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19 results for “song learning”
Machine learning reveals that climate, geography, and cultural drift all predict bird song variation in coastal Zonotrichia leucophrys
<p>Previous work has demonstrated that there is extensive variation in the songs of White-crowned Sparrow (<em>Zonotrichia leucophrys</em>) throughout the species range, including between neighboring (and genetically distinct) subspecies <em>Z. l. nuttalli </em>and <em>Z. l. pugetensis</em>. Using a machine learning approach to bioacoustic analysis, we demonstrate that variation in song is correlated with year of recording (representing cultural drift), geographic distance, and climatic differences, but the response is subspecies- and season-specific. Automated machine learning methods of bird song annotation can process large datasets more efficiently, allowing us to examine 1,913 recordings across ~60 years. We utilize a recently published artificial neural network to automatically annotate White-crowned Sparrow vocalizations. By analyzing differences in syllable usage and composition, we recapitulate the known pattern where <em>Z. l. nuttalli </em>and <em>Z. l. pugetensis </em>have significantly different songs. Our results are consistent with the interpretation that these differences are caused by the changes in characteristics of syllables in the White-crowned Sparrow repertoire. This supports the hypothesis that the evolution of vocalization behavior is affected by the environment, in addition to population structure.</p>
Manually labeled Bird song dataset of 22 species from Xeno-canto to enhance deep learning acoustic classifiers with contextual information.
<p>Data accompanying the paper: Jeantet and Dufourq (2023). Empowering Deep Learning Acoustic Classifiers with Human-like Ability to Utilize Contextual Information for Wildlife Monitoring. <em>Ecological Informatics</em>. 77, 15749541, DOI: 10.1016/j.ecoinf.2023.102256</p> <p> </p> <p>Our investigation contributes to the field of deep learning and bioacoustics by highlighting the potential for improved classification performance through the incorporation of contextual information such as time and location.</p> <p>To test if spatial-temporal information can enhance deep learning classifier, we developed a subset dataset derived from Xeno-Canto that included location metadata as input alongside the spectrogram. We used this dataset with the primary purpose of creating a bird song classification task with species carefully selected to share similar vocal characteristics but from distinct geographical distributions. We only considered the recordings of category `A', corresponding to the best quality score in the database.</p> <p>The dataset contains songs of <strong>22 bird species</strong> from 5 families and genera differents. The recordings were downloaded from the Xeno-canto database in .wav format and each recording was <strong>manually annotated </strong>by labelling the start and stop time for every vocalisation occurrence using Sonic Visualiser. In total, database contained 6537 occurrences of bird songs of various length from <strong>967 file recordings</strong>. A precise description of the distribution by species and country can be found in the associated article.</p> <p> </p> <p>The audio files are provided in "Audio.zip" and the manually verified annotation in "Annotations.zip". The name of each file follows the following nomenclature: Family_genus_species_country of recording_date of recording_ID Xenocanto_type of song.wav/svl. The meta-data information of each file can be find in the csv file provided (Xenocanto_metadata_qualityA_selection) based on the number of the ID Xeno-canto. The annotations can be viewed using the Sonic Visualiser software. The python codes to process these files and train neural networks can be found here : github</p> <p>The files were divided into a <strong>training folder</strong> and a<strong> validation folder</strong> to train and evaluate the efficiency of each method. For each species and country, we randomly selected 70% of the downloaded recordings for the training dataset and kept the remaining 30% for validation.</p> <p><strong>Process to select the species</strong> : We selected the ten most recorded families in the Passeriformes order, the most represented order in Xeno-canto database. From each of the ten families, we again sub-samples the ten most recorded genera. For each genus, we observed the countries of the recordings and the number of available recordings per species and countries. From these observations, we made a self-selection of genera containing species with similar songs but recorded in different regions, with enough recordings available by species and country to form a dataset . At the end, 5 genus were selected containing 22 species. We considered only recordings associated with bird songs, specifically, within Xeno-canto we selected the `song' type. To balance the number of recordings between species of the same genus, we reduced the number of recordings for the most represented species. Thus, for each genus we calculated the average of the number of records available per species and per country and limited the number of recordings for the species/country pairs that were in greater number to this value plus two.</p> <p> </p> <p> </p> <p> </p>
Machine learning reveals that climate, geography, and cultural drift all predict bird song variation in coastal Zonotrichia leucophrys
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Data from: Song learning and cognitive ability are not consistently related in a songbird
Learned aspects of song have been hypothesized to signal cognitive ability in songbirds. We tested this hypothesis in hand-reared song sparrows (Melospiza melodia) that were tutored with playback of adult songs during the critical period for song learning. The songs developed by the 19 male subjects were compared to the model songs to produce two measures of song learning: the proportion of notes copied from models and the average spectrogram cross-correlation between copied notes and model notes. Song repertoire size, which reflects song complexity, was also measured. At 1 year of age, subjects were given a battery of five cognitive tests that measured speed of learning in the context of a novel foraging task, color association, color reversal, detour-reaching, and spatial learning. Bivariate correlations between the three song measures and the five cognitive measures revealed no significant associations. As in other studies of avian cognition, different cognitive measures were for the most part not correlated with each other, and this result remained true when 22 hand-reared female song sparrows were added to the analysis. General linear mixed models controlling for effects of neophobia and nest of origin indicated that all three song measures were associated with better performance on color reversal and spatial learning but were associated with worse performance on novel foraging and detour-reaching. Overall, the results do not support the hypothesis that learned aspects of song signal cognitive ability.
