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18 results for “Audio Evaluation”
Test Stimuli for Context Based Evaluation of the OPUS Audio Codec
<p>A dataset that includes the 360° video .mp4 files and Ambisonic audio .wav files used in the listening tests for "Context Based Evaluation of the OPUS Audio Codec for Spatial Audio Content in Virtual Reality".</p>
Audio captioning DCASE 2020 evaluation (testing) split
<p>This is the <strong>evaluation split for Task 6, Automated Audio Captioning, in DCASE 2020 Challenge</strong>. </p> <p>This evaluation split is the Clotho testing split, which is thoroughly described in the corresponding paper: </p> <p><em>K. Drossos, S. Lipping and T. Virtanen, "Clotho: an Audio Captioning Dataset," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 2020, pp. 736-740, doi: 10.1109/ICASSP40776.2020.9052990.</em></p> <p>available online at: <a href="https://arxiv.org/abs/1910.09387">https://arxiv.org/abs/1910.09387</a> and at: <a href="https://ieeexplore.ieee.org/document/9052990 ">https://ieeexplore.ieee.org/document/9052990 </a></p> <p>This evaluation split is meant to be used for the purposes of the Task 6 at the scientific challenge DCASE 2020. This split it is not meant to be used for developing audio captioning methods. For developing audio captioning methods, you should use the development and evaluation splits of Clotho. </p> <p>If you want the development and evaluation splits of Clotho dataset, you can find them also in Zenodo, at: <a href="https://zenodo.org/record/3490684">https://zenodo.org/record/3490684</a></p> <p>--------------------------------------------------------------------------------------------------------</p> <p><strong>== License ==</strong></p> <p>The audio files in the archives:</p> <ul> <li>clotho_audio_test.7z </li> </ul> <p>and the associated meta-data in the CSV file:</p> <ul> <li>clotho_metadata_test.csv</li> </ul> <p>are under the corresponding licences (mostly CreativeCommons with attribution) of Freesound [1] platform, mentioned explicitly in the CSV file for each of the audio files. That is, each audio file in the 7z archive is listed in the CSV file with the meta-data. The meta-data for each file are: </p> <ul> <li>File name</li> <li>Start and ending samples for the excerpt that is used in the Clotho dataset</li> <li>Uploader/user in the Freesound platform (manufacturer)</li> <li>Link to the licence of the file</li> </ul> <p>--------------------------------------------------------------------------------------------------------</p> <p><strong>== References ==</strong><br> [1] Frederic Font, Gerard Roma, and Xavier Serra. 2013. Freesound technical demo. In Proceedings of the 21st ACM international conference on Multimedia (MM '13). ACM, New York, NY, USA, 411-412. DOI: https://doi.org/10.1145/2502081.2502245</p>
Evaluation datasets for DCASE 2018 Bird Audio Detection
<p>Evaluation data audio for <a href="http://dcase.community/challenge2018/task-bird-audio-detection">the DCASE 2018 Bird Audio Detection task (Task 3)</a>.</p> <ol> <li> <p><strong>Crowdsourced dataset, UK ("warblrb10k")</strong> - a held-out set of 2,000 recordings from the same conditions as the Warblr development dataset.</p> </li> <li> <p><strong>Remote monitoring dataset, Chernobyl ("Chernobyl")</strong> - 6,620 audio clips collected from unattended remote monitoring equipment in the Chernobyl Exclusion Zone (CEZ). This data was collected as part of the <a href="https://wiki.ceh.ac.uk/display/NRT/NERC+RATE+TREE+Home">TREE</a> (Transfer-Exposure-Effects) research project into the long-term effects of the Chernobyl accident on local ecology. The audio covers a range of birds and includes weather, large mammal and insect noise sampled across various CEZ environments, including abandoned village, grassland and forest areas.</p> </li> <li> <p><strong>Remote monitoring night-flight calls, Poland ("PolandNFC")</strong> - 4,000 recordings from Hanna Pamuɫa's PhD project of monitoring autumn nocturnal bird migration. The recordings were collected every night, from September to November 2016 on the Baltic Sea coast, Poland, using Song Meter SM2 units with microphones mounted on 3–5 m poles. For this challenge, we use a subset derived from 15 nights with different weather conditions and background noise including wind, rain, sea noise, insect calls, human voice and deer calls.</p> </li> </ol>
HEAR NeurIPS 2021 Datasets (Holistic Evaluation of Audio Representations)
