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201 results for “singing”
Data to: Aerosol emission is increased in professional singing
<p>This dataset contains raw data of emitted aerosols measured via a laser particle counter. Further, R-code for statistical analyses is available. The pre-print of an article based on these data was deposited here:</p> <p>https://depositonce.tu-berlin.de/handle/11303/11491</p>
N20EMv2 dataset for automatic music transcription from multimodal singing
<p>N20EMv2 dataset for multimodal automatic music transcription from multimodal singing, presented in our TOMM 2024 paper, Automatic Lyric Transcription and Automatic Music Transcription from Multimodal Singing. This dataset contains recordings of two modalities: audio and video. </p> <p>Our paper is available at: https://dl.acm.org/doi/10.1145/3651310.</p> <p>Code is available at: https://github.com/guxm2021/SVT_SpeechBrain</p> <p>Please cite our work as:</p> <pre>@article{gu2024automatic, title={Automatic Lyric Transcription and Automatic Music Transcription from Multimodal Singing}, author={Gu, Xiangming and Ou, Longshen and Zeng, Wei and Zhang, Jianan and Wong, Nicholas and Wang, Ye}, journal={ACM Transactions on Multimedia Computing, Communications and Applications}, publisher={ACM New York, NY}, year={2024} }</pre>
Jingju a cappella singing dataset part1
<p>This is the 4th version of the dataset. The folder structure has been changed since the 2nd version, where the Laosheng folder has been moved directly into wav or textgrid folder.</p> <p><strong>Description:</strong></p> <p>This dataset is a collection of boundary annotations of a cappella singing performed by Beijing Opera (Jingju, 京剧) professional and amateur singers. </p> <ol> <li>wav.zip: audio files in .wav format, mono or stereo.</li> <li>wav_mono.zip: audio files in .wav format, mono</li> <li>pycode.zip: util code for parsing the .textgrid annotation</li> <li>catalogue*.csv: recording metadata, source separation recordings are not included.</li> <li>textgrid.zip: phrase, syllable and phoneme annotation in Praat .textgrid format</li> <li>annotation_txt.zip: phrase, syllable and phoneme time boundaries (second) and labels in .txt format <ol> <li>*phrase_char: phrase-level time boundaries, labeled in Mandarin characters</li> <li>*phrase: phrase-level time boundaries, labeled in Mandarin pinyin</li> <li>*syllable: syllable-level time boundaries, labeled in Mandarin pinyin</li> <li>*phoneme: phoneme-level time boundaries, labeled in X-SAMPA</li> </ol> </li> </ol> <p>The boundaries (onset and offset) have been annotated in both <strong>Praat TextGrid (textgrid.zip)</strong> and .<strong>txt (annotation_txt.zip)</strong> format hierarchically:</p> <ol> <li>phrase (line),</li> <li>syllable,</li> <li>phoneme</li> </ol> <p>Singing units in pinyin and X-SAMPA have been annotated to a jingju a cappella singing audio dataset.</p> <p>The corresponding audio files are the a cappella singing arias recordings, which are stereo or mono, sampled at 44.1 kHz, and stored as .wav files. The .wav files are recorded by two institutes: those file names ending with ‘qm’ are recorded by C4DM, Queen Mary University of London; others file names ending with ‘upf’ or ‘lon’ are recorded by MTG-UPF. Additionally, another collection of 15 clean singing recordings is included in this dataset. They are extracted from the commercial recordings which originally contains karaoke accompaniment and mixed versions.</p> <p><strong>If you use this audio dataset in your work, please cite (1) this dataset as well (2) the following publication:</strong></p> <blockquote> <p>D. A. A. Black, M. Li, and M. Tian, “Automatic Identification of Emotional Cues in Chinese Opera Singing,” in 13th Int. Conf. on Music Perception and Cognition (ICMPC-2014), 2014, pp. 250–255.