Abstract
This paper presents a workflow and digital filters for compensating speed and equalization errors that can impact digitized audio open-reel tapes. Thirty cases of mismatch between recording and reproducing speed (3.75, 7.5, 15, and 30 in/s) and equalization standards [National Association of Broadcasters (NAB), Consultative Committee for International Radio (CCIR) and Audio Engineering Society) were considered. For three frequent cases of mismatch (NAB 3.75 in/s-CCIR 7.5 in/s; NAB 3.75 in/s-CCIR 15 in/s; and NAB 7.5 in/s-CCIR 15 in/s) Multiple Stimuli with Hidden Reference and Anchor-inspired tests with ≥21 participants assessed the workflow and digital filters. using excerpts of music and voice. Two different corection filters were used, both of which provided promising results. Following this subsequent analyses examined predictive variables for correct and incorrect Multiple Stimuli with Hidden Reference and Anchor performance, as well as spectral and numerical differences between filters, which provide key insights and recommendations for further related work.
| Original language | English |
|---|---|
| Pages (from-to) | 495-509 |
| Number of pages | 15 |
| Journal | Journal of the Audio Engineering Society |
| Volume | 70 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - Jun 2022 |
Bibliographical note
Publisher Copyright:© 2022 Audio Engineering Society. All rights reserved.
Datasets
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A workflow and digital filters for correcting speed and equalisation errors on digitised audio open-reel magnetic tapes - Supplementary Material
Pretto, N., Dalla Pozza, N., Padoan, A., Chmiel, A., Werner, K. J., Micalizzi, A., Schubert, E., Rodà, A., Milani, S. & Canazza, S., ZENODO, 7 Feb 2022
DOI: 10.5281/zenodo.5996876, https://zenodo.org/records/5996876
Dataset
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A workflow and digital filters for correcting speed and equalisation errors on digitised audio open-reel magnetic tapes - Audio Samples
Pretto, N., Dalla Pozza, N., Padoan, A., Chmiel, A., Werner, K. J., Micalizzi, A., Schubert, E., Rodà, A., Milani, S. & Canazza, S., ZENODO, 7 Feb 2022
DOI: 10.5281/zenodo.5996918, https://zenodo.org/records/5996918
Dataset