Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
2
datasets available to search
ShareScore release 0.9.0
Dataset results
2 results for “open reel audio tapes”
A workflow and digital filters for correcting speed and equalisation errors on digitised audio open-reel magnetic tapes - Audio Samples
<p>This repository makes available the audio samples related to the paper:</p> <p>Niccolò Pretto, Nadir Dalla Pozza, Alberto Padoan, Anthony Chmiel, Kurt James Werner, Alessandra Micalizzi, Emery Schubert, Antonio Rodà, Simone Milani and Sergio Canazza, <em>A workflow and digital filters for compensating speed and equalisation errors on digitised audio open-reel magnetic tapes</em>, Journal of the Audio Engineering Society, Special Issue on Audio Filter Design, 2022.</p> <p>The experiment and the three case studies are described in the publication above.</p> <p>This repository contains two main directories (<strong>bold</strong> indicates directory names):</p> <ul> <li> <p><strong>Experiment Samples</strong>: the 10 seconds long samples used in the experiment;</p> </li> <li> <p><strong>Long Samples</strong>: the original 6 minutes long samples, one for each of the identified cases.</p> </li> </ul> <p>Here is the notation of the file naming:</p> <ul> <li> <p>W: recording (writing);</p> </li> <li> <p>R: reproducing;</p> </li> <li> <p>3: 3.75 NAB;</p> </li> <li> <p>7N: 7.5 NAB;</p> </li> <li> <p>7C: 7.5 CCIR;</p> </li> <li> <p>15C: 15 CCIR.</p> </li> </ul> <p>For what concerns the <strong>Experiment Samples</strong> directory, here is the summary of the recording/reproducing standards of the adopted samples and their notation:</p> <ul> <li> <p>SET A: Recording 3.75 NAB (W3) - Reproducing 7.5 CCIR (R7C);</p> </li> <li> <p>SET B: Recording 3.75 NAB (W3) - Reproducing 15 CCIR (R15C);</p> </li> <li> <p>SET C: Recording 7.5 NAB (W7N) - Reproducing 15 CCIR (R15C).</p> </li> </ul> <p>Here are the variants of the samples:</p> <ul> <li> <p>REFERENCE: produced by using the correct equalization standard;</p> </li> <li> <p>ANCHOR: the "Reference" altered with a low-pass filter, with pass band set at 7 kHz for music and 3.5 kHz for speech;</p> </li> <li> <p>INCORRECT: produced by using an intentionally incorrect equalization, created by mismatching the recording and reading curves and resampled to the correct speed;</p> </li> <li> <p>MATLAB: the “Incorrect” variant corrected by means of a Matlab script;</p> </li> <li> <p>API: the “Incorrect” variant corrected by means of an <em>ad hoc</em> web interface adopting Web Audio API, for simulating real-time correction in web applications.</p> </li> </ul> <p>Here is the samples list:</p> <ul> <li> <p>SET A:</p> <ul> <li> <p>Training:</p> <ul> <li> <p>sample4: Richard Wagner - <em>Ride of the Valkyries</em>;</p> </li> </ul> </li> <li> <p>Test:</p> <ul> <li> <p>sample1: Taylor Swift - <em>Shake It Off</em>;</p> </li> <li> <p>sample5: Queen - <em>We Will Rock You</em>;</p> </li> <li> <p>sample8: Bruno Maderna - <em>Continuo</em>;</p> </li> <li> <p>sample9: Luciano Berio - <em>Différences</em>.</p> </li> </ul> </li> </ul> </li> <li> <p>SET B (the track title reflects the name of the file from which the track itself was extracted, from the CLIPS project of the University of Napoli: <a href="http://www.clips.unina.it/en/index.jsp">http://www.clips.unina.it/en/index.jsp</a>):</p> <ul> <li> <p>Training:</p> <ul> <li> <p>sample22: CLIPS project - <em>LP4m18bZ</em>;</p> </li> </ul> </li> <li> <p>Test:</p> <ul> <li> <p>sample15: CLIPS project - <em>LP1f20bZ</em>;</p> </li> <li> <p>sample16: CLIPS project - <em>LP4m20bZ</em>;</p> </li> <li> <p>sample17: CLIPS project - <em>LP1f19bZ</em>;</p> </li> <li> <p>sample18: CLIPS project - <em>LP4m19bZ</em>.</p> </li> </ul> </li> </ul> </li> <li> <p>SET C:</p> <ul> <li> <p>Training:</p> <ul> <li> <p>sample3: Carl Orff - <em>Carmina Burana - O Fortuna</em>;</p> </li> </ul> </li> <li> <p>Test:</p> <ul> <li> <p>sample2: The Weeknd - <em>Save Your Tears</em>;</p> </li> <li> <p>sample6: Eagles - <em>Hotel California</em>;</p> </li> <li> <p>sample10: Bruno Maderna - <em>Musica su Due Dimensioni</em>;</p> </li> <li> <p>sample12: Bruno Maderna - <em>Syntaxis</em>.</p> </li> </ul> </li> </ul> </li> </ul>
