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1,719 results for “song”
Cumulative cultural evolution and mechanisms for cultural selection in wild bird songs
<p>Cumulative cultural evolution, the accumulation of sequential changes within a single socially learned behaviour that results in improved function, is prominent in humans and has been documented in experimental studies of captive animals and managed wild populations. Here, we provide evidence that cumulative cultural evolution has occurred in the learned songs of Savannah sparrows. In a first step, "click trains" replaced "high note clusters" over a period of three decades. We use mathematical modeling to show that this replacement is consistent with the action of selection, rather than drift or frequency-dependent learning biases. Generations later, young birds elaborated the "click train" song form by adding more clicks. We show that the new songs with more clicks elicit stronger behavioural responses from both males and females. Therefore, we suggest that a combination of social learning, innovation, and sexual selection favoring a specific discrete trait was followed by directional sexual selection that resulted in naturally occurring cumulative cultural evolution in the songs of this wild animal population.</p>
Anthropogenic noise, song, and territorial aggression in southern house wrens
<p>Anthropogenic noise constrains the transmission of birdsong and alters the behavior of receivers. Many birds adjust their acoustic signals to minimize the interference of anthropogenic noise on signal transmission. Birds may also change their acoustic signals to exchange information during aggressive interactions. However, it is unclear how birds deal with a potential trade-off between adjusting their acoustic signals to better transmit in noisy environments versus mediating aggressive interactions. Additionally, we do not know how urbanization and anthropogenic noise alters the territorial behavior of receivers. We investigated the interplay among song, territorial aggression, urbanization, and anthropogenic noise, in males of the southern house wren (<em>Troglodytes aedon musculus</em>), using recordings of spontaneous songs (non-aggressive context) and a playback experiment simulating a male territorial intrusion (aggressive context). We found that urban wrens behaved more aggressively in response to the intruder by singing more and spent more time closer to the intruder than rural wrens regardless of noise. Males produced songs with lower minimum frequency and trills with wider frequency bandwidth and higher vocal performance under acute (playback) than relaxed (post-playback) aggressive encounters. These results suggest that males use songs to communicate aggressive intent or fighting ability. Urban wrens produced higher-pitched songs and trills than rural wrens irrespective of aggressive context. Urban wrens in the noisiest territories also produced the highest-pitched trills but only in the non-aggressive context. Rural wrens in the noisiest territories tended to produce the longest songs (non-aggressive context) or produced the shortest songs (aggressive context). Results suggest that urbanization affects territorial and vocal behaviors in southern house wrens. Males in this species seem to primarily adjust acoustic signals in response to the territorial intruder rather than noise.</p>
Fig. 11 in Differences in the male calling songs of two sibling species of Cicada (Hemiptera: Cicadoidea) in Greece
Fig. 11. Dendrogram of the relationships between 20 males of C. mordoganensis Boulard from Samos and Ikaria and 10 males of C. orni L. from Athens, revealed by UPGMA cluster analysis of Euclidean distances. Data standardized. IK – Ikaria; SA – Samos; AT –Athens; numbers refer to specimens.
Figs 1–2 in Differences in the male calling songs of two sibling species of Cicada (Hemiptera: Cicadoidea) in Greece
Figs 1–2: Left lateral view of the genital segments of a male. 1 – C. mordoganensis Boulard from Samos; 2 – C. lorni L. from Dionysos, Athens. Scale = 0.8 mm.
Figs 7–10 in Differences in the male calling songs of two sibling species of Cicada (Hemiptera: Cicadoidea) in Greece
Figs 7–10. Song of a male of C. orni L. (Dionysos, Athens). 7 – oscillogram over a period of 10 s; 8 – oscillogram with an extended time-base of 0.5 s; 9 – sonagram over a period of 1.0 s; 10 – spectrogram.
Figs 3–6 in Differences in the male calling songs of two sibling species of Cicada (Hemiptera: Cicadoidea) in Greece
Figs 3–6. Song of a male of C. mordoganensis Boulard (Samos). 3 – oscillogram over a period of 10 s; 4 – oscillogram with an extended time-base of 0.5 s; 5 – sonagram over a period of 1.55 s; 6 – spectrogram.
