The Use of Sound Level Meter Apps in the Clinical Setting

Fava, Gaetano; Oliveira, Gisele; Baglione, Melody; Pimpinella, Michael; Spitzer, J.

doi:10.1044/2015_ajslp-13-0137

Cited by 12 publications

(6 citation statements)

References 26 publications

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“…Because the microphones are different across devices, it is expected that the mean results will vary. In fact, the results from the current study were similar to Fava and colleagues (2016) when only looking at the main effect of device. In the current study, there were differences in the means of some of the voice measures across the smartphones and the head mounted microphone.…”

Section: Discussionsupporting

confidence: 81%

“…Results indicated that three SLM apps on an iPhone 5 and a RadioShack (Fort Worth, TX) SLM yielded inconsistent dB readings for the human voice at soft, habitual, and loud when compared with a Type 1 SLM. Frankly, it is not surprising that the results in Fava and colleagues (2016) were significantly different across recording devices for the human voice recordings and outside of the established criterion of ± 2dB. The procedures did not account for within subject variability across trials.…”

Section: Discussionmentioning

confidence: 95%

“…The current results are consistent with previous work that suggested certain apps may be used to accurately and reliably measure environmental noise ( Kardous & Shaw, 2014 ) and a Samsung Galaxy Note 3 compared with a head mounted microphone produced strong correlations between acoustic voice measures ( Uloza et al, 2015 ). A recent study presents contradictory suggestions that the use of apps for dB readings of the human voice is premature because all of the three apps tested were not comparable to a Larson-Davis (Depew, NY) Model 831 Type 1 sound level meter (SLM) ( Fava, Oliveira, Baglione, Pimpinella, & Spitzer, 2016 ). Results indicated that three SLM apps on an iPhone 5 and a RadioShack (Fort Worth, TX) SLM yielded inconsistent dB readings for the human voice at soft, habitual, and loud when compared with a Type 1 SLM.…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Influence of Smartphones and Software on Acoustic Voice Measures.

Grillo¹,

Brosious²,

Sorrell³

et al. 2016

Int J Telerehab

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This study assessed the within-subject variability of voice measures captured using different recording devices (i.e., smartphones and head mounted microphone) and software programs (i.e., Analysis of Dysphonia in Speech and Voice (ADSV), Multi-dimensional Voice Program (MDVP), and Praat). Correlations between the software programs that calculated the voice measures were also analyzed. Results demonstrated no significant within-subject variability across devices and software and that some of the measures were highly correlated across software programs. The study suggests that certain smartphones may be appropriate to record daily voice measures representing the effects of vocal loading within individuals. In addition, even though different algorithms are used to compute voice measures across software programs, some of the programs and measures share a similar relationship.

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Section: Discussionsupporting

confidence: 81%

Section: Discussionmentioning

confidence: 95%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Influence of Smartphones and Software on Acoustic Voice Measures.

Grillo¹,

Brosious²,

Sorrell³

et al. 2016

Int J Telerehab

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“…With recent technological advancements (e.g., portable tablet computers and multimedia players), sound level meters (SLMs) have become widely accessible for clinical use for voice and speech assessment. For example, using mobile phone applications (apps) for SLM readings is cost and time efficient, technologically advanced, portable, and engaging for patients and clinicians (Fava et al, 2016). Although a review of the literature supports the general effectiveness of using handheld technology as an augmentative and alternative communication system for children with ASD (Gilroy et al, 2017;Goldsmith & LeBlanc, 2004;Lorah et al, 2015Lorah et al, , 2018, researchers seldom evaluate transportable technology on different aspects of communication, such as acoustic output.…”