<p><strong>NOTES:</strong></p> <p><strong>* On Zenodo, please make sure you download datasets version 2021.3, not earlier versions. (2021.3 added the Vocal Imitations dataset at 48KHz. 2021.2 updated the tfds datasets.)</strong></p> <p><strong>* The datasets have different open licenses. Please see LICENSE.txt for each individual dataset's license.</strong></p> <p>These are the evaluation tasks for the HEAR (Holistic Evaluation of Audio Representations) 2021 NeurIPS challenge.</p> <p>The aim of this challenge is to develop a general-purpose audio representation that provides a strong basis for learning in a wide variety of tasks and scenarios. The HEAR 2021 challenge invites you to create an audio embedding that is as holistic as the human ear, i.e., one that performs well across a variety of everyday domains: What approach best generalizes to a wide range of downstream audio tasks without fine-tuning?<br> <br> HEAR 2021 evaluates audio representations using a benchmark suite across a variety of domains, including speech, environmental sound, and music.</p> <p>For more information, see the <a href="https://neuralaudio.ai/hear2021-holistic-evaluation-of-audio-representations.html">HEAR 2021 website</a> and upcoming PMLR journal article.</p> <p>Datasets were all normalized to a common human-readable format using <a href="https://github.com/neuralaudio/hear-preprocess">hearpreprocess</a>. Until 2022-04-01, datasets will be mirrored at <a href="https://data.neuralaudio.ai/">data.neuralaudio.ai</a>. This Zenodo mirror has all audio task but only at 48000Hz sampling rate. For other sampling rates (16000, 22050, 32000, 44100), please download files (requester pays) from Google Storage gs://hear2021-archive/tasks/ or AWS s3://hear2021-archive/tasks/</p>
DCASE 2024 Task 9: Language-Queried Audio Source Separation | Evaluation Set
<p>This is the <strong>evaluation set for Task 9, Language-Queried Audio Source Separation (LASS), in DCASE 2024 Challenge</strong>. </p> <p>This evaluation set is meant to be used for Task 9 at the scientific challenge DCASE 2024. This split is not meant to be used for training LASS methods. This split is meant to be used for evaluating LASS methods in the final testing & ranking stage. All audio clips are sourced from Freesound, uploaded between April and October 2023. Each audio file has been segmented into 10-second clips and converted to mono 16 kHz.</p> <p>This evaluation set consists of<strong> evaluation set (synth)</strong> and an <strong>evaluation set (real)</strong>. </p> <p><strong>== Evaluation set (synth) ==</strong></p> <p>This evaluation set is created using 1,000 audio clips. Each clip is annotated with three captions describing the content of the clip. We created 3,000 synthetic mixtures with signal-to-noise ratios (SNR) ranging from -15 to 15 dB. Each synthetic mixture includes one natural language query and its corresponding target source. We used annotated tag information to ensure that the two audio clips used in each mix do not share overlapping sound source classes. The original audio files used to create these mixtures are not released. The mixtures and language queries are available for evaluation.</p> <p>The audio files in the archives:</p> <ul> <li>lass_evaluation_synth.zip</li> </ul> <p>and the associated metadata (including audio filename and text queries) in the CSV file:</p> <ul> <li>lass_synthetic_evaluation.csv</li> </ul> <p><strong>== Evaluation set (real) ==</strong></p> <p>This evaluation set consists of 100 audio clips. Each audio clip contains at least two overlapping sound sources. For each audio clip, we manually annotated their component sources using text descriptions, so that each clip can be used as a 'mixture' from which to extract one or more of the component sources based on a text query. Each audio clip in evaluation (real) was labeled with two such text queries.</p> <p>The audio files in the archives:</p> <ul> <li>lass_evaluation_real.zip</li> </ul> <p>and the associated metadata (including audio filename and text queries) in the CSV file:</p> <ul> <li>lass_real_evaluation.csv</li> </ul>
Audio files and dataset: Guess who? Evaluating individual acoustic monitoring for males and females of the Tawny Pipit, a migratory passerine bird with a simple song
<p>We have uploaded the audio files (song files + recording files) and the dataset of the analysis conducted in our manuscript entitled ''Guess who? Evaluating individual acoustic monitoring for males and females of the<br> Tawny Pipit, a migratory passerine bird with a simple song''. This study is published in the scientific journal ''Journal of Ornithology''. We have also provided a txt. file, called 'Read me' explaining the uploaded data in more detail.</p>
SASS-E: The Steelpan Audio Sample Set for Evaluation
<p>SASS-E is an audio dataset containing 13,313 one-hit audio samples from three distinct steelpans.</p> <p><strong>Citation</strong></p> <p>This dataset is part of paper that will be published in the NIME 2023 proceedings in June, 2023. This section will be updated to reflect that citation then.</p> <p><strong>Data curator</strong></p> <p>Colin Malloy</p> <p><strong>Contact</strong></p> <p>You can contact Colin regarding this dataset at malloyc@uvic.ca.</p> <p> </p> <p><strong>About SASS-E</strong></p> <p>The SASS-E dataset is an audio dataset curated as part of Colin's PhD research. It consists of over 13,000 one-hit audio samples from three tenor steelpans totaling over 9 hours and 25 minutes of audio The samples were recorded in a professional quality recording studio at 48 kHz/ 24-bit depth. Approximately 50 strikes were recorded per note per instrument at a wide variety of dynamic levels and beating areas. This allows for comprehensive coverage of minute details and fluctuations in timbre.