The Accuracy of Smartphone Sound Level Meter Applications With and Without Calibration 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], ... Clinical Focus
Newly Published
Clinical Focus  |   November 06, 2018
The Accuracy of Smartphone Sound Level Meter Applications With and Without Calibration
 
Author Affiliations & Notes
  • Yula C. Serpanos
    Department of Communication Sciences and Disorders, Adelphi University, Garden City, NY
    Long Island Doctor of Audiology Consortium, Garden City, NY
  • Brittany Renne
    Department of Communication Sciences and Disorders, Adelphi University, Garden City, NY
    Long Island Doctor of Audiology Consortium, Garden City, NY
  • Janet R. Schoepflin
    Department of Communication Sciences and Disorders, Adelphi University, Garden City, NY
    Long Island Doctor of Audiology Consortium, Garden City, NY
  • Diane Davis
    Department of Communication Sciences and Disorders, Adelphi University, Garden City, NY
    Long Island Doctor of Audiology Consortium, Garden City, NY
  • Disclosure: The authors have declared that no competing interests existed at the time of publication.
    Disclosure: The authors have declared that no competing interests existed at the time of publication. ×
  • Correspondence to Yula C. Serpanos: serpanos@adelphi.edu
  • Editor-in-Chief: Julie Barkmeier-Kraemer
    Editor-in-Chief: Julie Barkmeier-Kraemer×
  • Editor: Rita Patel
    Editor: Rita Patel×
Article Information
Hearing & Speech Perception / Acoustics / Hearing Disorders / Newly Published / Clinical Focus
Clinical Focus   |   November 06, 2018
The Accuracy of Smartphone Sound Level Meter Applications With and Without Calibration
American Journal of Speech-Language Pathology, Newly Published. doi:10.1044/2018_AJSLP-17-0171
History: Received October 12, 2017 , Revised March 10, 2018 , Accepted July 4, 2018
 
American Journal of Speech-Language Pathology, Newly Published. doi:10.1044/2018_AJSLP-17-0171
History: Received October 12, 2017; Revised March 10, 2018; Accepted July 4, 2018

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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