Automatically Annotated Quan Tang Shi and Quand Song Shi
<p>Dataset containing the entire poetry of the Quan Tang Shi 全唐詩 and Quan Song Shi 全宋詩, automatically annotated using a Community annotator. This is supplementary material for "Leveraging graph algorithms to speed up the annotation of large rhymed corpora", CLAO vol 51.</p>
Rhyme annotation evaluation: a Hand-Annotated Sample of the Quan Tang Shi and Quan Song Shi
<p>This dataset consists of 3 files:</p> <ul> <li>hand_annotated_sample.json contains a sample of 444 poems from the Quan Tang Shi and Quan Song Shi; these poems were pre-annotated by a Community annotator and manually reviewed / amended by the main author.</li> <li> <p>hand_annotated_subsample_(author).json contains a sub-sample of the previous file, containing 44 poems (10%); the annotations therein are identical to the annotations in the file above.</p> </li> <li> <p>hand_annotated_subsample_(colleague).json contains the same sub-sample of 44 poems, but annotated by a colleague of the author. The aim is to assess human inter-annotator agreement for this type of poetry.</p> <p> </p> <p> </p> <p> </p> </li> </ul> <p> </p>
Are urbanization and brood parasitism associated with differences in telomere lengths in song sparrows?
<p>Urbanization reflects a major form of environmental change impacting wild birds globally. Whereas urban habitats may provide increased availability of water, some food items, and reduced predation levels compared to rural, they can also present novel stressors including increased light at night, ambient noise, and reduced nutrient availability. Urbanization can also alter levels of brood parasitism, with some host species experiencing elevated levels of brood parasitism in urban areas compared to rural areas. Though the demographic and behavioral consequences of urbanization and brood parasitism have received considerable attention, their consequences for cellular-level processes are less understood. Telomeres provide an opportunity to understand the cellular consequences of different environments as they are a well-established metric of biological state that can be associated with residual lifespan, disease risk, and behaviour, and are known to be sensitive to environmental conditions. Here we examine the relationships between urbanization, brood parasitism, and blood telomere lengths in adult and nestling song sparrows (Melospiza melodia). Song sparrows are a North American songbird found in both urban and rural habitats that experience high rates of brood parasitism by brown-headed cowbirds (Molothrus ater) in the urban, but not the rural, sites in our study system. Among adults and nestlings from non-parasitized nests, we found no differences in relative telomere lengths between urban and rural habitats. However, among urban nestlings, the presence of a brood parasite in the nest was associated with significantly shorter relative telomere lengths compared to when a brood parasite was absent. Our results suggest a novel, indirect, impact of urbanization on nestling songbirds through the physiological impacts of brood parasitism.</p>
Figs. 1–12. Dentaneura henanensis Song, Li in A remarkable new genus and species of Erythroneurini (Hemiptera: Cicadellidae: Typhlocybinae) from China
Figs. 1–12. Dentaneura henanensis Song, Li & Dai sp. nov. (male). 1. Habitus, dorsal view; 2. habitus, lateral view; 3. face; 4. wings (forewing and hind wing); 5. abdominal apodemes; 6. head and thorax, dorsal view; 7. pygofer, lateral view; 8. subgenital plate; 9. style; 10. aedeagus, lateral view; 11. aedeagus, ventral view; 12. connective.
Re-igniting Windrush Folk stories and songs to improve African-Caribbean mental health disparities in the London Boroughs of Lewisham & Greenwich
<p>This cross-disciplinary intergenerational project created a network of experts from multiple fields, aiming to achieve several key objectives. Using A-C Folk Songs and Art methods, it provided support to hub spaces, including training, remuneration for services and connecting them to therapists. The project also embeded culturally relevant narrative therapy techniques in community settings to support proactive positive mental health, whilst ensuring community organisations had access to affordable meeting spaces. Finally, it created opportunities for intergenerational connection, fostering a more cohesive and supportive community environment.</p> <p>A-C communities are 40% more likely than white-British people to come into contact with mental health services and be detained under the Mental Health Act, reflecting a stark historical pattern of structural racism and its ensuing health inequalities within the mental health system (Vige, 2019). Access to mental healthcare services are limited as a result of institutional, cultural and socio- economic exclusion factors related to BME groups (Memon, et al., 2016). The field of clinical psychology often 'assumes a deficit-based-approach' to the mental health of those minoritised by society (Renkly & Bertolini, 2018). This model is problematic with those from A-C groups because it places emphasis on the individual rather than systems of oppression and ignores the ways cultural traditions and communities create supporting mechanisms for mental health. Our approach offers an alternative model.