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| Year : 2014
| Volume
: 16 | Issue : 72 | Page
: 251-256 |
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| Sound level measurements using smartphone "apps": Useful or inaccurate? |
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Daniel R Nast, William S Speer, Colleen G Le Prell
Department of Speech, Language, and Hearing Sciences, University of Florida, Gainesville, FL 32608, USA
Click here for correspondence address
and email
| Date of Web Publication | 10-Sep-2014 |
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Many recreational activities are accompanied by loud concurrent sounds and decisions regarding the hearing hazards associated with these activities depend on accurate sound measurements. Sound level meters (SLMs) are designed for this purpose, but these are technical instruments that are not typically available in recreational settings and require training to use properly. Mobile technology has made such sound level measurements more feasible for even inexperienced users. Here, we assessed the accuracy of sound level measurements made using five mobile phone applications or "apps" on an Apple iPhone 4S, one of the most widely used mobile phones. Accuracy was assessed by comparing application-based measurements to measurements made using a calibrated SLM. Whereas most apps erred by reporting higher sound levels, one application measured levels within 5 dB of a calibrated SLM across all frequencies tested. Keywords: App, noise, smartphone, sound level meter
How to cite this article:
Nast DR, Speer WS, Le Prell CG. Sound level measurements using smartphone "apps": Useful or inaccurate?. Noise Health 2014;16:251-6 |
| Introduction | |  |
There are many potentially hazardous sources of noise in daily life. Damage to hearing from recreational noise has been discussed in detail by others. [1],[2] Some loud recreational environments include concerts, [3],[4],[5],[6] nightclubs/discotheques, [7],[8],[9],[10],[11] and sporting events. [12],[13] Identification of sound levels that are hazardous could prompt listeners to reduce the sound level, increase their distance from the sound source, or limit time in the noisy location if these solutions are within an individual's control. Alternatively, listeners can choose to use hearing protection. Making good decisions about unsafe noise depends on accurate sound level estimates. Sound level meters (SLMs) are a tool specifically designed for this purpose, and wearable dosimeters provide integrated information on exposure over a given period of time; however, these devices are not readily available to the average person in a recreational setting. Use of these devices requires a consumer to identify, purchase, and carry the device with them, in addition to understanding how to use the device and interpret the data.
Mobile technology has made sound level measurement more readily accessible. More than 50 SLM applications, "apps," are available for the iPhone ®, and apps are also available for Android platforms. These apps could provide quick, easy, convenient, and inexpensive sound level monitoring opportunities. [14],[15],[16] However, documentation establishing the accuracy with which a smartphone loaded with a SLM app can provide information for individuals about potentially hazardous noise conditions is just emerging. [17],[18] The lack of data regarding measurement accuracy has generated significant controversy about such devices.
The American National Standards Institute (ANSI) explicitly defines SLM performance and accuracy tolerances. [19],[20] Type 1 SLMs must be accurate within ±1 dB, and Type 2 SLMs must be accurate within ±2 dB. A third category, the Type 0, references high-precision instruments typically used in the laboratory rather than for field measurements. [21] A Type 2 SLM is typically appropriate for use in meeting occupational noise monitoring requirements set by the Occupational Safety and Health Administration (OSHA), [22] and ideally smartphone-based apps would meet the Type 2 criteria if they are to be useful in estimating noise hazard. Here, we assessed the accuracy of five SLM applications by comparing smartphone-based readings with measurements made using a Type 1 SLM.
| Methods | | |
Free-field sound levels were measured in a double-walled sound attenuating chamber meeting ANSI/Acoustical Society of America (ASA) S3.1-1999 (R2008) specifications for audiometric test rooms. Sound levels were measured using a hand-held analyzer (Brüel and Kjær Type 2250) with SLM software (BZ-7222) (Brüel and Kjær Sound & Vibration Measurement, Nærum, Denmark); the device was in compliance with Type 1 requirements per ANSI S1.4-1983 (R2006)/ANSI S1.4a-1985 (R2006). Accuracy was verified prior to each set of measurements using a sound calibrator (Brüel and Kjær Type 4231) (Brüel and Kjær Sound & Vibration Measurement, Nærum, Denmark) conforming to ANSI S1.40-1984. Average sound level measurements made with the SLM, which were the average of 20 data samples per test condition, were defined as the control condition against which the apps were assessed. All of the apps selected included options for both C-weighting and A-weighting. A-weighting is the standard for occupational noise surveys in 29 CFR 1910.95, and both A-weighting and C-weighting filters are specified in ANSI S1.4-1983 (R2006)/ANSI S1.4a-1985 (R2006). We assume that the apps implement the A- and C-weighting functions using digital filters within the apps, but note that the app developers do not provide documentation on the specific filter settings, and do not guarantee compliance with the relevant ANSI standards. It is possible the filter definitions may be incorrect in some applications.
