期刊
METHODSX
卷 8, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.mex.2021.101288
关键词
Soundscape; Spatial audio; Auralisation
资金
- National Research Foundation, Singapore
- Ministry of National Development, Singapore under its Cities of Tomorrow R&D Program (CoT Award) [COT-V4- 2020-1]
- Singapore Ministry of Education Academic Research Fund Tier-2 [MOE2017-T2-2-060]
This study introduces an automated calibration procedure for headphone audio using an artificial head and torso simulator, implemented with LabVIEW. The procedure, designed to improve reliability and productivity, utilizes a National Instruments data acquisition module and works with compliant artificial head measurement systems. The method demonstrates robustness and efficiency by calibrating audio stimuli to a user-specified tolerance level with a modified binary search approach.
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/OKYIAU. 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. (C) 2021 The Author(s). Published by Elsevier B.V.
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