</p> </blockquote> <p> </p> <p><strong>Details:</strong><br> Annotation format, units, parsing code and other information please refer to <a href="https://github.com/MTG/jingjuPhonemeAnnotation">https://github.com/MTG/jingjuPhonemeAnnotation</a></p> <p><br> <strong>License:</strong><br> Textgrid annotations are licensed under Creative Commons Attribution-NonCommercial 4.0 International License.</p> <p>Wav audio ending with ‘upf’ or ‘lon’ is licensed under Creative Commons Attribution-NonCommercial 4.0 International.</p> <p>For the license of .wav audio ending with ‘qm’ from C4DM Queen Mary University of London, please refer to this page <a href="http://isophonics.org/SingingVoiceDataset">http://isophonics.org/SingingVoiceDataset</a></p> <p><strong>Contact information:</strong></p> <p>Rong Gong: rong<dot>gong<at>upf<dot>edu</p> <p>Rafael Caro Repetto: rafael<dot>caro<at>upf<dot>edu</p>
Jamendo Corpus for Singing Voice Detection
<p>This is a public corpus of 93 creative-commons licensed music pieces annotated<br> by voice (sung or spoken) and no-voice.</p>
Margareth Menezes sings "No Woman, No Cry" and "Faraó" with Olodum at Casa do Olodum on November 8, 2022
<p>Margareth Menezes, who originally sang "Faraó, Divinidade do Egíto" (Pharoah, Divinity of Egypt) in Olodum's 1987 Carnival, sings it with Olodum at the Casa do Olodum on November 8, 2022. She starts by singing "No Woman, No Cry" by Bob Marley <em>(Natty Dread</em> 1974), which is featured in the second Black Panther Movie, Wakanda Forever, which released on November 10, 2022. </p>
Jingju a cappella singing syllable boundary and duration annotation dataset
<p>This dataset is a collection of syllable boundary annotations and syllable duration annotations of a cappella singing performed by jingju (京剧, Beijing opera) professional and amateur singers. This dataset was used as the experimental dataset in the following work:</p> <blockquote> <p>Rong Gong, Nicolas Obin, Georgi Dzhambazov and Xavier Serra, “Score-Informed syllable segmentation for jingju a cappella singing voice with Mel-frequency intensity profiles," in<em> Folk Music Analysis workshop (FMA) 2017, Málaga, Spain</em></p> </blockquote> <p><strong>Audio Content</strong></p> <p>The audio files are the a cappella singing arias recordings, which are stereo or mono, sampled at 44.1 kHz, and stored as wav files. They can be found at this link http://doi.org/10.5281/zenodo.344932</p> <p>The wav files are recorded by two institutes: those file names ending with ‘qm’ are recorded by C4DM Queen Mary University of London; others file names ending with ‘upf’ or ‘lon’ are recorded by MTG-UPF. If you use the dataset in your work, please cite the following publication.</p> <blockquote> <p>D. A. A. Black, M. Li, and M. Tian, “Automatic Identification of Emotional Cues in Chinese Opera Singing,” in <em>13th Int. Conf. on Music </em><em>Perception and Cognition</em> (ICMPC-2014), 2014, pp. 250–255.</p> </blockquote> <p><strong>Annotations</strong></p> <p>The syllable boundary annotation is in Textgrid format (Praat). The annotation is done in both phrase-level and syllable-level. The syllable duration annotation is in cvs format. Please consult Readme text in both folders for further details. The parsing code of the annotation files is provided in ‘pycode’ folder. </p> <p><strong>Availability of the Dataset</strong></p> <p>The annotations and codes in this dataset are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.</p> <p><strong>Contact</strong></p> <p>If you have any questions or comments about the dataset, please feel free to write to us.</p> <p>Rong Gong: rong<dot>gong<at>upf<dot>edu</p> <p>Rafael Caro Repetto: rafael<dot>caro<at>upf<dot>edu</p>
Jingju a cappella singing pitch contour segmentation ground truth dataset