The apps selected for this study were required to have both A-weighted and C-weighted capabilities. The apps identified as having both A-weighting and C-weighting capabilities were then grouped into five price ranges: Free, $0.99, $2.00-$4.99, $5.00-$9.99, and <$10.00. The ratings within the iTunes Store were then compared, and the top-rated app within each of the five price brackets was then selected to be included in the study, in an effort to assess technical performance of the apps that consumers perceived to be the "best" apps based on the ratings at that time. The five tested apps, in order of increasing the purchase price, were: DB volume (version 1.0.5, DSP Mobile) (Werder/Havel Germany), Advanced Decibel (version 1, Darren Gates), SPLnFFT Noise Meter (version 3.3, Fabian Lefebvre), SPL (version 2.6, Studio Six Digital), and SoundMeter (version 3.1, Faber Acoustical). The apps were purchased from the iTunes store and loaded onto a new iPhone ® 4S (Apple, Cupertino, CA) running Apple iOS v5.1.1 immediately after purchase.
During measurements, the SLM or the iPhone ® was mounted on a tripod facing the speaker with the microphone at a distance of 3 feet at 0° azimuth. Acoustic stimuli consisted of ~1/3 octave band noise with center frequencies at 0.25, 0.5, 1, 2, 4, and 8 kHz; signals were generated using a GSI-61 clinical audiometer (Grason Stadler, Eden Prairie, Minnesota, USA) calibrated annually according to ANSI/ASA S3.6-2010. Sound levels were measured in the absence of the acoustic signal (ambient noise) and in a calibrated sound field with stimuli presented at 0, 50, and 70 decibels hearing level (dB HL). Ambient noise inside the sound booth was 31 dB at 125 Hz, 21 dB at 250 Hz, 19 dB at 500 Hz, 14 dB at 1000 Hz, 4 dB at 2000 Hz, 7 dB at 4000 Hz, and 6 dB at 8000 Hz; these levels met the ANSI S3.1-1999 standard except at 500 Hz (limit = 16 dB) and1000 Hz (limit = 13 dB). Sound levels were also sampled with the audiometer output set at 85, 90, and 95 dB HL; that is, up to the maximum output level of the audiometer using external speakers (70 dB HL: 0.25 kHz; 85 dB HL: 0.5, 1, 2, 4, and 8 kHz; 90 dB HL: 2, 4, and 8 kHz; 95 dB HL: 4 kHz). We note here that sound presentation levels are specified in dB HL, as the audiometer used to present sounds is calibrated in dB HL. We have not attempted to convert between dB HL and dB sound pressure level (dB SPL) here (although this is possible); instead, the sound levels were measured using the calibrated Type 1 SLM (with A or C weighting selected), and the app measurements were compared against the Type 1 SLM measurements (with A or C weighting selected). The difference between the Type 1 SLM measurements and the app-based measurements were considered to be the app-specific error within A-weighted measurements, and within C-weighted measurements.
For each frequency by level comparison, the signal was turned on at a given output level and then sound levels were sampled using the SLM, with the SLM set on the fast time weighting setting. Once sound levels had stabilized, sound levels were read from the SLM once every 10 s until 10 measurements had been obtained. Sound levels were sampled using both the C-weighted filters and A-weighted filters for the SLM and each app, allowing the measurements to stabilize after each frequency change. Measurements continued until all frequency and intensity combinations had been assessed for each weighting on each device. Then, the process was repeated such that a total of 20 measurements were collected for each frequency by level by device/app condition. The first data sample within each data set did not systematically differ from subsequent samples, and there was no evidence suggesting any systematic differences between the first and second sets of data samples. Personal hearing protection was worn during data collection inside the sound booth.