</p> <p>This project uses an augmented generative co-design framework base on Bird et al (2021) where a narrative enquiry (Pinnegar, & Daynes, 2007) is used to engage participants in conversations on folk songs and how these can be utilised to support mental health of the local A-C community. Through a series of workshops we brought together storytellers, A-C elders and young adults 18-85 to gather traditional stories as well as to create new ones. This supported our understanding of both folk stories and song routes and the lessons learnt within them. We used these stories as analogies to map out the socio-cultural ways in which mental health is discussed in African-Caribbean communities, capturing these conversations via film which, after each session, is edited and re-shared in the next session to create a focus for future conversations. </p> <p>During the project we have co-produced a toolkit including: film, workshop plans and thematic analysis of findings. Our work will feed into at least 2 publications which connect the 10 NHS 75 projects (article and policy document) and we plan on publishing at least 2 further works; 1 on methodological insight and the other on research findings.</p> <p>Our work has been shared at the International symposium (1st July 2024 University of Greenwich) linked to mental Health and the Climate crisis, opening possible future avenues of work with international organisations as well as with the Caribbean Association. </p> <p> </p>
The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS)
<p><strong>Description</strong></p> <p>The Ryerson Audio-Visual Database of Emotional Speech and Song (<a href="https://affectivedatascience.com/datasets.html#ravdess">RAVDESS</a>) contains 7356 files (total size: 24.8 GB). The dataset contains 24 professional actors (12 female, 12 male), vocalizing two lexically-matched statements in a neutral North American accent. Speech includes calm, happy, sad, angry, fearful, surprise, and disgust expressions, and song contains calm, happy, sad, angry, and fearful emotions. Each expression is produced at two levels of emotional intensity (normal, strong), with an additional neutral expression. All conditions are available in three modality formats: Audio-only (16bit, 48kHz .wav), Audio-Video (720p H.264, AAC 48kHz, .mp4), and Video-only (no sound). Note, there are no song files for Actor_18.</p> <p>The RAVDESS was developed by Dr <a href="https://affectivedatascience.com/people/livingstone_sr" target="_blank" rel="noopener">Steven R. Livingstone</a>, who now leads the <a href="https://affectivedatascience.com">Affective Data Science Lab</a>, and Dr <a href="https://www.torontomu.ca/psychology/about-us/our-people/faculty/frank-russo/" target="_blank" rel="noopener">Frank A. Russo</a> who leads the <a href="https://psychlabs.torontomu.ca/smartlab/">SMART Lab</a>.</p> <p><strong>Citing the RAVDESS</strong></p> <p>The RAVDESS is released under a Creative Commons Attribution license, so please cite the RAVDESS if it is used in your work in any form. Published academic papers should use the academic paper citation for our PLoS1 paper. Personal works, such as machine learning projects/blog posts, should provide a URL to this Zenodo page, though a reference to our PLoS1 paper would also be appreciated.</p> <p><em>Academic paper citation</em></p> <blockquote> <p>Livingstone SR, Russo FA (2018) The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English. PLoS ONE 13(5): e0196391. <a href="https://doi.org/10.1371/journal.pone.0196391">https://doi.org/10.1371/journal.pone.0196391</a>.</p> </blockquote> <p><em>Personal use citation</em></p> <blockquote> <p>Include a link to this Zenodo page - <a href="../record/1188976">https://zenodo.org/record/1188976</a></p> </blockquote> <p><strong>Commercial Licenses</strong></p> <p>Commercial licenses for the RAVDESS can be purchased. For more information, please visit our <a href="https://psychlabs.torontomu.ca/smartlab/ravdess-commercial-licensing/">license page of fees</a>, or contact us at <a href="mailto:ravdess@gmail.com?subject=RAVDESS%20Commercial%20License">ravdess@gmail.com</a>.</p> <p><strong>Contact Information</strong></p> <p>If you would like further information about the RAVDESS, to purchase a commercial license, or if you experience any issues downloading files, please contact us at <a href="mailto:ravdess@gmail.com?subject=RAVDESS%20feedback%20from%20Zenodo">ravdess@gmail.com</a>.</p> <p><strong>Example Videos</strong></p> <p>Watch a sample of the RAVDESS <a href="https://www.youtube.com/watch?v=Y7OQoNEu3dY">speech</a> and <a href="https://www.youtube.com/watch?v=XQkmH4oYZkg">song</a> videos.