<p>The dataset used in the paper:</p> <blockquote> <p>Gong, Rong; Yang, Yile; Serra, Xavier; Pitch Contour Segmentation for Computer-aided Jingju Singing Training Sound and Music Computing (SMC 2016), 2016, Hamburg, Germany</p> </blockquote> <p>is in "dataset" folder. The a cappella singing audio recordings are not contained in this folder due to their large size, please contact the paper authors to request them (rong.gong@upf.edu). In the "dataset" folder you can find:</p> <ol> <li>ground truth</li> <li>Jinging singing scores in .xml format used for estimating the bigram note transition probabilities.</li> </ol> <p>The ground truth annotation is used for:</p> <ul> <li>melodic transcription (male_12_pos_1 missing)</li> <li>parameter optimization,</li> <li>evaluating the StdCdLe thresholding and the overall segmentation performance.</li> </ul> <p>The subfolder "groundtruth" contains the following annotation for each jingju a cappella audio:</p> <ul> <li>file name: description (format)</li> <li>*_melodicTrans.csv: melodic transcription ground truth used for the evaluation (start_time pitch duration -).</li> <li>*_coarseSeg.csv: StdCdLe ground truth used for the parameter optimization and the evaluation (segmentation points).</li> <li>*_refinedSeg.csv: ground truth used for optimizing other parameters and the evaluation (start_time - duration).</li> <li>*_pitchtrack.csv: pitch track (contour) extracted by pYIN pitch-tracking algorithm (filename time pitch).</li> <li>*_monoNoteOut.csv: notes estimated by pYIN note-tracking algorithm (filename start_time duration pitch).</li> </ul> <p> </p> <p> </p>
FIG. 1 in When did roosters start singing at Arslantepe? A preliminary assessment of the presence and spread of Gallus gallus (Linnaeus, 1758) in Iron Age Eastern Anatolia
FIG. 1. — Map of Anatolia and the Levant with the main sites mentioned in the text (modified data courtesy of National Centers for Environmental Infor- mation – ETOPO1, Natural Earth and Geo Network opensource. https://doi. org/10.7289/V5C8276M).
FIG. 4 in When did roosters start singing at Arslantepe? A preliminary assessment of the presence and spread of Gallus gallus (Linnaeus, 1758) in Iron Age Eastern Anatolia
FIG. 4. — Arslantepe, tarsometatarsi (left and right) of rooster from level IIIB. Photo credits: R. Ceccacci, ©MAIAO. Scale bar: 3 cm.
University of Rochester Audio-Visual Solo Singing Performance (URSing) Dataset
<p>We introduce a dataset for facilitating audio-visual analysis of singing performances. The dataset comprises a number of songs where singers’ solo voices are recorded in isolation. For each song, we provide the high-quality audio recordings of the solo singing voice and mix with accompaniments, and the video recording of the upper body of the vocal soloist which contains facial expressions and lip movements. We anticipate that the dataset will be useful for developing audiovisual source separation systems. Note that some of the accompaniment tracks come with the backing vocals, which introduces extra challenges of developing an audio-based singing voice separation system, and encourages researchers to integrate the soloists’ visual information to facilitate the separation process. We also anticipate that the dataset will be useful for other multi-modal information retrieval techniques such as audiovisual expressions analysis, audio-visual correspondence, audiovisual lyrics transcription, etc.</p>
Electrobyte for Singing Voice Detection
<p>This is a public dataset of 90 copyright-free electronic songs with vocal annotations (sing/no sing).</p>
Annotated-VocalSet: A Singing Voice Dataset
<p>This dataset provides annotations for the <a href="https://doi.org/10.5281/zenodo.1442513">VocalSet dataset</a>, which is available online at</p> <pre><a href="https://doi.org/10.5281/zenodo.1442513">https://doi.org/10.5281/zenodo.1442513</a></pre> <p>.</p> <p>The annotations generated for the VocalSet audio files include fundamental frequency contour, note onset, note offset, the transition between notes, note F0, note duration, Midi pitch, and lyrics.