.1016/j.envpol.2018.09.076</p>
Joint sound scene and event dataset
<p>A dataset of synthentic sound scenes created in scaper [1]using real-world recordings. Synthesized scenes are split into train and test such that no original recordings are in both splits. </p> <p>Dataset created for the task of performing both acoustic scene classification jointly with sound event detection. Time stamped annotations and jams files included. </p> <p>10 acoustic scene classes, 32 sound event classes.</p> <p>filename syntax is [sceneLabel_recordingID_polyphonylevel]</p> <p>Full credits / citation details are below, please contact Yogi at h.bear@qmul.ac.uk with any queries. </p> <p>Citation:</p> <p>Helen L Bear, Ines Nolasco, and Emmanouil Benetos, <em>Towards joint sound scene and polyphonic sound event detection.</em> Interspeech 2019. Graz. </p> <p>References</p> <ol> <li>Salamon, Justin, et al. "Scaper: A library for soundscape synthesis and augmentation." <em>Applications of Signal Processing to Audio and Acoustics (WASPAA), 2017 IEEE Workshop on</em>. IEEE, 2017.</li> </ol>
Monaural and binaural sound localization cues in crocodilians
<p>This dataset is composed by all the recorded microphonic signals necessary for the computation of external sound localization cues: HRTFs (Head-Related Transfer Functions), Interaural Level Differences (ILD) and Interaural Time Differences (ITD) on awake crocodilians (<em>Crocodylus niloticus</em> and <em>Caiman latirostris</em>) and skulls(<em>Crocodylus niloticus</em>).</p> <p>The Matlab scripts necessary to compute and display HRTF, ILD and ITD are included as well as instructions in txt and pdf files.</p>
NIGENS general sound events database
<p>NIGENS (<strong>N</strong>eural <em><strong>I</strong></em>nformation Processing group <em><strong>GEN</strong></em>eral sounds) is a database provided for sound-related modeling in the field of computational auditory scene analysis, particularly for sound event detection, that has emerged from the <a href="http://twoears.eu">Two!Ears project</a>.</p> <p>It contains 1017 wav files of various lengths (between 1s and 5mins), in total comprising 4h:46m of sound material. Mostly, sounds are provided with 32-bit precision and 44100 Hz sampling rate. The files contain sound events in isolation, i.e. without superposition of ambient or other foreground sources. </p> <p>Fourteen distinct sound classes are included: <em>alarm</em>, <em>crying baby</em>, <em>crash</em>, <em>barking dog</em>, <em>running engine</em>, <em>burning fire</em>, <em>footsteps</em>, <em>knocking on door</em>, <em>female</em> and <em>male speech</em>, <em>female</em> and <em>male scream</em>, <em>ringing phone</em>, <em>piano</em>. Additionally, there is the <em>general</em> (“anything else”) class. Care has been taken to select sound classes representing different features, like noise-like or pronounced, discrete or continuous.</p> <p>The <em>general</em> class is a pool of sound events different than the 14 distuingished target sound classes, containing as heterogeneous sounds as possible (303 in total). For example, it includes nature sounds such as wind, rain, or animals, sounds from human-made environments such as honks, doors, or guns, as well as human sounds like coughs. These sounds are intended both as ``disturbance'' sound events (superposing) and as counterexamples to target sound classes.<br> <br> Wav files are accompanied by annotation (.txt) files that include perceptual on- and offset times of the file's sound events. </p> <p>You are free to use this database non-commercially under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 license.</p> <p><strong>If you use this data set, please cite as:</strong></p> <p><strong>Ivo Trowitzsch, Jalil Taghia, Youssef Kashef, and Klaus Obermayer (2019). <em>The NIGENS general sound events database</em>. Technische Universität Berlin, Tech. Rep. arXiv:1902.08314 [cs.SD] </strong></p> <p>In [1], we have developed and analyzed a robust binaural sound event detection training scheme using NIGENS. In [2], we have extended it to join sound event detection and localization through spatial segregation.</p> <p>[1] Trowitzsch, I., Mohr, J., Kashef, Y., Obermayer, K. (2017). <em>Robust detection of environmental sounds in binaural auditory scenes</em>. IEEE/ACM Transactions on Audio, Speech, and Language Processing 25(6).</p> <p>[2] Trowitzsch, I., Schymura, C., Kolossa, D., Obermayer, K. (2019). <em>Joining Sound Event Detection and Localization Through Spatial Segregation</em>. accepted for publication in IEEE/ACM Transactions on Audio, Speech, and Language Processing. DOI: 10.1109/TASLP.2019.2958408. E-Preprint: <a href="https://arxiv.org/abs/1904.00055">arXiv:1904.00055</a> [cs.SD].</p>
Short-term effects of sound localization training in virtual reality
<p>This repository contains the dataset and software used in the submission to Scientific Reports entitled "Short-term effects of sound localization training in virtual reality".</p> <p>A README.txt file is included, which describes the contents of the repository.</p> <p>Please note that the authors were not granted permission to redistribute the LIMSI Spatialization Engine, which was used to spatialize the sounds described in the manuscript. Please contact the author(s) for details.</p>
Sounds, synthesized using Impulse Pattern Formulation
<p>The Sound of the dizi with and without mirliton membrane was recorded and synthesized using Impulse Pattern Formulation (IPF). Further, the sound of a multiphonic played on the clarinet was recorded and synthesized using IPF. Finally, the sound of a bowed string was recorded while slowly increasing the bow force. This sound is synthesized by the IPF as well.</p> <p>A more in-depth description of how these sounds were synthesized can be found in the related publication:</p> <p>Linke, S., Bader, R., & Mores, R. (2019). The Impulse Pattern Formulation (IPF) as a nonlinear model of musical instruments. In M. Kob (Ed.), Proceedings of the International Symposium on Music Acoustics 2019 - ISMA 2019 (pp. 336–345). <a href="http://pub.dega-akustik.de/ISMA2019/data/ISMA_proceedings_all.pdf">http://pub.dega-akustik.de/ISMA2019/data/ISMA_proceedings_all.pdf</a></p>
Sound Events for Surveillance Applications
<p>The Sound Events for Surveillance Applications (SESA) dataset files were obtained from Freesound. The dataset was divided between train (480 files) and test (105 files) folders. All audio files are WAV, Mono-Channel, 16 kHz, and 8-bit with up to 33 seconds. # Classes: 0 - Casual (not a threat) 1 - Gunshot 2 - Explosion 3 - Siren (also contains alarms)</p>
example stimuli of "Behavioral effects of rhythm, carrier frequency and temporal cueing on the perception of sound sequences"
<p>Exemplary subset of stimuli accompanying the manuscript "Behavioral effects of rhythm, carrier frequency and temporal cueing on the perception of sound sequences"</p>
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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)
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DANDI Archive for NWB datasets
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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.