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19 results for “bird vocalization”
The Effect of Soundscape Composition on Bird Vocalization Classification in a Citizen Science Biodiversity Monitoring Project
<p>This archive includes sound clips (.wav files) and associated mel-scale spectrograms of bird vocalizations for 54 species in Sonoma County, California, USA. These data were used for training and validating convolutional neural network (CNN) models for bird species detection. We also include xeno-canto training and validation mel spectrograms used to pretrain CNNs. Details on these data are explained in the paper by Clark et al. (2023) titled "The effect of soundscape composition on bird vocalization classification in a citizen science biodiversity monitoring project". These data are available for use without restrictions, with no warranty on data quality or utility for a given application. We request that any work that does use these data cite the Clark et al. (2023) paper.<br> <br> Clark, M.L., Salas, L., Baligar, S., Quinn, C., Snyder, R.L., Leland, D., Schackwitz, W., Goetz, S.J., Newsam, S. (2023). The effect of soundscape composition on bird vocalization classification in a citizen science biodiversity monitoring project. <em>Ecological Informatics</em>. <a href="https://doi.org/10.1016/j.ecoinf.2023.102065">https://doi.org/10.1016/j.ecoinf.2023.102065</a></p> <p>Associated code for training CNN models, performing inference, and applying post-classification corrections can be found in the GitHub archive <a href="https://github.com/pointblue/Soundscapes2Landscapes/tree/master/CNN_Bird_Species">https://github.com/pointblue/Soundscapes2Landscapes/tree/master/CNN_Bird_Species</a></p> <p>Raw sound data from the Soundscapes to Landscapes project are available upon request: Dr. Matthew Clark, matthew.clark@sonoma.edu</p> <p>These data were collected as part of the Soundscapes to Landscapes project (<a href="https://soundscapes2landscapes.org/">soundscapes2landscapes.org</a>), funded by NASA’s Citizen Science for Earth Systems Program (CSESP) 16-CSESP 2016-0009 under cooperative agreement 80NSSC18M0107.<br> <br> ----------------------------<br> This depository includes the following archives:</p> <ul> <li> <p>mel_specs.zip: contains 2-sec mel spectrograms split into training (“tr”), validation (“val”), testing (“test”) data for each target bird species (n = 54) used to fine-tune the CNNs. Select spectrogram files are appended with “aug” if they are augmented versions for the training data.</p> </li> <li> <p>wav.zip: contains the associated wav-format sound recordings used to generate the training, validation, testing mel spectrograms found in mel_specs.zip.</p> </li> <li> <p>Xeno-canto_pretrain.tar: contains 2-sec mel spectrograms split into training and validation data for 40 bird species used for CNN pre-training that were generated using a warbleR segmentation methodology described in the paper. The sound files used to generate these mel spectrograms came from the Kaggle competition, <a href="https://www.kaggle.com/datasets/imoore/xenocanto-bird-recordings-dataset">https://www.kaggle.com/datasets/imoore/xenocanto-bird-recordings-dataset</a><br> Mel spectrogram naming reflects the XC number used for cataloging on Xeno-canto in the format XC123456_2.png. The six numbers following the XC characters can be used to search for unique recordings on Xeno-canto (<a href="https://xeno-canto.org/">https://xeno-canto.org/</a>) using the search query “nr:123456” in the search tool or queried using the Xeno-canto API (<a href="https://xeno-canto.org/explore/api">https://xeno-canto.org/explore/api</a>). Unique recording names can be extracted from the mel spectrogram filenames.</p> </li> <li> <p>soundscape_test_wavs.zip: the wav-format sound recordings used to perform soundscape testing.</p> </li> </ul>
Non-crop vegetation characteristics and vocalizing bird richness across 44 sites in Iowa, USA in June 2019
<p>This data was derived from field work conducted in June 2019 where sixty AudioMoth passive acoustic monitors were placed along agricultural field margins in Iowa, USA. Twenty-five of the monitoring location were established by farmer and landowner collaborators, and the remaining (35) sites were established by the author (A.P.D.). Unique vocalizing bird species were counted in ninety-five recordings from 6 to 8 days during dawn hours per site. High resolution mapping identified non-crop vegetation and texture at spatial extents ranging from 100 to 1000 meters. Pesticide and fertilizer application were collected via a survey with collaborators. Site location names are included when the research site was an Iowa State Research and Demonstration Farm (ISRF). When the site was a collaborator, the site name was anonymized to "Collaborator" to respect the privacy of participants.</p>
Data and code from: Diverse relationships between amplitude and frequency in bird vocalizations
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Raw amplitude measurements for arctic bird vocalizations from Utqiagvik, Alaska, with associated environmental data and modelling code