</p> <p><a href="https://doi.org/10.5281/zenodo.1442513">VocalSet</a> consists of more than 10 hours of monophonic recorded audio of professional singers in a variety of vocal techniques (n = 17) and several singers (m = 20) with several WAV files (p = 3560). However, although several categories, including techniques, singers, tempo, and loudness, are considered in the dataset, the sung notes were not annotated. Therefore, this dataset aims to annotate VocalSet to make it a more powerful dataset for researchers.</p> <p>Details of the dataset are provided in the following academic journal paper.</p> <p><a href="https://www.mdpi.com/2076-3417/12/18/9257">Faghih, Behnam, and Joseph Timoney. 2022. "Annotated-VocalSet: A Singing Voice Dataset" <em>Applied Sciences</em> 12, no. 18: 9257. https://doi.org/10.3390/app12189257</a></p> <p>Please use the above paper to cite this dataset.</p>
Рис. 4. Коррелограммы покаЗателей обилиЯ наЗемного моллюска M. cartusiana раЗных воЗрастных групп (1 – ювенильные; 2 – вЗрослые; 3 – все вместе): A – участок № 1, 2010 г.; B – участок № 2, 2011 г.; C – участок № 4, 2012 г.); D – участок № 5, 2012 г. (достоверные оценки индекса Морана отмечены Залитыми Значками). Fig. 4. Spatial correlogram of land snail M. cartusiana age groups abundance (1 – juvenile; 2 – adult; 3 – total): A – site 1, 2010; B – site 2, 2011; C – site 4, 2012; D – site 5, 2012 (Moran index confidence value presented by filled sings). in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach
Рис. 4. Коррелограммы покаЗателей обилиЯ наЗемного моллюска M. cartusiana раЗных воЗрастных групп (1 – ювенильные; 2 – вЗрослые; 3 – все вместе): A – участок № 1, 2010 г.; B – участок № 2, 2011 г.; C – участок № 4, 2012 г.); D – участок № 5, 2012 г. (достоверные оценки индекса Морана отмечены Залитыми Значками). Fig. 4. Spatial correlogram of land snail M. cartusiana age groups abundance (1 – juvenile; 2 – adult; 3 – total): A – site 1, 2010; B – site 2, 2011; C – site 4, 2012; D – site 5, 2012 (Moran index confidence value presented by filled sings).
Dataset: The Singing Machine Company, Inc. (MICS) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Jingju a cappella singing dataset part2
<p>这个京剧清唱数据库包含有120个唱段、1265个唱句。此数据库是CompMusic项目所有数据库的一个组成部分(http://compmusic.upf.edu/corpora, http://compmusic.upf.edu/datasets)。CompMusic前期所使用的另一个京剧数据库可以在这里找到(https://doi.org/10.5281/zenodo.344932)。我们邀请了专业和业余的京剧演员参与到录音过程当中,大部分的京剧音乐元素都被囊括在了这个数据库中。此外,它还包含有每个唱段和每个唱句的元数据,以供自动演唱评价的研究使用。</p> <p>This is a jingju (also known as Beijing or Peking opera) a cappella singing audio dataset which consists of 120 arias, accounting for 1265 melodic lines. This dataset is also an extension our existing CompMusic jingju corpora (http://compmusic.upf.edu/corpora) and dataset (http://compmusic.upf.edu/datasets), for example, Jingju a cappella singing dataset part1 (https://doi.org/10.5281/zenodo.344932). Both professional and amateur singers were invited to the dataset recording sessions, and the most common jingju musical elements have been covered. This dataset is also accompanied by metadata per aria and melodic line annotated for automatic singing evaluation research purpose.</p> <p> </p> <p><strong>文件 Files:</strong></p> <ol> <li>wav.zip: audio files in .wav format, mono</li> <li>metadata.zip: aria and line level metadata</li> <li>annotation_version2.zip: line and syllable time boundaries and labels annotations, in Praat .textgrid format</li> <li>annotation_txt.zip: line and syllable time boundaries and labels annotations, in .txt format <ol> <li>*phrase_char: phrase-level time boundaries, labeled in Mandarin characters</li> <li>*phrase: phrase-level time boundaries, labeled in Mandarin pinyin</li> <li>*syllable: syllable-level time boundaries, labeled in Mandarin pinyin</li> </ol> </li> </ol> <p> </p> <p><strong>艺术家 Artists:</strong></p> <p>我们邀请了5位专业的京剧演员(中国戏曲学院,他们都有丰富的舞台表演和教学经验)和4位非艺术类高校京剧社团的业余京剧演员。</p> <p>We invited 5 professional singers from NACTA (National Academy of Chinese Theatre Arts, all of them have rich experience in stage performance and teaching) and another 4 amateur singers from jingju associations in non-art schools to the recording sessions. </p> <p> </p> <p><strong>伴奏 Accompaniment:</strong></p> <p>7位演员(3位专业和4位业余)跟随商业录音伴奏;另外2位专业演员由专业京胡乐手伴奏(中国戏曲学院)。