</p> <p><strong>Emotion Classification Users</strong></p> <p>If you're interested in using machine learning to classify emotional expressions with the RAVDESS, please see our new RAVDESS Facial Landmark Tracking data set [<a href="../record/3255102">Zenodo project page</a>].</p> <p><strong>Construction and Validation</strong></p> <p>Full details on the construction and perceptual validation of the RAVDESS are described in our PLoS ONE paper - <a href="https://doi.org/10.1371/journal.pone.0196391">https://doi.org/10.1371/journal.pone.0196391</a>.</p> <p>The RAVDESS contains 7356 files. Each file was rated 10 times on emotional validity, intensity, and genuineness. Ratings were provided by 247 individuals who were characteristic of untrained adult research participants from North America. A further set of 72 participants provided test-retest data. High levels of emotional validity, interrater reliability, and test-retest intrarater reliability were reported. Validation data is open-access, and can be downloaded along with our paper from <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0196391">PLoS ONE</a>.</p> <p><strong>Contents</strong></p> <p><em>Audio-only files</em></p> <p>Audio-only files of all actors (01-24) are available as two separate zip files (~200 MB each):</p> <ul> <li>Speech file (Audio_Speech_Actors_01-24.zip, 215 MB) contains 1440 files: 60 trials per actor x 24 actors = 1440. </li> <li>Song file (Audio_Song_Actors_01-24.zip, 198 MB) contains 1012 files: 44 trials per actor x 23 actors = 1012.</li> </ul> <p><em>Audio-Visual and Video-only files</em></p> <p>Video files are provided as separate zip downloads for each actor (01-24, ~500 MB each), and are split into separate speech and song downloads:</p> <ul> <li>Speech files (Video_Speech_Actor_01.zip to Video_Speech_Actor_24.zip) collectively contains 2880 files: 60 trials per actor x 2 modalities (AV, VO) x 24 actors = 2880.</li> <li>Song files (Video_Song_Actor_01.zip to Video_Song_Actor_24.zip) collectively contains 2024 files: 44 trials per actor x 2 modalities (AV, VO) x 23 actors = 2024.</li> </ul> <p><em>File Summary</em></p> <p>In total, the RAVDESS collection includes 7356 files (2880+2024+1440+1012 files).</p> <p><strong>File naming convention</strong></p> <p>Each of the 7356 RAVDESS files has a unique filename. The filename consists of a 7-part numerical identifier (e.g., 02-01-06-01-02-01-12.mp4). These identifiers define the stimulus characteristics: <br><br><em>Filename identifiers </em></p> <ul> <li>Modality (01 = full-AV, 02 = video-only, 03 = audio-only).</li> <li>Vocal channel (01 = speech, 02 = song).</li> <li>Emotion (01 = neutral, 02 = calm, 03 = happy, 04 = sad, 05 = angry, 06 = fearful, 07 = disgust, 08 = surprised).</li> <li>Emotional intensity (01 = normal, 02 = strong). NOTE: There is no strong intensity for the 'neutral' emotion.</li> <li>Statement (01 = "Kids are talking by the door", 02 = "Dogs are sitting by the door").</li> <li>Repetition (01 = 1st repetition, 02 = 2nd repetition).</li> <li>Actor (01 to 24. Odd numbered actors are male, even numbered actors are female).</li> </ul> <p><br><em>Filename example: 02-01-06-01-02-01-12.mp4 </em></p> <ol> <li>Video-only (02)</li> <li>Speech (01)</li> <li>Fearful (06)</li> <li>Normal intensity (01)</li> <li>Statement "dogs" (02)</li> <li>1st Repetition (01)</li> <li>12th Actor (12)</li> <li>Female, as the actor ID number is even.</li> </ol> <p><strong>License information</strong></p> <p>The RAVDESS is released under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> </p> <p>Commercial licenses for the RAVDESS can also be purchased. For more information, please visit our <a href="https://smartlaboratory.org/ravdess/commercial-licensing/">license fee page</a>, or contact us at <a href="mailto:ravdess@gmail.com?subject=RAVDESS%20Commercial%20License">ravdess@gmail.com.</a></p> <p><strong>Related Data sets</strong></p> <ul> <li>RAVDESS Facial Landmark Tracking data set [<a href="../record/3255102">Zenodo project page</a>].</li> </ul>
MSD-I: Million Song Dataset with Images for Multimodal Genre Classification