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Data from: Noise pollution filters bird communities based on vocal frequency
BACKGROUND: Human-generated noise pollution now permeates natural habitats worldwide, presenting evolutionarily novel acoustic conditions unprecedented to most landscapes. These acoustics are not only harmful to humans, but threaten wildlife, and especially birds, via changes to species densities, foraging behavior, reproductive success, and predator-prey interactions. Explanations for the negative effects of noise on birds include the disruption of acoustic communication through energetic masking, potentially forcing species that rely upon acoustic communication to abandon otherwise suitable areas. However, this hypothesis has not been adequately tested because confounding stimuli often co-vary with noise and are difficult to separate from noise exposure. METHODOLOGY/PRINCIPAL FINDINGS: Using a natural experiment that controls for confounding stimuli, we evaluate whether species vocal features or urban-tolerance classification explain their responses to noise measured through habitat use. Two data sets representing nesting and abundance responses reveal that noise filters bird communities nonrandomly. Signal duration and urban tolerance failed to explain species-specific responses, but birds with low-frequency signals that are more susceptible to masking from noise avoided noisy areas and birds with higher pitched vocalizations remained. Signal frequency was also negatively correlated with body mass, suggesting that larger birds may be more sensitive to noise due to the link between body size and vocal frequency. CONCLUSIONS/SIGNIFICANCE: Our findings suggest that acoustic masking by noise may be a strong selective force shaping the ecology of birds worldwide. Larger birds with lower frequency signals may be excluded from noisy habitat, whereas smaller species persist via transmission of higher pitched signals. We discuss our findings as they relate to interspecific relationships among body size, vocal amplitude and frequency and suggest that they are immediately relevant to the global problem of increases in noise by providing critical insight as to which species traits influence tolerance of these novel acoustics.
Data from: Spectrogram cross-correlation can be used to measure the complexity of bird vocalizations
<p>This data set contains catalog numbers of Macaulay Library sound files and RVV library sound files used in the study- Sawant, S; Arvind, C; Joshi, V & Robin, V. V. (2021) Spectrogram cross-correlation can be used to measure the complexity of bird vocalizations.</p>
Data from: Noise pollution filters bird communities based on vocal frequency
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Data from: Song evolution, speciation, and vocal learning in passerine birds
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Data from: Snowmobile noise alters bird vocalization patterns during winter and pre-breeding season
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Data from: Are vocal characteristics related to leadership patterns in mixed-species bird flocks?
What structures the organization of mixed-species bird flocks, so that some 'nuclear' species lead the flocks, and others follow? Previous research has shown that species actively listen to each other, and that leaders are gregarious; such gregarious species tend to make contact calls and hence may be vocally conspicuous. Here we investigated whether vocal characteristics are associated with leadership, using a global dataset of mixed-species flock studies and recordings from sound archives. We first asked whether leaders are different from following or occasional species in flocks in the proportion of the recordings that contain calls (n=58 flock studies, 145 species), and especially alarm calls (n=111 species). We found that leaders tended to have a higher proportion of their vocalizations that were calls than occasional species, and both leaders and following species had a significantly higher proportion of their calls rated as alarms compared to occasional species. Next, we investigated the acoustic characteristics of flock participants' calls, hypothesizing that leaders would make more calls, and have less silence on the recordings. We also hypothesized that leaders' calls would be simple acoustically, as contact calls tend to be, and thus similar to each other, as well as being detectable, in being low frequency and high bandwidth. The analysis (n=45 species, 169 recordings) found that only one of these predictions was supported: leading species were less often silent than following or occasional species. Unexpectedly, leaders' calls were less similar to each other than occasional species. The greater amount of information available and the greater variety of that information support the hypothesis that leadership in flocks is related to vocal communication. We highlight the use of sound archives to ask questions about behavioral and community ecology, while acknowledging some limitations of such studies.
