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1,719 results for “songs”
T-REC Song Recognition Dataset
<p>This csv file (tab delimited) contains the track names, artist(s), total number of responses, measured recognition (user study), computed recognition (T-REC) and measured recognition on specific demographics i.e. male, female and age groups 18-24, 25-34, 35-44, 45-54, 55-65 for 100 music tracks used in the paper "Data-driven song recognition estimation using collective memory dynamics models" accepted for publication in the ISMIR 2019 conference.</p>
Data and models for automatic scansion experiment Dutch Song Database
<p>This release contains the <strong>data </strong>used in an <a href="https://github.com/WHaverals/scanner">experiment</a> on automatic scansion for historical Dutch song texts. Aside form the data, two <strong>models </strong>are included in this release as well. One model is essential for running the code that is part of this experiment (model_s); while the other model is an example of an acquired automatic scansion model (best_model).</p> <p><strong>Item descriptions</strong>:</p> <ul> <li><em>meertens-meter-songs.zip</em> → collection of 23,197 historic Dutch songs (xml-format). These files (and the gathered meta-data) stems from a collaboration project between the <em><a href="http://www.liederenbank.nl/index.php?lan=en">Dutch Song Database</a> </em>and the <em><a href="https://dbnl.org">Digital Library for Dutch Literature</a></em>. All files contain meta-data on the number of beats that is present in individual verse lines. Snippet:</li> </ul> <blockquote> <pre><code class="language-xml"><lg> <l id="s1:l1" met="4" type="-+"> Een Meysken op een Rivierken <rhyme label="a" type="m">sadt</rhyme>,</l> <l id="s1:l2" met="2" type="-+"> So schoon zy <rhyme label="b" type="m">was</rhyme>,</l> <l id="s1:l3" met="4" type="-+"> Sy sadt en verbeyde haer soete <rhyme label="c" type="m">Lief</rhyme>,</l> <l id="s1:l4" met="2" type="-+"> Int groene <rhyme label="b" type="m" corresp="#s1:l2">gras</rhyme>.</l> </lg></code></pre> <p> </p> </blockquote> <p> </p> <ul> <li><em>model_s</em> → model used for syllabification and assignment of lexical stress of (historic) Dutch words. The development of this model was part of a <a href="https://github.com/WHaverals/stresser">previous project</a>.</li> </ul> <p> </p> <ul> <li><em>stress_xml.zip</em> → collection of 23,197 historic Dutch songs (xml-format). These are the same songs a the <em>meertens-meter-songs</em>, yet now their individual words are syllabified and annotated for lexical stress. The songs in this folder are used as input during the training process. Snippet:</li> </ul> <blockquote> <pre><code class="language-xml"><l id="s1:l1" met="4" type="-+"> <w token="een"> <s word-stress="1" line-stress="0">een</s> </w> <w token="meysken"> <s word-stress="1" line-stress="0">meys</s> <s word-stress="0" line-stress="0">ken</s> </w> <w token="op"> <s word-stress="1" line-stress="0">op</s> </w> <w token="een"> <s word-stress="1" line-stress="0">een</s> </w> <w token="rivierken"> <s word-stress="0" line-stress="0">ri</s> <s word-stress="1" line-stress="0">vier</s> <s word-stress="0" line-stress="0">ken</s> </w> <rhyme label="a" type="m"> <w token="sadt"> <s word-stress="1" line-stress="0">sadt</s> </w> </rhyme> </l></code></pre> <p> </p> </blockquote> <ul> <li><em>gold_scan.zip</em> → 198 Dutch song files (xml-format). These files have been annotated by an expert for line stress.</li> </ul> <p> </p> <ul> <li><em>eval_splits.zip </em>→ contains the splits made from <em>gold_scan. </em>These are the splits used in the automatic scansion experiment: a development set of 98 songs (used during training), and a test set of 99 songs (used for evaluating the best model after training).</li> </ul> <p> </p> <ul> <li><em>best_model.zip</em> → contains the files of an acquired model for automatic Dutch song scansion.</li> </ul>
Figure 18 in First results of a faunistic survey on the Orthoptera of Jadovnik Mountain, southwestern Serbia, with data on the calling songs of some bush cricket species
Figure 18. Stenobothrus rubicundulus (male).
