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The Accuracy of Smartphone Sound Level Meter Applications With and Without Calibration

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AMERICAN JOURNAL OF SPEECH-LANGUAGE PATHOLOGY
卷 27, 期 4, 页码 1319-1328

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AMER SPEECH-LANGUAGE-HEARING ASSOC
DOI: 10.1044/2018_AJSLP-17-0171

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Purpose: The purpose of this study is to determine the accuracy of smartphone sound level meter applications (SLMAs) with calibration features across stimulus levels and for ambient room noise measures in the clinical setting. Method: The accuracy of 3 iOS-based smartphone SLMAs (SLMA1: Analyzer [Version 2.7.2, DSP Mobile], SLMA2: Sound Level Meter Pro [Version 2.2, Mint Muse LLC], and SLMA3: SPL Meter [Version 9.3, Andrew Smith, Studio Six Digital]), using a single smartphone device (iPhone 6S Model A1688, iOS 9.3.4, Apple), was evaluated with and without calibration using a 1000-Hz narrowband noise (NBN) and white noise (WN) stimuli over a range of sound levels (20-100 dB) and in ambient noise measures of 8 speech and hearing room environments. A simultaneous and corresponding SLMA and Type 1 sound level meter (SLM) measure per condition were documented with a photo image; each condition was replicated 5 times. Mean SLMA and SLM measures were compared. SLMA measures were considered accurate if within +/- 2 dB of the SLM. Results: Measures of NBN and WN signals using these SLMAs were accurate at levels above 40-50 dB when calibrated. NBN and WN signals using some SLMAs were significantly (p < .05) more accurate with calibration at levels > 40 to 50 dB. SLMA measures with or without calibration adjustment were inaccurate and overestimated room ambient noise levels < 50 dB. Conclusions: These findings suggest that some SLMAs are accurate for measuring NBN and WN stimuli within the range of 50-100 dB in sound-treated environments when calibrated. However, outcomes indicated that some SLMAs, even with calibration, overestimated low ambient noise levels and may not accurately verify quiet room environments < 50 dB for clinical services. These results should not be generalized for all smartphone types, and continued research on SLMAs using next-generation smartphone devices is warranted.

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