Data from: Vocal plasticity in mallards: multiple signal changes in noise and the evolution of the Lombard effect in birds
Signal plasticity is a building block of complex animal communication systems. A particular form of signal plasticity is the Lombard effect, in which a signaler increases its vocal amplitude in response to an increase in the background noise. The Lombard effect is a basic mechanism for communication in noise that is well-studied in human speech and which has also been reported in other mammals and several bird species. Sometimes, but not always, the Lombard effect is accompanied by additional changes in signal parameters. However, the evolution of the Lombard effect and other related vocal adjustments in birds are still unclear because so far only three major avian clades have been studied. We report the first evidence for the Lombard effect in an anseriform bird, the mallard (Anas platyrhynchos). In association with the Lombard effect, the fifteen ducklings in our experiment also increased the peak frequency of their calls in noise. However, they did not change the duration of call syllables or their call rates as has been found in other bird species. Our findings support the notion that all extant birds use the Lombard effect to solve the common problem of maintaining communication in noise, i.e. it is an ancestral trait shared among all living avian taxa, which means that it has evolved more than 70 million years ago within that group. At the same time, our data suggest that parameter changes associated with the Lombard effect follow more complex patterns, with marked differences between taxa, some of which might be related to proximate constraints..
Data from: Booming far: the long-range vocal strategy of a lekking bird
The pressures of selection acting on transmission of information by acoustic signals are particularly high in long-distance communication networks. Males of the North African houbara bustard (Chlamydotis undulata undulata) produce extremely low-frequency vocalizations called 'booms' as a component of their courtship displays. These displays are performed on sites separated by a distance of on average 550 m, constituting exploded leks. Here, we investigate the acoustic features of booms involved in species-specific identity. We first assessed the modifications of acoustic parameters during boom transmission at long range within the natural habitat of the species, finding that the frequency content of booms was reliably transmitted up to 600 m. Additionally, by testing males' behavioural responses to playbacks of modified signals, we found that the presence of the second harmonic and the frequency modulation are the key parameters for species identification, and also that a sequence of booms elicited stronger responses than a single boom. Thus, the coding-decoding process relies on redundant and propagation-resistant features, making the booms particularly well adapted for the long-range transmission of information between males. Moreover, by experimentally disentangling the presentation of visual and acoustic signals, we showed that during the booming phase of courtship, the two sensory modalities act in synergy. The acoustic component is dominant in the context of intra-sexual competition. While the visual component is not necessary to induce agonistic response, it acts as an amplifier and reduces the time of detection of the signaller. The utilization of these adaptive strategies allows houbara males to maximize the active space of vocalizations emitted in exploded leks.
Evolution of vocal performance and song complexity in island birds
<p>Oceanic islands share distinctive characteristics thought to underlie a set of parallel evolutionary trends across islands and taxonomic groups – including life history traits, morphology and visual signals. To which extent acoustic signals also change in parallel on islands is less clear. Some important processes associated with insularity, such as founder effects and reduced sexual selection, could lead to a decrease in vocal performance and song complexity on islands. In a field-based study, we recorded 11 insular species and their closest mainland relatives. Out of the 11 species pairs, 6 live in the tropics (São Tomé / Mount Cameroon), and 5 in the temperate region (Madeira / southern France). For each species we measured two proxies of vocal performance (song duration and syllable rate) and one proxy of song complexity (syllable diversity). This study did not recover a clear relationship between the island environment and song traits. If as expected, syllable rate was lower in island species than in their mainland counterparts, the two other proxies showed no clear island-mainland pattern of divergence. Several factors may explain the absence of reduction for song duration and syllable diversity. Among those, relaxation of interspecific competition on islands may have led to an increase in syllable diversity, or correlations between song variables may have constrained song evolution. More studies on island species are needed to draw a better picture of divergence patterns and go beyond the confounding ecological factors that could explain peculiar song characteristics in islands.</p>
Evolution of vocal performance and song complexity in island birds
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Data from: Are vocal characteristics related to leadership patterns in mixed-species bird flocks?
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Data from: Crying wolf to a predator: deceptive vocal mimicry by a bird protecting young
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Data from: Vocal plasticity in mallards: multiple signal changes in noise and the evolution of the Lombard effect in birds
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Data from: Male vocalizations convey information on kinship and inbreeding in a Lekking bird
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Data from: Booming far: the long-range vocal strategy of a lekking bird
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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)
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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.