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503 results for “Voice”
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>
CRPIH_UVigo-GL-Voices: Galician TTS dataset
<p><em>CRPIH_UVigo-GL-Voices</em> is a Galician TTS multi-speaker dataset containing audio recordings from four different speakers (two female and two male voices). The characteristics of each voice are detailed in the table below:</p> <table> <tbody> <tr> <td><strong>Voice Name</strong></td> <td><strong>Gender</strong></td> <td><strong>Speaker</strong></td> <td><strong>Recording</strong></td> <td><strong># Utts</strong></td> <td><strong>Duration</strong></td> <td><strong>Sampling Rate</strong></td> <td><strong>Format</strong></td> </tr> <tr> <td>Sabela</td> <td>Female</td> <td>Professional radio broadcaster</td> <td>Professional studio</td> <td>9,999</td> <td>14h 28m</td> <td>16kHz/ 44kHz</td> <td>16-bit PCM</td> </tr> <tr> <td>Icía</td> <td>Female</td> <td>Amateur</td> <td>Semi-professional studio</td> <td>2,950</td> <td>4h 5m</td> <td>16kHz/ 96kHz</td> <td>16/24-bit PCM</td> </tr> <tr> <td>Iago</td> <td>Male</td> <td>Amateur</td> <td>Radio studio</td> <td>1,316</td> <td>1h 13m</td> <td>16kHz/ 48kHz</td> <td>16-bit PCM</td> </tr> <tr> <td>Paulo</td> <td>Male</td> <td>Amateur</td> <td>Radio studio</td> <td>1,316</td> <td>1h 15m</td> <td>16kHz</td> <td>16-bit PCM</td> </tr> </tbody> </table> <p> </p> <p>Each speaker recorded a subset of utterances from a text corpus of 10,000 sentences, with a length between 1 and 44 words. This corpus is mainly composed of press excerpts, but it also contains a small subset of manually designed sentences. The press excerpts were extracted from newspapers published before 2010 ("O Correo Galego", "Galicia Hoxe" and "Vieiros"), whereas the hand-crafted sentences were created at the CRPIH in the year 1999.</p> <p>The data is organized into folders with each folder corresponding to one of the speakers. Each speaker's folder is composed of the following subdirectories:</p> <ul> <li><strong>txt</strong> → Audio transcripts enconded in ISO 8859-1.</li> <li><strong>fon</strong> → Phoneme-level forced alignment between phonemic transcriptions (provided by Cotovía) and audio recordings.</li> <li><strong>wav_[bit depth]bits_[sampling rate]kHz</strong> → WAV format audio files at [sampling-rate]kHz [bit-depth]-bit (see table above).</li> </ul> <p>The file naming convention is as follows: two lowercase elements indicating the creators of the dataset (“<em>crpih_uvigo</em>”), the ISO code for the Galician language (“<em>gl</em>”), the name of the voice (e. g., “<em>sabela</em>”), and a 5-digit number identifying the utterance. All components are separated by underscores (e. g., “<em>crpih_uvigo_gl_sabela_00001.txt</em>”). For some of the speakers, there is an additional element for identifying dataset's splits (e. g., “<em>crpih_uvigo_gl_iago_a_00001.txt</em>” and “<em>crpih_uvigo_gl_iago_b_00001.txt</em>”).</p> <p><strong>Acknowledgements</strong></p> <p>We would like to thank the speakers for recording and donating their voices.</p>
Source code: Bilateral human laryngeal motor cortex in perceptual decision of lexical tone and voicing of consonant
<p>Source code (and data) for the paper <em>Bilateral Human Laryngeal Motor Cortex in Perceptual Decision of Lexical Tone and Voicing of Consonant</em>.</p> <p><a href="https://zenodo.org/api/files/d8e78f58-333f-45fb-954c-4e3a343fa5a1/Exp1_datacollection.zip">Exp1_datacollection.zip</a>:code for data collection in Experiment 1.</p> <p><a href="https://zenodo.org/api/files/d8e78f58-333f-45fb-954c-4e3a343fa5a1/Exp2_datacollection.zip">Exp2_datacollection.zip</a>: code for data collection in Experiment 2.</p> <p><a href="https://zenodo.org/api/files/d8e78f58-333f-45fb-954c-4e3a343fa5a1/codes_for_dataprocess.zip">codes_for_dataprocess.zip</a>:data processing code and source data.</p> <p>Exp1 and Exp2 collection codes are provided for reference, but for practice, due to copyright and software environment issues, please contact Baishen (liangbs@psych.ac.cn, liangbs95@gmail.com) for technical assistant. </p>
Sex-related communicative functions of voice spectral energy in human chorusing