Social experiences shape song preference learning independent of developmental exposure to song
<p>Communication governs the formation and maintenance of social relationships. The interpretation of communication signals depends not only on the signal's content, but also on a receiver's individual experience. Experiences throughout life may interact to affect behavioral plasticity, such that a lack of developmental sensory exposure could constrain adult learning, while salient adult social experiences could remedy developmental deficits. We investigated how experiences impact the formation and direction of female auditory preferences in the zebra finch. Zebra finches form long-lasting pair bonds and females learn preferences for their mate's vocalizations. We found that after two weeks of cohabitation with a male, females formed pair bonds and learned to prefer their partner's song regardless of whether they were reared with ("normally-reared") or without ("song-naïve") developmental exposure to song. In contrast, females that heard but did not physically interact with a male did not prefer his song. In addition, previous work has found that song-naive females do not show species-typical preferences for courtship song. We found that cohabitation with a male ameliorated this difference in preference. Thus, courtship and pair bonding, but not acoustic-only interactions, strongly influence preference learning regardless of rearing experience, and may dynamically drive auditory plasticity for recognition and preference.</p>
Machine Learning for Bird Song Learning (ML4BL) dataset
<p><strong>General description</strong></p> <p>This dataset contains Zebra Finch decisions about perceptual similarity on song units. All the data and files are used for reproducing the results of the paper 'Bird song comparison using deep learning trained from avian perceptual judgments' by the same authors. </p> <p><strong>Git repo on Zenodo:</strong> <a href="https://doi.org/10.5281/zenodo.5545932">https://doi.org/10.5281/zenodo.5545932</a><br> <strong>Git repo access: </strong><a href="https://github.com/veronicamorfi/ml4bl/tree/v1.0.0">https://github.com/veronicamorfi/ml4bl/tree/v1.0.0</a></p> <p><strong>Directory organisation:</strong><br> ML4BL_ZF<br> |_files<br> |_Final_probes_20200816.csv - all trials and decisions of the birds (aviary 1 cycle 1 data are removed from experiments)<br> |_luscinia_triplets_filtered.csv - triplets to use for training<br> |_mean_std_luscinia_pretraining.pckl - mean and std of luscinia triplets used for trianing<br> |_*_cons_* - % side consistency on triplets (train/test) - train set contains both train and val splits<br> |_*_gt_* - cycle accuracy for triplets of the specific bird (train/test) - train set contains both train and val splits<br> |_*_trials_* - number of decisions made for a triplet (train/test) - train set contains both train and val splits<br> |_*_triplets_* - triplet information (aviary_cycle-acc_birdID, POS, NEG, ANC) (train/test) - train set contains both train and val splits<br> |_*_low*_ - low-margin (ambiguous) triplets (train/val/test)<br> |_*_high_ - high-margin (unambiguous) triplets (train/val/test)<br> |_*_cycle_bird_keys_* - unique aviary_cycle-acc_birdID keys (train/test) - train set contains both train and val splits<br> |_TunedLusciniaV1e.csv - pairwise distance of two recordings computed by Luscinia<br> |_training_setup_1_ordered_acc_single_cons_50_70_trials.pckl - dictionary containing everything needed for training the model (keys: 'train_keys', 'train_triplets', 'val_keys', 'vali_triplets', 'test_triplets', 'test_keys', 'train_mean', 'train_std')<br> |_melspecs - *.pckl - melspectrograms of recordings<br> |_wavs - *wav - recordings<br> |_README.txt</p> <p><strong>Recordings</strong></p> <p>887 syllables extracted from zebra finch song recordings, with a sampling rate of 48kHz and high pass filtered (100Hz), with a 20ms intro/outro fade. </p> <p><strong>Decisions</strong></p> <p>Triplets were created from the recordings and the birds made side based decisions about their similarity (see 'Bird song comparison using deep learning trained from avian perceptual judgments' for further information).</p> <p><strong>Training dictionary Information</strong></p> <p>Dictionary keys:<br> 'train_keys', 'train_triplets', 'val_keys', 'vali_triplets', 'test_triplets', 'test_keys', 'train_mean', 'train_std'</p> <p>train_triplets/vali_triplets/test_triplets: <br> Aviary_Cycle_birdID, POS, NEG, ANC, Decisions, Cycle_ACC(%), Consistency(%)<br> <br> train_keys/val_keys/test_keys:<br> Aviary_Cycle_birdID</p> <p>train_mean/train_std:<br> shape: (1, mel_bins)</p> <p> </p> <p><strong>Open Access</strong></p> <p>This dataset is available under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.</p> <p><br> <strong>Contact info</strong></p> <p>Please send any questions about the recordings to:<br> Lies Zandberg: Elisabeth.Zandberg@rhul.ac.uk</p> <p>Please send any feedback or questions about the code and the rest of the data to:<br> Veronica Morfi: g.v.morfi@qmul.ac.uk</p>