App-based sound level measurements were compared to SLM control measurements for sound levels of 50 dB HL and above using a two-way analysis of variance (ANOVA) with frequency and app as independent variables, and the apps measured level as the dependent variable. Corrections for multiple pairwise comparisons were accomplished using Dunn's corrections for multiple comparisons; pairwise comparisons were limited to the apps compared against the Type I SLM control condition. We then calculated the average SPL measurement across the 20 measurements for each frequency by intensity pairing using the SLM in the C-weighted and A-weighted conditions. To illustrate the magnitude of the observed errors within each app, we then subtracted this average SLM measurement from each individual measurement for the C-weighted and A-weighted tests, respectively. These data were equivalent to the difference of means output from the ANOVA's.
| Results | | |
Measurements of the ambient noise level and the 0-dB HL signals were indistinguishable regardless of device (SLM, smartphone); the 0-dB HL signals did not exceed the ambient noise background at any tested frequency from 250 Hz to 8 kHz within any of the devices or conditions. Moreover, the low SPLs in both ambient and 0-dB HL conditions did not exceed the noise floor for the devices. The technical specifications for the Type 1 SLM include internal self-generated noise levels, which vary as a function of filter settings (A-weighting: 16. 6 dB; C-weighting: 16.2 dB; z-weighting from 5 Hz to 20 kHz: 20.1 dB). For the apps as used on the iPhone device, all of the ambient level measurements were at least 5-dB higher than the ambient levels measured using the Type 1 SLM, suggesting higher internal noise issues and poorer performance at low sound levels. As such, ambient and 0-dB HL signals are not presented or discussed any further. App-based measurements for the stimuli presented at 50 dB HL and above are presented in [Figure 1] (C-weighted levels) and [Figure 2] (A-weighted levels). Because the levels measured were virtually indistinguishable across the 20 tests within a given condition, the differences between the SLM and each app were statistically significant at most of the 42 frequency by level combinations completed for each app, even when errors were on the order of 1-2 dB when compared to the control. However, even if statistically significant, errors of 1-2 dB are acceptable in most noise measurement scenarios. The accuracy of the apps, relative to the Type 1 SLM, varied widely across apps. Performance was somewhat better for C-weighted measurements than for A-weighted measurements, perhaps because the C-weighting is a more limited filtering. Within both C-weighted and A-weighted measurements; however, there were some apps with nonlinear errors, that is during higher sound presentation levels at 2, 4, and 8 kHz, while several apps measured little or no increase despite increasing free field stimulus levels. On one hand, this pattern likely reflects saturation of the measuring device. However, it does not appear to be the iPhone microphone specifically, as the apparent saturation was app-specific, with two of the apps not showing any evidence for saturated responses. Calculated errors (difference in means, relative to the average Type 1 SLM measurement) are shown in [Figure 3]. | Figure 1: C-weighted app-based measurements were compared to C-weighted measurements made using a calibrated Type 1 sound level meter (SLM). In the C-weighted condition, SoundMeter measurements were typically within 3 dB of the SLM measurements across frequency and level conditions. For twoadditional apps, dB volume and advanced decibel, C-weighted level measurements were within 5 dB for most frequency by level combinations. The exceptions to this were at sound levels at and above 85 dBC at 2 kHz, where sound levels measurements were 7-8 dB lower than that measured using the calibrated Type 1 SLM. The other apps, SPLnFFT and SPL, reported levels that were approximately 8-10 dB elevated relative to the SLM across conditions. Nonlinear errors were observed at presentation levels above 85 dBC for dB volume, advanced decibel, and SPL apps. Data are mean ± standard deviation
Click here to view |