<p>Music is a human communicative art whose evolutionary origins may lie in capacities that support cooperation and/or competition. A mixed account favoring simultaneous cooperation and competition draws on analogous interactive displays produced by collectively signalling non-human animals (e.g., crickets and frogs). In these displays, rhythmically coordinated calls serve as a beacon whereby groups of males "cooperatively" attract potential female mates, while the likelihood of each male competitively attracting an actual mate depends on the precedence of his signal. Human behaviour consistent with the mixed account was previously observed in a renowned boys choir, where the basses—the oldest boys with the deepest voices—boosted their acoustic prominence by increasing energy in a high-frequency band of the vocal spectrum when girls were in an otherwise male audience. The current study tested female and male sensitivity and preferences for this subtle vocal modulation in online listening tasks. Results indicate that while female and male listeners are similarly sensitive to enhanced high-spectral energy elicited by the presence of female audience members, only female listeners exhibit a reliable preference for it. Findings suggest that human chorusing is a flexible form of social communicative behavior that allows simultaneous group cohesion and sexually motivated competition.</p>
Data for: An instantaneous voice synthesis neuroprosthesis
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Sex-related communicative functions of voice spectral energy in human chorusing
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See no evil in the voice-to-voice customer service context
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Voices for the Martuwarra
<p>Voices for the Martuwarra (40 minutes) is a collaborative documentary featuring interviews with members of the Martuwarra Fitzroy River (MFR) Council, scientists, environmentalists, and researchers. Through breathtaking visuals of the Martuwarra, Fitzroy River, and engaging interviews with multiple stakeholders, the film traces the creation of the MFR Council, which was established in 2018 to give a political voice to Traditional Owners of the Fitzroy River Region amidst extensive development plans earmarked for the Kimberley in the mining and agricultural sectors. Since its inception the <a href="https://www.martuwarrafitzroyriver.org">MFR Council</a> has attracted the attention of leading scientists and researchers, both in Australia and internationally, through its ability to bring together the majority of Traditional Owner groups from the region, and spearheading collaborative, cutting-edge research into various domains: First Law, culture, science, history, economy and development. </p> <p>The MFR Council believes rivers are the lifeblood of our Nation, and is focused on protecting their sacred National Heritage Listed Fitzroy River’s Right To Life from proposed invasive developments. Giving voice to many Kimberley Traditional Owners, the film depicts the profound and complex inter-relatedness between culture, Country, Living Waters and people. As a call for action, it also reminds audiences of the reasons why, in the age of the Anthropocene and Climate Emergency, it is more important than ever to listen to Indigenous voices, as they hold solutions for the future of humanity and of our planet.</p>
Model-By-Voice New Interaction Model - Video Demo
<p>Demo of the main features added to the new interaction model of the platform Model-By-Voice, including:<br> <br> - Modeling through non-vocal sounds</p> <p>- Modeling through drawn gestures</p> <p>- Modeling through a table navigation paradigm</p> <p>- Addition of shortcuts such as "copy"</p> <p>- Customization of the voice synthesizer</p> <p>- Notion of "User" and "profile" where you can save your preferences, gestures, non-vocal-sounds and patterns</p> <p>Features not covered in the demo:<br> <br> - Navigating on the table through keyboard arrows</p> <p>- Designing the interaction protocol that adjust the platform to the level of knowledge in modeling engineering and visual difficulties.</p> <p>- Other shortcuts such as "use pattern" and "cut"</p> <p> </p> <p>The demo represents the usage of the platform by a first time user.</p>
Mixe-Zoquean Voices
<p>Mixe-Zoquean primary recordings provided by Ana Kondic</p>
SVDD Challenge 2024: A Singing Voice Deepfake Detection Challenge (WildSVDD Track)
<p>For more information about SVDD Challenge 2024, please refer to https://challenge.singfake.org/.<br><br>WildSVDD track dataset is an extension of <a href="https://singfake.org/">SingFake</a> dataset. </p>
Data for: Connecting voice profiling to genomics
<p>This dataset contains a graph-based path-finding algorithm to detect chainlink genes for voice chains that connect the genes in chromosomal microdeletion regions to the FOXP2 gene. Instructions are within multiple README files included with the package. The code is set up to be used on Linux platforms.</p>