Data from: Variable ambient temperature promotes song learning and production in zebra finches
<p>This data set contains the data used in the analysis of the article titled: Variable ambient temperature promotes song learning and production in zebra finches.</p>
Data from: Variable ambient temperature promotes song learning and production in zebra finches
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Social experiences shape song preference learning independent of developmental exposure to song
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Data from: Song learning and cognitive ability are not consistently related in a songbird
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Data from: Song evolution, speciation, and vocal learning in passerine birds
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Pairwise Learning to Rank for Hit Song Prediction: Dataset
<p>Dataset for the paper "Pairwise Learning to Rank for Hit Song Prediction", to be published at BTW 2023.</p>
Data from: Immigrant song: males and females learn songs post-dispersal in a tropical bird
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Data from: Open-ended song learning in a hummingbird
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Data from: Hatching late in the season requires flexibility in the timing of song learning
Most songbirds learn their songs from adult tutors, who can be their father or other male conspecifics. However, the variables that control song learning in a natural social context are largely unknown. We investigated whether the time of hatching of male domesticated canaries has an impact on their song development and on the neuroendocrine parameters of the song control system. Average age difference between early- and late-hatched males was 50 days with a maximum of 90 days. Song activity of adult tutor males decreased significantly during the breeding season. While early-hatched males were exposed to tutor songs for on average the first 99 days, late-hatched peers heard adult song only during the first 48 days of life. Remarkably, although hatching late in the season negatively affected body condition, no differences between both groups of males were found in song characteristics either in autumn or in the following spring. Similarly, hatching date had no effect on song nucleus size and circulating testosterone levels. Our data suggest that late-hatched males must have undergone accelerated song development. Furthermore, the limited tutor song exposure did not affect adult song organization and song performance.
Data from: Timing of perineuronal nets development in the zebra finch song control system correlates with developmental song learning
The appearance of perineuronal nets (PNN) represents one of the mechanisms that contribute to the closing of sensitive periods for neural plasticity. This relationship has mostly been studied in the ocular dominance model in rodents. Previous studies also indicated that PNN might control neural plasticity in the song control system (SCS) of songbirds. To further elucidate this relationship, we quantified PNN expression and their localization around parvalbumin interneurons at key time-points during ontogeny in both male and female zebra finches and correlated these data with the well-described development of song in this species. We also extended these analyses to the auditory system. The development of PNN during ontogeny correlated with song crystallization although the timing of PNN appearance in the four main telencephalic song control nuclei slightly varied between nuclei in agreement with the established role these nuclei play during song learning. Our data also indicate that very few PNN develop in the secondary auditory forebrain areas even in adult birds, which may allow constant adaptation to a changing acoustic environment by allowing synaptic reorganization during adulthood.
Data from: Timing of perineuronal nets development in the zebra finch song control system correlates with developmental song learning
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Data from: Hatching late in the season requires flexibility in the timing of song learning
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Developmental shifts in gene expression in the auditory forebrain during the sensitive period for song learning
GEO Series GSE36657. Taeniopygia guttata. 24 samples. Type: Expression profiling by array.
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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.
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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.