mentioning

confidence: 99%

Experimental analysis of voice volume for children with autism spectrum disorder

Garcia,

Wunderlich,

Pelfrey

et al. 2023

Behavioral Interventions

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Inappropriate prosodic production, or features of speech, is an often observed but rarely treated communication skill deficit in individuals with autism. Expanding on previous literature, we conducted a functional analysis (FA) on the voice volume responses (VVRs) of two children with autism spectrum disorder utilizing similar procedures to those of Edgerton and Wine. Then, we evaluated the efficacy of visual feedback from an app and a function‐based treatment to decrease loud VVRs and increase appropriate VVRs. Results of the FA indicated loud VVRs were maintained by social negative reinforcement for one participant and by both social negative and automatic reinforcement for another. For one participant, the intervention decreased the use of loud VVRs and increased appropriate VVRs. For the other participant, the results of the intervention were inconclusive. Implications of conducting functional analyses and behavioral interventions to treat inappropriate voice volume are discussed.

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“…18 Few clinical studies have compared the performance of sound measurement systems that include smartphones and SLMAs using different features, stimuli, and acoustic environments. Fava et al 19 determined that their use of three uncalibrated SLMAs in an anechoic chamber resulted in inconsistent measures of pure tones (125, 250, 500, 1,000, 2,000, 4,000, and 8,000 Hz) from 60 to 100 dB SPL compared with a Type 1 SLM, and were inaccurate for human voice and vocal intensity measurements > 50 dB SPL. However, Serpanos et al 20 found that SLMAs using calibrated internal smartphone microphones were accurate for measuring narrow band noise and white noise (WN) at levels > 40 dBA in a sound-treated test booth.…”

mentioning

confidence: 99%

The Accuracy of Smartphone Sound Level Meter Applications (SLMAs) in Measuring Sound Levels in Clinical Rooms

et al. 2021

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Background The accuracy of smartphone sound level meter applications (SLMAs) has been investigated with varied results, based on differences in platform, device, app, available features, test stimuli, and methodology. Purpose This article determines the accuracy of smartphone SLMAs with and without calibration of external and internal microphones for measuring sound levels in clinical rooms. Research Design Quasi-experimental research design comparing the accuracy of two smartphone SLMAs with and without calibration of external and internal microphones. Data Collection and Analysis Two iOS-based smartphone SLMAs (NIOSH SLM and SPL Meter) on an iPhone 6S were used with and without calibrated external and internal microphones. Measures included: (1) white noise (WN) stimuli from 20 to 100 dB sound pressure level in a sound-treated test booth and (2) sound levels in quiet in four nonsound-treated clinical rooms and in simulated background sound conditions using music at 45, 55, and 80 dBA. Chi-square analysis was used to determine a significant difference (p ≤ 0.05) in sound measures between the SLMAs and a Type 1 SLM. Results Measures of WN signals and room sound level measures in quiet and simulated background sound conditions were significantly more accurate at levels ≥ 40 dBA using the SLMAs with calibrated external and internal microphones. However, SLMA measures with and without calibration of external and internal microphones overestimated sound levels < 40 dBA. Conclusion The SLMAs studied with calibrated external or internal microphones are able to verify the room environment for audiologic screening at 1,000, 2,000, and 4,000 Hz at 20 dB hearing level (American Academy of Audiology and American Speech-Language-Hearing Association) using supra-aural earphones (American National Standards Institute S3.1–1999 [R2018]). However, the tested SLMAs overestimated low-level sound < 40 dBA, even when the external or internal microphones were calibrated. Clinicians are advised to calibrate the microphones prior to using measurement systems involving smartphones and SLMAs to measure room sound levels and to monitor background noise levels throughout the provision of clinical services.

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The Use of Sound Level Meter Apps in the Clinical Setting

Abstract: The use of apps for SPL readings in the clinical setting is premature because all 3 apps adopted were incomparable with the Type 1 SLM.

Cited by 12 publications

References 26 publications

Influence of Smartphones and Software on Acoustic Voice Measures.

Influence of Smartphones and Software on Acoustic Voice Measures.

Experimental analysis of voice volume for children with autism spectrum disorder

The Accuracy of Smartphone Sound Level Meter Applications (SLMAs) in Measuring Sound Levels in Clinical Rooms

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