</p> <p>The audio samples are pre-split into training, validation, and test sets with 7,931 samples in the training set, 2,680 samples in the validation set, and 2,702 samples in the test set.</p> <p>The audio files are have filenames in the following format: <MIDI note number>_<set>_<instrument label>_sample_<number>.wav. They are each labeled with the MIDI note number for the given note struck. The instrument label is in the format "ctenor-0x" where x is the number assigned to the instrument.</p> <p> </p> <p><strong>Future of SASS-E</strong></p> <p>In the future, we plan to add samples from a wider variety of steelpans. The goal is to create a comprehensive dataset that covers all major varieties of steelpans from the soprano to bass voices of the family. This will include other kinds of tenor steelpans (such as low D tenors, Invader tenors, and 3rds/4ths tenors).</p> <p> </p> <p><strong>License</strong></p> <p>All of the samples in SASS-E are released under Creative Commons (CC) CC-BY-NC. This license allows reusers to distribute, remix, adapt, and build upon the material in any medium or format for noncommercial purposes only, and only so long as attribution is given to the creator. Specifically, these samples are not to be used in a commercial sample library (such as a virtual instrument).</p>
HEAR NeurIPS 2021 Submitted Models (Holistic Evaluation of Audio Representations)
<p>This is a mirror of audio representation models that are HEAR API compatible, submitted to the HEAR 2021 NeurIPS challenge: Holistic Evaluation of Audio Representations.</p> <p>To use these models, please see the <a href="https://github.com/neuralaudio/hear2021-submitted-models">hear2021-submitted-models</a> repository. For more information about HEAR, see the <a href="https://neuralaudio.ai/hear2021-holistic-evaluation-of-audio-representations.html">HEAR 2021 website</a>, upcoming PMLR journal article, and the <a href="https://zenodo.org/record/5887964">HEAR 2021 datasets</a> on Zenodo.</p> <p>The aim of this challenge is to develop a general-purpose audio representation that provides a strong basis for learning in a wide variety of tasks and scenarios. The HEAR 2021 challenge invites you to create an audio embedding that is as holistic as the human ear, i.e., one that performs well across a variety of everyday domains: What approach best generalizes to a wide range of downstream audio tasks without fine-tuning? HEAR 2021 evaluates audio representations using a benchmark suite across a variety of domains, including speech, environmental sound, and music.</p>
Evaluation and Optimization of Hearing Devices in 3-D Complex Audio Environments Study 3 (HEAR3D_CE3)
ClinicalTrials.gov study NCT04180254. IPD Sharing: NO. Countries: 1. Publications: 4.
Evaluation and Optimization of Hearing Devices in 3-D Complex Audio Environments
ClinicalTrials.gov study NCT03514758. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Evaluation and Optimization of Hearing Devices in 3-D Complex Audio Environments Study 2
ClinicalTrials.gov study NCT03774030. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Data to accompany "Evaluation of Spatial Audio Reproduction Methods (Part 2): Analysis of Listener Preference", J. AES, 2016
<p>This work was supported by the EPSRC Programme Grant S3A: Future Spatial Audio for an Immersive Listener Experience at Home (EP/L000539/1). Details about the data underlying this work, along with the terms for data access, are available from http://dx.doi.org/10.15126/surreydata.00809533</p> <p>If you use the data, please cite the following paper:</p> <p>J. Francombe, T. Brookes, and R. Mason, 2016: Evaluation of Spatial Audio Reproduction Methods (Part 2): Analysis of Listener Preference. Journal of the Audio Engineering Society</p>
Clinical Evaluation of the Nuance Audio Hearing Aid in Adults With Mild to Moderate Hearing Difficulties
ClinicalTrials.gov study NCT07100522. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Evaluating the Value of Audio-guided Meditation During Breast Biopsy Procedures
ClinicalTrials.gov study NCT05343975. IPD Sharing: NO. Countries: 1. Publications: 0.
Calibration and Evaluation of an Audio Pulse Oximeter Sensor (AudioOx) at Ascent and Descent From Simulated Altitude
ClinicalTrials.gov study NCT01732029. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Audio-vestibular Evaluation of Children and Young Adults With Osteogenesis Imperfecta
ClinicalTrials.gov study NCT05419960. IPD Sharing: NO. Countries: 1. Publications: 0.
Evaluating the Effect of a Mobile Audio Companion (Elly) to Reduce Anxiety in Cancer Patients
ClinicalTrials.gov study NCT04604158. IPD Sharing: NO. Countries: 1. Publications: 0.
Evaluation of Audio-Visual Distraction Technique on Child's Anxiety
ClinicalTrials.gov study NCT03229720. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
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)
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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.