</p> <p>7 singers (3 professional and 4 amateurs) were singing along with the accompaniment of commercial audio recordings; other 2 professional singers were accompanied by 2 professional <em>jinghu</em> players (NACTA).</p> <p><strong>数据库的覆盖性,完整性,质量和重复利用性 Coverage, completeness, </strong><strong>quality</strong><strong> and reusability:</strong></p> <ol> <li><em><strong>覆盖性: </strong></em>数据库包含三个主要的京剧行当 - 老生、旦和净;两个主要声腔 - 西皮和二黄,和一些附属声腔,比如四平调、南梆子;包含所有的有节拍的板式 - 原版、慢板、快板、二六、流水、三眼和他们的变化板式。<strong><em>Coverage</em></strong>: The dataset includes the three main role-types -<em> </em><em>laosheng</em><em>, dan</em> and<em> jing</em>; two main <em>shengqiang</em> - <em>xipi</em> and <em>erhuang</em>, and a few auxiliary ones, such as <em>sipingdiao</em><em>, </em><em>nanbangzi</em><em>;</em> the whole range of metered <em>banshi</em> - <em>yuanban</em><em>, </em><em>manban</em><em>, </em><em>kuaiban</em><em>, </em><em>erliu</em><em>, </em><em>liushui</em><em>, </em><em>sanyan</em> and its three variations.</li> <li><em><strong>完整性: </strong></em>数据库包含有录音和唱句层级的元数据,由Excel spreadsheet格式保存。对于录音层级,元数据包括唱段名、行当、声腔、板式、是否由京胡伴奏。对于唱句层级,每一句都包含行当、声腔、板式、上下句、唱词和所匹配的MusicXML曲谱(有需要曲谱请联系作者)。<strong><em>Completeness</em></strong>: The dataset contains the metadata of the recordings and annotations both at the recording and the line level, organized in separate spreadsheets. For the recordings, the metadata contains the title of the work in Chinese, role-type, <em>shengqiang</em><em>, </em><em>banshi</em>, whether it contains jinghu accompaniment. As for the lines, each of them is annotated with the role-type, <em>shengqiang</em><em>, </em><em>banshi</em><em>,</em> line type, that is, opening or closing, the lyrics for the whole line and the related score in the score collection (available on request).</li> <li><em><strong>质量: </strong></em>一小部分的录音带有中等程度的房间混响和轻微的背景噪声。其余的录音质量都很好。<strong><em>Quality</em></strong>: A small number of the recordings contain medium room reverberation and minor background noise. However, apart from those, the other recordings are dry, clean and of good quality.</li> <li><em><strong>重复利用性: </strong></em>所有数据库音频和元数据都由Creative Commons Attribution-NonCommercial 4.0 International方式授权。<strong><em>Reusability</em></strong>: All the audio and metadata files in this dataset are licensed under Creative Commons Attribution-NonCommercial 4.0 International.</li> </ol> <p> </p> <p><strong>标注 Annotation:</strong></p> <p>数据库包含一部分录音的唱句起始位置和音节起始位置标注,标注格式为Praat TextGrid。唱句标注包含有每一唱句的歌词,此歌词从曲谱提取,并不与实际演唱一致;音节标注包含拼音,经过作者修正,试图与演唱发音一致。标注的统计如下:</p> <ul> <li>老生唱句数量,音节数量,音节平均时长 (秒),音节时长标准差 (秒): 405, 3941, 1.32, 2.15</li> <li>旦唱句数量, 音节数量, 音节平均时长 (秒), 音节时长标准差 (秒): 467, 4394, 1.63, 3.25</li> <li>总体唱句数量, 音节数量, 音节平均时长 (秒), 音节时长标准差 (秒): 872, 8335, 1.48, 2.79</li> </ul> <p>The dataset contains the line and syllable boundary annotation for a part of recordings, in Praat TextGrid format. The line annotation contains the lyrics for each line, which is extracted from the score, and might not coherent with the actual singing; the syllable annotation contains pinyin, corrected by the author to be coherent with the actual singing. The statistics of the annotation are:</p> <ul> <li><em>laosheng</em> num. of lines, num. of syllables, average syllable duration (s), standard deviation (s): 405, 3941, 1.32, 2.15</li> <li><em>dan</em> num. of lines, num. of syllables, average syllable duration (s), standard deviation (s): 467, 4394, 1.63, 3.25</li> <li>Overall num. of lines, num. of lines, num. of syllables, average syllable duration (s), standard deviation (s): 872, 8335, 1.48, 2.79</li> </ul> <p> </p> <p><strong>引用 Citation:</strong></p> <p>如需更多信息,请参考下面论文;如果您在工作中使用该数据库,请引用下面论文:</p> <p>For more information, please refer the following publication and If you use this dataset in your work, please cite the following publication:</p> <blockquote> <p>Rong Gong, Rafael Caro Repetto, Xavier Serra, “Creating an A Cappella Singing Audio Dataset for Automatic Jingju Singing Evaluation Research,” in 4th International Digital Libraries for Musicology workshop (DLfM 2017), Shanghai, China.