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10 results for “environmental sounds”
Familiar Environmental Sound Test -- Stimuli
<p>This zip file includes environmental sound stimuli that comprise Familiar Environmental Sound Test. There are 25 individual sounds for FEST-I. These sounds are composed into FEST-S which consists of two sets of 5 semantically coherent and 5 semantically incoherent sequences, five sounds in each sequence. These sequences are used to detect semantic context effects in environmental sound perception. Two practice sequences are also included.</p>
DCASE 2024 Challenge Task 7 Development Dataset : Environmental Sound Scene Synthesis
<h1><strong>Description</strong></h1> <p>This dataset comprises embeddings and captions utilized as the development dataset for <a href="https://dcase.community/challenge2024/task-sound-scene-synthesis"><strong>DCASE 2024 Challenge Task 7</strong></a>, focusing on 'Environmental Sound Scene Synthesis.' The embeddings are derived from 60 different 4-second audio files formatted as mono 32-bit 32kHz, and are contained in the 'embeddings.tar.xz' file. Captions corresponding to each audio file can be found in 'caption.csv'. This dataset does not comprise the audio files, only the embeddings. Three different types of embeddings are provided: VGGish (vggish), MS-CLAP (clap-2023), and PANNs CNN14 Wavegram-Logmel (panns-wavegram-logmel). Only PANNs CNN14 Wavegram-Logmel (panns-wavegram-logmel) embeddings are used for evaluation in the challenge. For further details, please refer to the challenge website.</p> <p><strong>Contact</strong></p> <ul> <li>Modan Tailleur, modan.tailleur@ls2n.fr</li> <li>Mathieu Lagrange, mathieu.lagrange@ls2n.fr</li> </ul>
Data from: Vocal signatures affected by population identity and environmental sound levels
<p>Passive acoustic monitoring has improved our understanding of vocalizing organisms in remote habitats and during all weather conditions. Many vocally active species are highly mobile, and their populations overlap. However, distinct vocalizations allow the tracking and discrimination of individuals or populations. Using signature whistles, the individually distinct calls of bottlenose dolphins, we calculated a minimum abundance of individuals, characterized and compared signature whistles from five locations, and determined reoccurrences of individuals throughout the Mid-Atlantic Bight and Chesapeake Bay, USA. We identified 1,888 signature whistles in which the duration, number of extrema, start, end, and minimum frequencies of signature whistles varied significantly by site. All characteristics of signature whistles were deemed important for determining from which site the whistle originated and due to the distinct signature whistle characteristics and lack of spatial mixing of the dolphins detected at the Offshore site, we suspect that these dolphins are of a different population than those at the Coastal and Bay sites. Signature whistles were also found to be shorter when sound levels were higher. Using only the passively recorded vocalizations of this marine top predator, we obtained information about its population and how it is affected by ambient sound levels, which will increase as offshore wind energy is developed. In this rapidly developing area, these calls offer critical management insights for this protected species.</p>
Data from: Stressful city sounds: glucocorticoid responses to experimental traffic noise are environmentally dependent
A major challenge in urban ecology is to identify the environmental factors responsible for phenotypic differences between urban and rural individuals. However, the intercorrelation between the factors that characterise urban environments, combined with a lack of experimental manipulations of these factors in both urban and rural areas, hinder efforts to identify which aspects of urban environments are responsible for phenotypic differences. Among the factors modified by urbanisation, anthropogenic sound, particularly traffic noise, is especially detrimental to animals. The mechanisms by which anthropogenic sound affects animals are unclear, but one potential mechanism is through changes in glucocorticoid hormone levels. We exposed adult house wrens, Troglodytes aedon, to either traffic noise or pink noise. We found that urban wrens had higher initial (pre-restraint) corticosterone than rural wrens before treatment, and that traffic noise elevated initial corticosterone of rural, but not urban, wrens. By contrast, restraint stress-induced corticosterone was not affected by noise treatment. Our results indicate that traffic noise specifically contributes to determining the glucocorticoid phenotype, and suggest that glucocorticoids are a mechanism by which anthropogenic sound causes phenotypic differences between urban and rural animals.