Figure 14 in First results of a faunistic survey on the Orthoptera of Jadovnik Mountain, southwestern Serbia, with data on the calling songs of some bush cricket species
Figure 14. SEM image of the stridulatory file in Psorodonotus macedonicus, Sopotnica Waterfalls.
Figure 12 in First results of a faunistic survey on the Orthoptera of Jadovnik Mountain, southwestern Serbia, with data on the calling songs of some bush cricket species
Figure 12. Metrioptera brachyptera (male).
Figure 17 in First results of a faunistic survey on the Orthoptera of Jadovnik Mountain, southwestern Serbia, with data on the calling songs of some bush cricket species
Figure 17. Galvagniella albanica (male).
Figure 9 in First results of a faunistic survey on the Orthoptera of Jadovnik Mountain, southwestern Serbia, with data on the calling songs of some bush cricket species
Figure 9. Poecilimon pseudornatus (male).
Figure 6 in First results of a faunistic survey on the Orthoptera of Jadovnik Mountain, southwestern Serbia, with data on the calling songs of some bush cricket species
Figure 6. Locality: Ogoreljača–Mali Jadovnik (meadow).
Figure 15 in First results of a faunistic survey on the Orthoptera of Jadovnik Mountain, southwestern Serbia, with data on the calling songs of some bush cricket species
Figure 15. Tetrix bipunctata (female).
Figure 1 in The song structure and repertoire size of Daurian Redstarts (Phoenicurus auroreus) in South Korea
Figure 1. The map of recording sites of Daurian Redstart songs in South Korea.
Figure 1 in The first finding of sessile ciliates Vorticella pyriforme Stiller, 1939 and Zoothamnium sinense Song, 1991 (Ciliophora, Peritrichia) in the Black Sea
Figure 1. Map of sampling area (September 2020).
50 Years of Art Song & Gender on Major European Stages, Part 1 (Austria): Vienna's Konzerthaus, Musikverein & Salzburg Festspiele Song Recitals 1970 - 2020
<p>3 .csv files containing names, years and gender breakdown (m=male, f=female, b=both) of pianists, singers, and composers featured vocal recitals on the three most prestigious stages in Austria, the Musikverein an Konzerthaus in Vienna and at the Salzburger Festpiele between 1970 and 2020.</p> <p>Data was scraped by the author from the Festspiele digital archive (https://archive.salzburgerfestspiele.at/archiv), the concert archives of the Gesellschaft der Musikfreunde in Wien (https://www.musikverein.at/en/archive/) and the Konzerthaus databank (https://konzerthaus.at/datenbanksuche) and is part of an ongoing project to visibilize the gendered nature of collaborative piano at the most prestigious levels. Thanks to Diána Fuchs (https://diana-fuchs.com/) who assisted with the collection of the Musikverein data, with the support of Melanie Unseld at the Department of Musicology and Performance Studies at the mdw - University of Music and Performing Arts Vienna.</p>
Corpus of song lyrics in Spanish labeled for gender-based violence against women
<pre><strong>Content:</strong> Labeled corpus including expressions of gender-based violence extracted from song lyrics in Spanish.<br><strong>Tags:</strong> [0: no gender-based violence; 1: gender-based violence] <strong>Description:</strong> It consists of 1000 labeled expressions, of which 778 correspond to expressions without content of gender-based violence and 222 data <br>that contain expressions of gender-based violence collected from song lyrics in Spanish. <strong>Language:</strong> Spanish <strong>Size:</strong> 549KB</pre>
Data from: Sympatric wren-warblers partition acoustic signal space and song perch height
Animals employing acoustic signals, such as birds, must effectively communicate over both background noise and potentially attenuating objects in the environment. To surmount these obstacles, animals evolve species-specific acoustic signals that do not overlap with sources of interference (such as songs of close relatives), and issue these songs from locations that maximize transmission. In multispecies assemblages of birds, the acoustic resource may thus be interspecifically partitioned along multiple axes, including song perch height and signal space. However, very few such studies have focused on open habitats, where differences in sound transmission patterns and limited availability of song perches may drive competition across multiple axes within signal space. Here, we demonstrate acoustic signal space partitioning in four sympatric species of wren-warbler (Cisticolidae, Prinia), in an Indian dry deciduous scrub-grassland habitat. We found that the breeding songs of the four species partition acoustic signal space, resulting in interspecific community organization. Within each species' signal space, we uncovered different intraspecific patterns in note diversity. Two species partitioned intraspecific signal space into multiple note types, whereas the other two varied note repetition rate to different extents. Finally, we found that the four species also partition song perch heights, thus exhibiting acoustic niche separation along multiple axes. We hypothesize that divergent song perch heights may be driven by competition for higher singing perches or other ecological factors rather than signal propagation. Acoustic signal partitioning along multiple axes may therefore arise from a combination of diverse ecological processes.