</p> </blockquote> <p> </p> <p><strong>协议 License:</strong></p> <p><a href="https://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial 4.0</a></p> <p> </p> <p><strong>联系方式 Contact information</strong>:</p> <p><em>如果任何问题,请联系作者 </em><em>If you have any question, please contact the authors:</em></p> <p>龚嵘 Rong Gong: Email - rong<dot>gong<at>upf<dot>edu, Wechat id - gongr86</p> <p>贵云飞 Rafael Caro Repetto: Email - rafael<dot>caro<at>upf<dot>edu </p> <p> </p> <p><em>如果您想联系京剧演员 If you want to contact the jingju</em><em> singers:</em></p> <p>廖佳尼 Jiani Liao: Wechat id - v1307624197</p> <p>邵雅昆 Yakun Shao: Wechat id - S_yakun-</p> <p> </p> <p><em>或京胡乐手 Or jinghu</em><em> players:</em></p> <p>张蓝天 Lantian Zhang: Wechat id - tian576632395</p>
VocalSet: A Singing Voice Dataset
<p><strong>NEW IN VocalSet 1.2:</strong> We now have 3 file organization versions:</p> <ol> <li>Files organized by singer</li> <li>Files organized by technique</li> <li>Files organized by vowel</li> </ol> <p>We hope that this will ease the process of training and testing models using these different attributes of the dataset.</p> <p> </p> <p><strong>Overview:</strong></p> <p>We present VocalSet, a singing voice dataset consisting of 10.1 hours of monophonic recorded audio of professional singers demonstrating both standard and extended vocal techniques on all 5 vowels. Existing singing voice datasets aim to capture a focused subset of singing voice characteristics, and generally consist of just a few singers. VocalSet contains recordings from 20 different singers (9 male, 11 female) and a range of voice types. VocalSet aims to improve the state of existing singing voice datasets and singing voice research by capturing not only a range of vowels, but also a diverse set of voices on many different vocal techniques, sung in contexts of scales, arpeggios, long tones, and excerpts.</p> <p>We have included two .txt files 'train_singers_technique.txt 'and 'test_singers_technique.txt' in which you will find a list of the singers we used to train and test our technique classifier on. 'DataSetVocalises.pdf' contains the sheet singers sang from in their recording sessions. 'readme-anon.txt' contains more information about the dataset, including the mapping from filename to singer voice type as well as more information on the vocalises that will help you map files to sheet music. Enjoy and please cite accordingly!</p>
Data for: Atypical singing is associated with developmental stress and zero fitness in a male white-throated sparrow (Zonotrichia albicollis)
<p>Here we provide data for a manuscript in which we describe the atypical song of a male white-throated sparrow (<em>Zonotrichia albicollis</em>). We observed this male over multiple breeding seasons at our Cranberry Lake study site (Adirondack Mountains; New York; 44.15N, 74.78W). We recorded the male singing, and also made observations regarding his failure to obtain reproductive success. In addition, as the male was banded as a nestling, we were able to compare his morphometric measurements at the time to the population average. Our observations of this unique individual support a connection between developmental stress, atypical song, and fitness outcomes.</p>
MedleyVox: An Evaluation Dataset for Multiple Singing Voices Separation