Contributions of environmental conditions and sound characteristics to differences in perceptibility: Recommendations for passive acoustic monitoring
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Data from: Vocal signatures affected by population identity and environmental sound levels
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Data from: Stressful city sounds: glucocorticoid responses to experimental traffic noise are environmentally dependent
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Data from: Weather conditions determine attenuation and speed of sound: environmental limitations for monitoring and analysing bat echolocation
Echolocating bats are regularly studied to investigate auditory-guided behaviours and as important bioindicators. Bioacoustic monitoring methods based on echolocation calls are increasingly used for risk assessment and to ultimately inform conservation strategies for bats. As echolocation calls transmit through the air at the speed of sound, they undergo changes due to atmospheric and geometric attenuation. Both the speed of sound and atmospheric attenuation, however, are variable and determined by weather conditions, particularly temperature and relative humidity. Changing weather conditions thus cause variation in analysed call parameters, limiting our ability to detect and correctly analyse bat calls. Here, I use real-world weather data to exemplify the effect of varying weather conditions on the acoustic properties of air. I then present atmospheric attenuation and speed of sound for the global range of weather conditions and bat call frequencies to show their relative effects. Atmospheric attenuation is a non-linear function of call frequency, temperature, relative humidity and atmospheric pressure. While atmospheric attenuation is strongly positively correlated with call frequency, it is also significantly influenced by temperature and relative humidity in a complex non-linear fashion. Variable weather conditions thus result in variable and unknown effects on the recorded call, affecting estimates of call frequency and intensity, particularly for high frequencies. Weather-induced variation in speed of sound reaches up to about ±3%, but is generally much smaller and only relevant for acoustic localisation methods of bats. The frequency- and weather-dependent variation in atmospheric attenuation has a three-fold effect on bioacoustic monitoring of bats: it limits our capability (1) to monitor bats equally across time, space, and species, (2) to correctly measure frequency parameters of bat echolocation calls, particularly for high-frequencies, and (3) to correctly identify bat species in species-rich assemblies or for sympatric species with similar call designs.
Data from: Weather conditions determine attenuation and speed of sound: environmental limitations for monitoring and analysing bat echolocation
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ESC-50-Voice: Dataset of vocal imitation for environmental sound in ESC-50
<h2><strong>Description</strong></h2> <p>This is a dataset with vocal imitation, which involve the process of replicating or mimicking the rhythm and pitch of sounds by voice for an environmental sound in ESC-50 [1] that can be used in various tasks that use environmental sounds. The dataset consists of 9,920 vocal imitations (8 imitators per environmental sound). Each imitator is a Japanese speaker. All audio data are 48kHz/16bit wav files. </p> <p>Each audio file is named as follows:</p> <pre><code>vocal_imitation/SpeakerID/FileName_SpeakerID.wav</code></pre> <p>FileName means the original audio file name in ESC-50. SpaekerID means the ID of the imitator. We recorded vocal imitations for a part of sound events in ESC-50. A list of the sound events used can be obtained from EventList.csv.</p> <p>Note that this dataset does not contain environmental sound files, which can be obtained from ESC-50. Environmental sounds in ESC-50 are available <a href="https://github.com/karolpiczak/ESC-50">here</a>.</p> <h2><strong>Terms of use</strong></h2> <p>The materials may be used free of charge for research purposes, but please refrain from redistribution or use that is offensive to public order and morals. If you want to use for commercial purposes, please contact us (Yuki Okamoto or Keisuke Imoto).</p> <h2><strong>Citation</strong></h2> <p>If you use this dataset, please cite as follow:</p> <p>Yuki Okamoto, Keisuke Imoto, Shinnosuke Takamichi, Ryotaro Nagase, Takahiro Fukumori, and Yoichi Yamashita, "Environmental Sound Synthesis from Vocal Imitations and Sound Event Labels," Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 411-415, 2024.</p> <h2><strong>Feedback</strong></h2> <p>If there is any problem, please contact us</p> <ul> <li>Yuki Okamoto, <a href="mailto:y-okamoto@ieee.org">y-okamoto@ieee.org</a></li> <li>Keisuke Imoto, <a href="mailto:keisuke.imoto@ieee.org">keisuke.imoto@ieee.org</a></li> </ul> <p> </p> <p>[1] K. J. Piczak, "Esc: Dataset for environmental sound classification,” in Proc. the 23rd ACM International Conference on Multimedia, 2015, p. 1015–1018.</p>
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