Acoustic feature measurements of male and female NZ bellbird (Anthornis melanura) song syllables
<p>Acoustic feature measurements of 20,700 syllables (acoustic units) of male and female birdsong, from NZ bellbirds (Anthornis melanura). The measurements were extracted in Koe bioacoustics software (koe.io.ac.nz), on recordings from six sites in the Hauraki Gulf, northeastern New Zealand. The sites are Tawhiti Rahi island (Poor Knights island group), Lady Alice Island (Hen and Chicks island group), Hauturu (Little Barrier Island), Tawharanui Peninsula, Repanga (Cuvier Island), and Tiritiri Matangi Island.</p> <p>Descriptions of extracted acoustic features can be found at https://github.com/fzyukio/koe/wiki#extract-unit-features</p>
Data for: Ecology and behavior predict an evolutionary trade-off between song complexity and elaborate plumages in antwrens (Aves, Thamnophilidae)
<p>The environment can impose constraints on signal transmission properties such that signals should evolve in predictable directions (Sensory Drive Hypothesis). However, behavioral and ecological factors can limit investment in more than one sensory modality leading to a trade-off in use of different signals (Transfer Hypothesis). In birds, there is mixed evidence for both sensory drive and transfer hypothesis. Few studies have tested sensory drive while also evaluating the transfer hypothesis, limiting understanding of the relative roles of these processes in signal evolution. Here, we assessed both hypotheses using acoustic and visual signals in male and female antwrens (Thamnophilidae), a species-rich group that inhabits diverse environments and exhibits behaviors, such as mixed-species flocking, that could limit investment in different signal modalities. We uncovered significant effects of habitat (sensory drive) and mixed-species flocking behavior on both sensory modalities, and we revealed evolutionary trade-offs between song and plumage complexity, consistent with the transfer hypothesis. We also showed sex- and trait-specific responses in visual signals that suggest both natural and social selection play an important role in the evolution of sexual dimorphism. Altogether, these results support the idea that environmental (sensory drive) and behavioral pressures (social selection) shape signal evolution in antwrens.</p>
Figure 3 in Stridulation in Aphodius dung beetles: Songs and morphology of stridulatory organs in North American Aphodius species (Scarabaeidae)
Figure 3. SEM of the plectrum (a) and the file (b) of Aphodius rainieri.
Figure 3 in Run to the nest: A parody on the Iron Maiden song by Corotoca spp. (Coleoptera, Staphylinidae)
Figure 3. Simple regressions using each of the independent variables of the multiple linear regressions for time until extrusion and return to the nest by Corotoca melantho and C. fontesi. Left: return time (y-var) vs time until stopping (x-var) (R² = 0. 6152; F = 35. 17, P <0. 001); Right: return time vs distance traveled (x-var) (R² = 0. 10; F = 6. 461, P = 0. 13).
Figure 4 in Run to the nest: A parody on the Iron Maiden song by Corotoca spp. (Coleoptera, Staphylinidae)
Figure 4. Bar plots of the average speeds of the two beetle species (bars represent the standard errors of the means). Lighter bars represent V1 (the out of nest velocity) while the darker bars represent V2 (the return speed). Higher speed spindle as are observed when returning to the nest.
Figure 2 in Run to the nest: A parody on the Iron Maiden song by Corotoca spp. (Coleoptera, Staphylinidae)
Figure 2. Scheme of the collection methodology: (A) Corotoca spp. leaves the nest to follow the host foraging trail. It is closely observed until stopping; the distance from the nest to where the beetle stops is measured and the time required is recorded; (B) When the beetle stops, the stopwatch is paused while larval extrusion occurs; (C) After larval the position, the return time of the beetle to the nest is recorded.
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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)
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.