<p>MedleyVox is an evaluation dataset for multiple singing voices separation, which consists of 381 segments (1.1hour), containing 23 songs from MedleyDB v1 and v2 (https://medleydb.weebly.com).</p> <p> </p> <p>MedleyVox contains 1) unison, 2) duet, 3) main vs. rest (folder name 'rest') , and 4) N-singing ('unison' + 'duet' + 'rest', please check our dataloader code for N-singing in https://github.com/jeonchangbin49/MedleyVox/blob/main/svs/data/test_dataset.py) categories explained in our ICASSP 2023 paper. For more details, please check our paper (https://arxiv.org/pdf/2211.07302.pdf) and code repository (https://github.com/jeonchangbin49/MedleyVox).</p> <p> </p> <p>Acknowledgment</p> <p>We are grateful to Rachel Bittner, the author of the original MedleyDB data, for allowing us to publish the MedleyVox dataset.</p> <p> </p> <p>License</p> <p>This work is licensed under a <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.</p>
The American Woodcock Singing Ground Survey largely conforms to the phenology of male woodcock migration
<p><strong>ABSTRACT </strong>American woodcock (<em>Scolopax minor; </em>hereafter woodcock) are monitored, in part, by counts of displaying male woodcock collected via the American Woodcock Singing Ground Survey (SGS), which suggests long-term, range-wide declines in woodcock populations. Data from the SGS have been used extensively to develop conservation plans, direct management actions, and understand causes of decline. To avoid bias, the SGS should be timed to avoid spring migration, and the distribution of survey routes should coincide with woodcock breeding distribution. We marked 133 male woodcock captured throughout eastern North America with GPS transmitters during 2019–2022, and compared the timing of their spring migration with the spatiotemporal stratification of the SGS. Most woodcock (74 %) completed migration prior to the onset of the SGS. In the northern-most SGS zone a greater percentage of males (34 %) continued migration during the survey window, however the influence of this mismatch is offset because SGS routes were run more frequently during the second half of the window. Young woodcock completing their first spring migration took 8.6 days longer to do so, on average, compared to adults, and so were more likely to migrate during the SGS window. We found little evidence that timing of migration varied among years. Existing SGS routes cover the majority of male woodcock post-migratory breeding distribution, with 90% of male woodcock establishing breeding sites within the spatial coverage of the SGS. Our results confirm the SGS includes some migrant males, with the proportion relative to resident breeding males increasing in more northern survey strata. Our data suggests these errors are unlikely to bias trend estimates at large scales (e.g., within woodcock management regions), but there may be potential for bias at more local scales (e.g., state or provincial population indices). </p>
"I am a journalist myself, working for a public radio and television in the Netherlands. As a radio reporter Ivisited Bangladesh just after the cyclone Sidr hit the coastal area in November 1997 (…) Itravelled to the islands on a boat. On that boat were two boatmen and one of them started singing while we were sailing. As Igeotagged this song you can see exactly where it was. Iwas staying at that time in Pirojpur, took a taxi to the river and got a boat. Along tall typical motorboat. It was a journey of three-quarters of an hour during which he sang two songs." [Jeroen/zeshoog]12 in Collecting Sounds. Online Sharing of Field Recordings as Cultural Practice
"I am a journalist myself, working for a public radio and television in the Netherlands. As a radio reporter Ivisited Bangladesh just after the cyclone Sidr hit the coastal area in November 1997 (…) Itravelled to the islands on a boat. On that boat were two boatmen and one of them started singing while we were sailing. As Igeotagged this song you can see exactly where it was. Iwas staying at that time in Pirojpur, took a taxi to the river and got a boat. Along tall typical motorboat. It was a journey of three-quarters of an hour during which he sang two songs." [Jeroen/zeshoog]12
ScienceDex guides
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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.