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|Title:||Automation of binaural headphone audio calibration on an artificial head||Authors:||Ooi, Kenneth
|Keywords:||Engineering::Electrical and electronic engineering||Issue Date:||2021||Source:||Ooi, K., Xie, Y., Lam, B. & Gan, W. (2021). Automation of binaural headphone audio calibration on an artificial head. MethodsX, 8, 101288-. https://dx.doi.org/10.1016/j.mex.2021.101288||Project:||COT-V4– 2020–1
|Journal:||MethodsX||Abstract:||In studies with auralisation of audio stimuli over headphones, accurate presentation of headphone audio is critical for replicability and ecological validity. Audio stimuli levels are usually calibrated by placing studio quality headphones on an artificial head and torso simulator. Manual adjustment of audio tracks becomes laborious when the number of stimuli is large, especially for applications with large datasets. To increase reliability and productivity, we devised a stimulus-agnostic, automated calibration procedure for headphone audio via an artificial head and torso simulator, with a LabVIEW implementation available at doi:10.21979/N9/0KYIAU.•The procedure uses a National Instruments NI-9234 data acquisition module and works with any ITU‑T P.58:2013 and ANSI/ASA S 3.36:2012 compliant artificial head measurement systems.•The procedure works by an adjustment to a generic guess, followed by a modified binary search, wherein the audio stimuli are calibrated to within a user-specified tolerance level.•Each stimulus in a validation run to calibrate 250 stimuli to 65.0 ± 0.5 dB was played back an average of 2.22 ± 0.92 times before successful calibration, thus demonstrating the robustness and efficiency of our proposed method.||URI:||https://hdl.handle.net/10356/152884||ISSN:||2215-0161||DOI:||10.1016/j.mex.2021.101288||DOI (Related Dataset):||10.21979/N9/0KYIAU||Rights:||© 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Journal Articles|
Updated on May 16, 2022
Updated on May 16, 2022
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