A workflow and novel digital filters for compensating speed and equalization errors on digitized audio open-reel tapes: audio samples
<p>This repository makes available the audio samples related to the experiments described in the paper: </p> <p><em>Niccolò Pretto, Nadir Dalla Pozza, Alberto Padoan, Anthony Chmiel, Kurt James Werner, Alessandra Micalizzi, Emery Schubert, Antonio Rodà, Simone Milani and Sergio Canazza. 2021. A workflow and novel digital filters for compensating speed and equalization errors on digitized audio open-reel tapes. In Proceedings of the 16th International Conference on Audio Mostly (AM '21). Association for Computing Machinery, New York, NY, USA</em></p> <p>The experiment and the three case studies are described in the publication below. Here is the summary of their recording/reproducing standards and their notation.</p> <ul> <li>SET A: Recording 3.75 NAB (W3) - Reproducing 7.5 CCIR (R7C);</li> <li>SET B: Recording 3.75 NAB (W3) - Reproducing 15 CCIR (R15C);</li> <li>SET C: Recording 7.5 NAB (W7N) - Reproducing 15 CCIR (R15C).</li> </ul> <p>Here is the notation of the file naming: </p> <ul> <li>W: recording (writing);</li> <li>R: reproducing;</li> <li>3: 3.75 NAB;</li> <li>7N: 7.5 NAB;</li> <li>7C: 7.5 CCIR;</li> <li>15C: 15 CCIR.</li> </ul> <p>Here are the variants of the samples:</p> <ul> <li>REFERENCE: produced by using the correct equalization standard;</li> <li>ANCHOR: the "Reference" altered with a low-pass filter, with pass band set at 7 kHz for music and 3.5 kHz for speech;</li> <li>INCORRECT: produced by using an intentionally incorrect equalization, created by mismatching the recording and reading curves and resampled to the correct speed;</li> <li>MATLAB: the “Incorrect” variant corrected by means of a Matlab script;</li> <li>API: the “Incorrect” variant corrected by means of an <em>ad hoc</em> web interface adopting Web Audio API, for simulating real-time correction in web applications.</li> </ul> <p>Here is the samples list:</p> <p>SET A:</p> <ul> <li>Training: <ul> <li>sample4: Richard Wagner - <em>Ride of the Valkyries</em>;</li> </ul> </li> <li>Test: <ul> <li>sample1: Taylor Swift - <em>Shake It Off</em>;</li> <li>sample5: Queen - <em>We Will Rock You</em>;</li> <li>sample8: Bruno Maderna - <em>Continuo</em>;</li> <li>sample9: Luciano Berio - <em>Différences</em>.</li> </ul> </li> </ul> <p>SET B (the track title reflects the name of the file from which the track itself was extracted, from the CLIPS project of the University of Napoli: <a href="http://www.clips.unina.it/en/index.jsp">http://www.clips.unina.it/en/index.jsp</a>):</p> <ul> <li>Training: <ul> <li>sample22: CLIPS project - <em>LP4m18bZ</em>;</li> </ul> </li> <li>Test: <ul> <li>sample15: CLIPS project - <em>LP1f20bZ</em>;</li> <li>sample16: CLIPS project - <em>LP4m20bZ</em>;</li> <li>sample17: CLIPS project - <em>LP1f19bZ</em>;</li> <li>sample18: CLIPS project - <em>LP4m19bZ</em>.</li> </ul> </li> </ul> <p>SET C:</p> <ul> <li>Training: <ul> <li>sample3: Carl Orff - <em>Carmina Burana - O Fortuna</em>;</li> </ul> </li> <li>Test: <ul> <li>sample2: The Weeknd - <em>Save Your Tears</em>;</li> <li>sample6: Eagles - <em>Hotel California</em>;</li> <li>sample10: Bruno Maderna - <em>Musica su Due Dimensioni</em>;</li> <li>sample12: Bruno Maderna - <em>Syntaxis</em>.</li> </ul> </li> </ul> <p>Supplementary material can be found at the following DOI: 10.5281/zenodo.5118708</p> <p> </p>
ScienceDex guides
Understand access before you commit
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.