Voice of the volunteers
<p>The ‘Voice of the Volunteers’ workshop was designed to hear directly from volunteers who had taken part in online cultural heritage citizen research projects. We wanted to hear about their motivations, experience and preferences. We also used the workshop as an opportunity to elicit feedback on the newly launched indexing tool, which allows volunteers taking part in Zooniverse projects to choose their own pathway through a set of records.</p> <p>This workshop was part of a series of workshops within the project<a href="https://tanc-ahrc.github.io/EngagingCrowds/index.html"> </a><a href="https://tanc-ahrc.github.io/EngagingCrowds/index.html"><em>Engaging Crowds: citizen research and heritage data at scale</em></a>, a collaboration between<a href="https://www.nationalarchives.gov.uk/"> The National Archives</a>,<a href="https://www.rbge.org.uk/"> the Royal Botanic Garden Edinburgh</a>,<a href="https://www.rmg.co.uk/national-maritime-museum"> Royal Museums Greenwich</a> and<a href="https://www.zooniverse.org/"> Zooniverse at the University of Oxford</a>. <em>Engaging Crowds</em> is a Foundation Project within the Arts and Humanities Research Council-funded<a href="https://www.nationalcollection.org.uk/"> Towards a National Collection programme</a>.</p>
Modeling of Speech-dependent Own Voice Transfer Characteristics for Hearables with In-ear Microphones: Audio Examples
<p>This upload contains audio examples for the preprint "Modeling of Speech-dependent Own Voice Transfer Characteristics for Hearables with In-ear Microphones".</p> <p>The audio files correspond to subplots of the spectrogram shown in the default preview, starting from the upper left corner (subplot 0) to the upper right corner (subplot 1) and so on.</p> <h2>Abstract</h2> <p>Many hearables contain an in-ear microphone, which may be used to capture the own voice of its user. However, due to the hearable occluding the ear canal, the in-ear microphone mostly records body-conducted speech, typically suffering from band-limitation effects and amplification at low frequencies. Since the occlusion effect is determined by the ratio between the air-conducted and body-conducted components of own voice, the own voice transfer characteristics between the outer face of the hearable and the in-ear microphone depend on the speech content and the individual talker. In this paper, we propose a speech-dependent model of the own voice transfer characteristics based on phoneme recognition, assuming a linear time-invariant relative transfer function for each phoneme. We consider both individual models as well as models averaged over several talkers. Experimental results based on recordings with a prototype hearable show that the proposed speech-dependent model enables to simulate in-ear signals more accurately than a speech-independent model in terms of technical measures, especially under utterance mismatch and talker mismatch. Additionally, simulation results show that talker-averaged models generalize better to different talkers than individual models.</p> <p> </p> <p>The examples are also available here: <a href="https://m-ohlenbusch.github.io/own_voice_modeling_examples/" target="_blank" rel="noopener">https://m-ohlenbusch.github.io/own_voice_modeling_examples/</a></p> <p>Arxiv preprint: <a href="https://arxiv.org/abs/2310.06554">https://arxiv.org/abs/2310.06554</a></p>
DATA: Nonbinary Voices for Digital Assistants – An Investigation of User Perceptions and Gender Stereotypes
<p>This dataset (n=318) investigates peoples' perceptions of gender stereotypes in nonbinary digital voices.</p>
CUCO Database: A voice and speech corpus of patients who underwent upper airway surgery in pre- and post-operative states
<p>The data set comprises 3,800 speech audio files of 3 types of upper respiratory tract surgeries and 1 control set. The dataset has an average of <strong>35.51 +- 5.91</strong> audio recordings per patient. It provides valuable resources to the scientific community to systematically investigate the objective effects of upper respiratory tract surgery on voice and speech. </p> <p>This data set is a complete corpus comprising data from 107 Spanish Castilian speakers. This corpus encompasses voice and speech recordings from both control speakers and patients who underwent upper airway surgical procedures in pre- and post-operative stages. The surgeries in focus include <strong>Tonsillectomy</strong>, <strong>Functional Endoscopic Sinus Surgery</strong>, and <strong>Septoplasty</strong>, all consistently performed by a single surgeon.