Find research datasets worth reusing

opencc-by-4.0Nov 2023View details →

Spoken_audio_digits

Open the record for dataset details and reuse information.

Fig. 2 in A mesh network case study for digital audio signal processing in Smart Farm

Fig. 2. IoT node prototype.

Fig. 6. T3 in A mesh network case study for digital audio signal processing in Smart Farm

Fig. 6. T3 test results.

Fig. 4 in A mesh network case study for digital audio signal processing in Smart Farm

Fig. 4. Organization diagram of tests T1 (left above), T2 (left below) and T3 (right).

Fig. 7. Packets received during testing using 4 in A mesh network case study for digital audio signal processing in Smart Farm

Fig. 7. Packets received during testing using 4 nodes.

Fig. 1 in A mesh network case study for digital audio signal processing in Smart Farm

Fig. 1. Mesh network topology (left) and star topology (right).

restrictedIPD-UNDECIDEDFeb 2026View details →

ClinicalTrials.gov24/100

The Effect of Digital Stories Prepared With Digital Audio File (Podcast) on Midwifery Learning

ClinicalTrials.gov study NCT06046378. IPD Sharing: Not stated. Countries: 1. Publications: 0.

restrictedFeb 2022View details →

A workflow and digital filters for correcting speed and equalisation errors on digitised audio open-reel magnetic tapes - Audio Samples

This repository makes available the audio samples related to the paper: Niccolò Pretto, Nadir Dalla Pozza, Alberto Padoan, Anthony Chmiel, Kurt James Werner, Alessandra Micalizzi, Emery Schubert, Antonio Rodà, Simone Milani and Sergio Canazza, A workflow and digital filters for compensating speed and equalisation errors on digitised audio open-reel magnetic tapes, Journal of the Audio Engineering Society, Special Issue on Audio Filter Design, 2022. The experiment and the three case studies are described in the publication above. This repository contains two main directories (bold indicates directory names): <ul> <li> Experiment Samples: the 10 seconds long samples used in the experiment; </li> <li> Long Samples: the original 6 minutes long samples, one for each of the identified cases. </li> </ul> Here is the notation of the file naming: <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> For what concerns the Experiment Samples directory, here is the summary of the recording/reproducing standards of the adopted samples and their notation: <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> Here are the variants of the samples: <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 ad hoc web interface adopting Web Audio API, for simulating real-time correction in web applications. </li> </ul> Here is the samples list: <ul> <li> SET A: <ul> <li> Training: <ul> <li> sample4: Richard Wagner - Ride of the Valkyries; </li> </ul> </li> <li> Test: <ul> <li> sample1: Taylor Swift - Shake It Off; </li> <li> sample5: Queen - We Will Rock You; </li> <li> sample8: Bruno Maderna - Continuo; </li> <li> sample9: Luciano Berio - Différences. </li> </ul> </li> </ul> </li> <li> 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>): <ul> <li> Training: <ul> <li> sample22: CLIPS project - LP4m18bZ; </li> </ul> </li> <li> Test: <ul> <li> sample15: CLIPS project - LP1f20bZ; </li> <li> sample16: CLIPS project - LP4m20bZ; </li> <li> sample17: CLIPS project - LP1f19bZ; </li> <li> sample18: CLIPS project - LP4m19bZ. </li> </ul> </li> </ul> </li> <li> SET C: <ul> <li> Training: <ul> <li> sample3: Carl Orff - Carmina Burana - O Fortuna; </li> </ul> </li> <li> Test: <ul> <li> sample2: The Weeknd - Save Your Tears; </li> <li> sample6: Eagles - Hotel California; </li> <li> sample10: Bruno Maderna - Musica su Due Dimensioni; </li> <li> sample12: Bruno Maderna - Syntaxis. </li> </ul> </li> </ul> </li> </ul>

restrictedAug 2021View details →

A workflow and novel digital filters for compensating speed and equalization errors on digitized audio open-reel tapes: audio samples

This repository makes available the audio samples related to the experiments described in the paper:  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 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. <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> Here is the notation of the file naming:  <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> Here are the variants of the samples: <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 ad hoc web interface adopting Web Audio API, for simulating real-time correction in web applications.</li> </ul> Here is the samples list: SET A: <ul> <li>Training: <ul> <li>sample4: Richard Wagner - Ride of the Valkyries;</li> </ul> </li> <li>Test: <ul> <li>sample1: Taylor Swift - Shake It Off;</li> <li>sample5: Queen - We Will Rock You;</li> <li>sample8: Bruno Maderna - Continuo;</li> <li>sample9: Luciano Berio - Différences.</li> </ul> </li> </ul> 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>): <ul> <li>Training: <ul> <li>sample22: CLIPS project - LP4m18bZ;</li> </ul> </li> <li>Test: <ul> <li>sample15: CLIPS project - LP1f20bZ;</li> <li>sample16: CLIPS project - LP4m20bZ;</li> <li>sample17: CLIPS project - LP1f19bZ;</li> <li>sample18: CLIPS project - LP4m19bZ.</li> </ul> </li> </ul> SET C: <ul> <li>Training: <ul> <li>sample3: Carl Orff - Carmina Burana - O Fortuna;</li> </ul> </li> <li>Test: <ul> <li>sample2: The Weeknd - Save Your Tears;</li> <li>sample6: Eagles - Hotel California;</li> <li>sample10: Bruno Maderna - Musica su Due Dimensioni;</li> <li>sample12: Bruno Maderna - Syntaxis.</li> </ul> </li> </ul> Supplementary material can be found at the following DOI: 10.5281/zenodo.5118708

Fig. 5 in A mesh network case study for digital audio signal processing in Smart Farm

Fig. 5. Results in T1 (left) and T2 (right).

Fig. 3 in A mesh network case study for digital audio signal processing in Smart Farm

Fig. 3. Diagram of the proposed algorithm..