 | Figure 2: A-weighted app-based measurements were compared to A-weighted measurements made using a calibrated Type 1 sound level meter (SLM). In the A-weighted condition, SoundMeter was highly consistent across conditions, with measurements being within 1-2 dB of the calibrated Type 1 SLM. dB volume was accurate within 1-2 dB at levels up to 80 dB at 1, 2, 4, and 8 kHz. At 250 and 500 Hz, and at 1, 2, 4, and 8 kHz frequencies when levels were above 85 dB, dB volume sound levels were reported as 5-10 dB lower than that measured using the calibrated Type 1 SLM. The other apps, advanced decibel, SPLnFFT, and SPL, consistently reported levels that were elevated relative to the SLM, with differences ranging from 3 to 10 dB across frequencies and levels for each app. Nonlinear errors were observed at presentation levels above 85 dBA for dB volume, advanced decibel, and SPL apps. Data are mean ± standard deviation
Click here to view |
 | Figure 3: The difference in means is shown for frequency by level conditions. We calculated the average measured level across the 20 measurements with the sound level meter (SLM) made for both the C-weighted (a, c, and e) and A-weighted (b, d, and f) conditions, and subtracted the average SLM measurement from each individual measurement. In some cases app-based errors exceeded ±10 dB. Only one app, SoundMeter, was accurate within 5 dB across all frequencies and levels for both C-and A-weightings. Data are mean ± standard deviation
Click here to view |
| Discussion | | |
Sound level meter apps typically included a disclaimer stating that there was an effort to make the apps as accurate as possible, but that accuracy was not guaranteed. Some disclaimers indicated that the apps were intended for entertainment purposes only. Some apps were clearly more accurate than others within the selection of five apps that were tested here. Per the 1998 recommendations of The National Institute for Occupational Safety and Health, a 3-dB increase in sound level halves the "safe" listening time; [23] existing OSHA regulations define a 5-dB increase in sound level as halving the safe listening time. [24] Based on these well-accepted 3-dB and 5-dB time-intensity trading relationships, we contend here that an error exceeding 3-5 dB would have significant "real world relevance" regarding noise risk and decisions about safe listening duration. The data collected here suggest caution is indeed warranted for users who seek information about environmental noise levels for the purpose of evaluating and assessing potential noise risk using an app loaded on a smartphone. Indeed, with the potential exception of the SoundMeter app by Faber Acoustical, the data collected here indicate that the SLM apps are best used for entertainment purposes, as they are not accurate as SLMs, at least for these narrowband signals. Future investigations should consider including pink noise and/or white noise conditions or other noise signals with greater spectral variability.
Only one app, SoundMeter, was accurate within 5 dB across all frequencies and levels for both C- and A-weightings. Worth note, most of the apps tested here reported sound levels that were higher than those measured using a calibrated Type 1 SLM. With respect to the utility of app-based SLMs for noise assessment in recreational settings, apps that inaccurately report lower noise levels than are actually present would be more hazardous. Only one app, dB volume, had significant (>5 dB) errors in which the app reported lower sound levels than those measured using the Type 1 SLM. However, these errors were largely limited to the A-weighted condition, perhaps suggesting an error in the application of the A-weighting filter. None of the apps consistently reported significantly lower sound level measurements than the SLM in C-weighted conditions, although two apps (dB volume and advanced decibel) reported levels approximately 5 dB lower than the SLM at a subset of the frequency by intensity combinations. While the frequency by frequency data collected here provide useful insight into errors as a function of frequency, in the real world, noise does not come neatly packaged in one-third octave bands. Obvious and important next steps include assessments of real world noise.
The current data extend recent work by Kardous and Shaw, who described accuracy of several SLM apps used to measure SPL, unweighted and A-weighted, for a pink noise signal. [17] They reported that the SPLnFFT app had the most accurate unweighted measurements, and the SoundMeter app had the most accurate A-weighted measurements. As reported for pink noise signals, [17] our A-weighted level measurements for the third-octave band noise signals revealed the SoundMeter app to be the most accurate of the apps we tested. In contrast to the unweighted measurements by Kardous and Shaw; however, we frequently observed errors of 5-10 dB for the SPLnFFT app in the C-weighted condition.