</p> <p>This corpus has been the basis for different previous studies to evaluate changes in voice and its quality due to surgery. The results do not suggest significant changes in the most relevant acoustic parameters studied for the voice, which is consistent with the initial hypothesis. However, the analysis of speech recordings remains open, with a special focus on the nasalised segments, which are expected to change due to surgical intervention.</p> <p>This data set also opens the way to study the effect of upper airway surgery on the performance of speaker recognition and identification methods, as well as to be used to test anti-spoofing methodologies to make them more robust. </p> <p>Please, if you use this database, cite this open-access paper where the data acquisition is explained and detailed:</p> <p>Hernández-García, E., Guerrero-López, A., Arias-Londoño, J. D., & Godino-Llorente, J. I. (2024). A voice and speech corpus of patients who underwent upper airway surgery in pre-and post-operative states. <em>Scientific Data</em>, <em>11</em>(1), 746.</p>
Common Voice Corpus 1
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Dataset for Interspeech 2018 submission: Singing voice phoneme segmentation by hierarchically inferring syllable and phoneme onset positions
<p>This dataset contains the materials for training, testing the joint and HSMM models mentioned in the paper "<em>Singing voice phoneme segmentation by hierarchically inferring syllable and phoneme onset positions"</em>.</p> <p>The filename list of this dataset can be found in the function <em>get_train_test_recordings_joint()</em> of <em>./general/trainTestSeparation.py</em> file. The dataset contains the Praat TextGrids and .wavs of the variables: <em>train_primary_school, val_primary_school</em> and <em>test_primary_school</em>. For accessing other datasets such as <em>train_nacta_2017, train_nacta</em> and <em>train_sepa</em>, please download them from the links:</p> <p>jingju dataset part1: <a href="https://zenodo.org/record/1185154">https://zenodo.org/record/1185154</a></p> <p>jingju dataset part2: <a href="https://doi.org/10.5281/zenodo.842229">https://doi.org/10.5281/zenodo.842229</a></p> <p>Once you have downloaded these three datasets, you need to set the paths in <em>./general/filePathShared.py</em>.</p> <p>Set <em>path_jingju_dataset</em> to the parent path of these three datasets.</p> <p>Set <em>primarySchool_dataset_root_path</em> to the path of the interspeech2018 dataset (the current dataset).</p> <p>Set <em>nacta_dataset_root_path</em> to the path of the jingju dataset part1.</p> <p>Set <em>nacta2017_dataset_root_path</em> to the path the jingju dataset part2.</p> <p>For more information on this paper, please refer to the Github page: <a href="https://github.com/ronggong/interspeech2018_submission01">https://github.com/ronggong/interspeech2018_submission01</a></p> <p> </p>
Jingju a cappella singing voice test dataset for "An efficient deep learning model for musical onset detection"
<p>Jingju a cappella singing voice test dataset used in the paper "An efficient deep learning model for musical onset detection".</p> <p>Arxiv paper link: <a href="https://arxiv.org/abs/1806.06773">https://arxiv.org/abs/1806.06773</a></p> <p>Supplementary information and code for the paper: <a href="https://github.com/ronggong/musical-onset-efficient">https://github.com/ronggong/musical-onset-efficient</a></p> <p><strong>Content:</strong></p> <ol> <li>ismir_2018_dataset_for_reviewing.zip: audio, syllable boundary and label annotation</li> <li>jingju dataset train test split filenames.xlsx: train and test split filename list</li> </ol> <p><strong>Citation:</strong></p> <pre>@article{gong2018towards, title={Towards an efficient deep learning model for musical onset detection}, author={Gong, Rong and Serra, Xavier}, journal={arXiv preprint arXiv:1806.06773}, year={2018} } </pre> <p><strong>Contact:</strong></p> <p>Rong Gong: rong.gong<at>upf.edu</p>
Raw data corresponding to Huestegge, S. M., Raettig, T., & Huestegge, L. (2019). "Are face-incongruent voices harder to process? Effects of face-voice gender incongruency on basic cognitive information processing." Journal: Experimental Psychology.
<p>Raw data file prior to subject-based aggregation. Variables and values are decribed within the file. For further reference and specifications please also refer to the original publication in the journal Experimental Psychology.</p>
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.