<p>The Million Song Dataset (https://labrosa.ee.columbia.edu/millionsong/) is a collection of metadata and precomputed audio features for 1 million songs. Along with this dataset, a dataset with annotations of 15 top-level genres with a single label per song was released. In our work, we combine the CD2c version of this genre datase (http://www.tagtraum.com/msd_genre_datasets.html) with a collection of album cover images. </p> <p><br> The final dataset contains 30,713 tracks from the MSD and their related album cover images, each annotated with a unique genre label among 15 classes. Based on an initial analysis on the images, we identified that this set of tracks is associated to 16,753 albums, yielding an average of 1.8 songs per album.</p> <p>We randomly divide the dataset into three parts: 70% for training, 15% for validation, and 15% for test, with no artist and album overlap across these sets. This is crucial to avoid possible overfitting, as the classifier may learn to predict the artist instead of the genre. </p> <p> </p> <p>Content:</p> <p>MSD-I dataset (mapping, metadata, annotations and links to images)<br> Data splits and feature vectors for TISMIR single-label classification experiments </p> <p>These data can be used together with the Tartarus deep learning python module https://github.com/sergiooramas/tartarus.</p> <p> </p> <p>Scientific References:</p> <p>Please cite the following paper if using MSD-I dataset or Tartarus software.</p> <p>Oramas, S., Barbieri, F., Nieto, O., and Serra, X (2018). Multimodal Deep Learning for Music Genre Classification, Transactions of the International Society for Music Information Retrieval, V(1).</p>
Hit Song Prediction (Million Song Dataset and Audio Features)
<p><strong>Hit Song Prediction Dataset</strong></p> <p>This dataset is based on the Million Song Dataset (MSD), which contains one million songs that are representative for western commercial music released between 1922 and 2011. The dataset contains release year information for 515,576 of the MSD songs. Please refer to http://millionsongdataset.com/ for further information on the million song dataset.</p> <p>For our hit song prediction experiments, we extract high- and low-level audio features using the Essentia toolkit (cf. https://essentia.upf.edu/). For the high-level features, we make use of the pre-trained classifiers as provided by Essentia. For a detailed description of the features, please visit the Essentia documentation.</p> <p><br> The dataset hence contains:</p> <ul> <li><strong>Audio features</strong>: the compressed msd_audio_features.tar.gz file contains the low- and high-level features for each track, stored as json files. Please note that we organize all MSD audio feature files based on the track's identifier with one folder holding all tracks with the same first letter of the track identifier to keep the files manageable. For each track, we provide two files: one containing the high-level and one containing the low-level features extracted by Essentia.</li> <li><strong>Billboard data:</strong> the folder billboard_data contains two files: msd_bb_matches.csv contains information about the MSD tracks that were also featured in the Billboard Hot 100 charts. Here, we provide the MSD id, Echo Nest id, artist name, track title, release year, peak position in Billboard charts and the number of weeks in the charts. The second file, msd_bb_non_matches.csv contains meta-information about the tracks of the MSD that were not featured in the Billboard Hot 100 and hence were used as negative samples. Here, we provide the MSD id, Echo Nest id, artist name, track title and the release year.</li> </ul> <p><br> If you make use of the dataset, please kindly cite the following paper:</p> <p>Eva Zangerle, Michael Vötter, Ramona Huber, and Yi-Hsuan Yang. Hit Song Prediction: Leveraging Low- and High-Level Audio Features. In Proceedings of the 20th International Society for Music Information Retrieval Conference 2019 (ISMIR 2019), 2019.</p> <p><br> @inproceedings{zangerle_ismir19,<br> title = {{Hit Song Prediction: Leveraging Low- and High-Level Audio Features}},<br> author = {Eva Zangerle and Ramona Huber and Michael V\"{o}tter and Yi-Hsuan Yang},<br> year = {2019},<br> booktitle = {{Proceedings of the 20th International Society for Music Information Retrieval Conference 2019 (ISMIR 2019)}},<br> }</p>
Figure 8 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 8. SEM image of the stridulatory file in Poecilimon affinis dinaricus, Ogoreljača–Mali Jadovnik (meadow).
Figure 10 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 10. Poecilimon pseudornatus, oscillograms of male calling song at 28 °C from: A) Sopotnica Ra; B) Ogoreljača–Mali Jadovnik (roadside).
Figure 7 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 7. Calling song of Poecilimon affinis dinaricus males from Ogoreljača–Mali Jadovnik (meadow). Syllables at different temperatures: A) syllable at 20 °C; B) syllable at 28 °C.
Figure 3 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 3. Isophya clara, oscillograms of male calling song from Ovčar-Kablar Gorge. A) 6 syllables; B) syllable with after-click.
Figure 4 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 4. SEM photographs of the stridulatory files: (A) Isophya clara, Gvozd; (B) Isophya clara, Ovčar-Kablar Gorge.
Figure 2 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 2. Isophya clara, oscillograms of male calling song from Gvozd. A) 6 syllables; B) syllable with after-click.
ScienceDex guides
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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)
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.