Given the potential for errors in the filter settings, we encourage assessments of unweighted SPL when possible. In the real world, sound level measurement accuracy will vary not only as a function of device accuracy, but also the sampling strategy and the analysis protocol. [22] Here, a single iPhone 4S ® was used for all app-based data collection. The microphone on the iPhone was assumed to meet Apple's factory standard as the phone had not been used prior to this study. Thus, the data presented here likely represent the "best case scenario" (i.e., new microphone, limited opportunity for device damage prior to use in these tests) for that device. Even with optimal microphone conditions, none of the apps were able to record values low enough to measure ambient levels for verifying ANSI S3.1 maximum permissible noise levels in audiometric test rooms. With newer devices, technical specifications for the factory default microphone may change, which has the potential to influence measurement accuracy. Microphone and digital filter specifications, such as the antialiasing filter, are not listed in the iPhone ® 4S manual or on the Apple website. If smartphone manufacturers limit the ability of developers to apply high pass and/or low pass filters, this will further compromise the accuracy of weighting functions applied by app developers. While the exact upper and lower limits of the microphone, that is, its dynamic range, are not known, the nature of miniature microphones and our data (saturation of output levels in three of the five apps) suggest the signal is oftentimes unlikely to be measured accurately. Furthermore, some apps include procedures for measurement calibration, which may serve to improve accuracy for those apps. An important caveat for all apps is that the sensitivity of the microphone will limit the accuracy of the measurements, and some apps allow for external microphone usage to account for this variable. Clearly, the dynamic range was exceeded for many of the app conditions we tested. Further testing at higher intensities is needed to assess dynamic range more completely, as well as to assess variability across devices, including devices that have been subject to real world use. Unless the accuracy of an app has been determined, consumers should never trust noise level estimates based solely on app-based measurements.
| Conclusion | | |
While some apps that we tested offered a calibration feature, the current study did not utilize that function in order to simulate how a typical (nonexpert) app-user might operate the apps on their smartphone. Due to the inaccuracies of the apps as measured in this study, we do not recommend using even the best performing app, SoundMeter, without calibration, and without complete understanding of the dynamic range over which the device can provide accurate measurements. The nonlinearity of the errors was a significant issue. The majority of the apps we tested displayed nonlinearity of outputs at high level stimuli, which is suggestive of under-reporting of hazardous listening environments.
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Correspondence Address:
Dr. Colleen G Le Prell
Department of Speech, Language, and Hearing Sciences, University of Florida, Box 100174, Gainesville, FL 32608
USA
 Source of Support: The project was supported by the Hearing Research Center at the University of Florida. The authors thank Ben Faber at Faber Acoustics, for donating a free download of the SoundMeter application to D.R.N. for use in this study. In addition, the authors thank Edward Lobariñas for helpful comments on an earlier version of this manuscript., Conflict of Interest: None  | Check |
DOI: 10.4103/1463-1741.140495
[Figure 1], [Figure 2], [Figure 3] |
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| Xiaozhe Yin, Masoud Fallah-Shorshani, Rob McConnell, Scott Fruin, Meredith Franklin | | Environmental Science & Technology. 2020; 54(20): 12860 | | [Pubmed] | [DOI] | | | 24 |
Efficacy of Android Based Mobile Device as a Screening Tool for Hearing Loss in Quiet and Noisy Environments |
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| Devendra Kumar Gupta, Seema Pati, Sanajeet Singh, Ravi Roy, Rajeev Chugh, Sunil Goyal, Shivali Bansal, Keerti Sangwan | | Journal of Evolution of Medical and Dental Sciences. 2019; 8(32): 2577 | | [Pubmed] | [DOI] | | | 25 |
Using a smartphone to investigate classroom acoustics |
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| Maria Jesús Elejalde-García, Erica Macho-Stadler | | Journal of Physics: Conference Series. 2019; 1287(1): 012023 | | [Pubmed] | [DOI] | | | 26 |
Assimilation of mobile phone measurements for noise mapping of a neighborhood |
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| Raphaël Ventura, Vivien Mallet, Valérie Issarny | | The Journal of the Acoustical Society of America. 2018; 144(3): 1279 | | [Pubmed] | [DOI] | | | 27 |
Evaluation and calibration of mobile phones for noise monitoring application |
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| Raphaël Ventura, Vivien Mallet, Valérie Issarny, Pierre-Guillaume Raverdy, Fadwa Rebhi | | The Journal of the Acoustical Society of America. 2017; 142(5): 3084 | | [Pubmed] | [DOI] | |
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