Range Of Sound Levels Research Articles

Robot control architecture for autonomous sound source mapping

Acoustic imaging for the monitoring of industrial infrastructures remains a common feature to address numerous issues, such as diagnosing faults in rotating machinery or detecting gas leaks. Simultaneously, autonomous vehicles have emerged as a strong tool to automate various maintenance and diagnostic tasks, incorporating increasingly diverse sensors, including microphone arrays. The objective of this work is to present a new control strategy for an autonomous vehicle equipped with a microphone array, aimed at mapping its environment completely blindly. The system must be robust to background noise and to a wide range of sound levels from sources, in order to be effective in an industrial setting. The control strategy proposed in this preliminary work meets these expectations and allows for mapping a large number of sources in a noisy environment, with a wide range of sources sound levels. Its versatile approach allows adapting the localization algorithm used between MUSIC and constrained-MUSIC depending on the situation and acoustic environment, leveraging the information the robot will have collected during its measurement. Its performances are evaluated in a simulation study.

Assessing the Usefulness of Mobile Apps for Noise Management in Occupational Health and Safety: Quantitative Measurement and Expert Elicitation Study.

Overexposure to occupational noise can lead to hearing loss. Occupational noise mapping is conventionally performed with a calibrated sound level meter (SLM). With the rise of mobile apps, there is a growing number of SLM apps available on mobile phones. However, few studies have evaluated such apps for accuracy and usefulness to guide those with occupational noise detection needs in selecting a quality app. The purpose of this study was to evaluate the accuracy and usefulness of SLM mobile apps to guide workplace health and safety professionals in determining these apps' suitability for assessing occupational noise exposure. The following three iOS apps were assessed: the NIOSH (National Institute for Occupational Safety and Health) Sound Level Meter, Decibel X, and SoundMeter X apps. The selected apps were evaluated for their accuracy in measuring sound levels in low-, moderate-, and high-noise settings within both simulated environments and real-world environments by comparing them to a conventional SLM. The usefulness of the apps was then assessed by occupational health specialists using the Mobile App Rating Scale (MARS). The NIOSH Sound Level Meter app accurately measured noise across a range of sound levels in both simulated settings and real-world settings. However, considerable variation was observed between readings. In comparison, the Decibel X and SoundMeter X apps showed more consistent readings but consistently underestimated noise levels, suggesting that they may pose a risk for workers. Nevertheless, none of the differences in sound measurements between the three apps and the conventional SLM were statistically significant (NIOSH Sound Level Meter: P=.78; Decibel X: P=.38; SoundMeter X: P=.40). The MARS scores for the three apps were all above 3.0, indicating the usefulness of these apps. Under the conditions of this study, the NIOSH Sound Level Meter app had equivalent accuracy to the calibrated SLM and a degree of usefulness according to the MARS. This suggests that the NIOSH Sound Level Meter app may be suitable for mapping noise levels as part of a monitoring strategy in workplaces. However, it is important to understand its limitations. Mobile apps should complement but not replace conventional SLMs when trying to assess occupational noise exposure risk. Our outcomes also suggest that the MARS tool may have limited applicability to measurement-based apps and may be more suited to information-based apps that collect, record, and store information.

Correcting for bias effects due to exposure uncertainty in community noise exposure-response analyses.

A method for correcting the relationship-distorting effects of sound level uncertainty on community noise exposure-response investigations is presented. The method employs a "calibration model," which describes how the true exposure-response relationship becomes distorted (shallower) in the presence of sound level uncertainty and other factors. This model was incorporated in a maximum likelihood estimation procedure that solves for the coefficient values that would have been observed in the absence of sound level measurement error. Three interacting situational variables known to attenuate the slopes of fitting functions were incorporated in the model: the sample population sound level uncertainty, the sound level range, and the distribution of sound levels over that range. The method provided unbiased estimates of the slope and intercept when the calibration parameter values were known exactly. Sensitivity to inexact knowledge of the values was also examined. Although the bias effect could be removed, uncertainty in the solved regression coefficients increased with increasing predictor variable (sound level) uncertainty and with data set displacement from the asymptote.

Background Sound Levels in Rural Areas of the U.S.

ANSI S12.9 Part 3 Annex C provides a compilation of the average daytime, nighttime, and day-night sound levels in six land use categories, ranging from Category 1 - "very noisy urban residential" to Category 6 - "very quiet suburban and rural residential." However, the data in table C.1 of the standard, which provides typical day, night, and day-night sound levels by land use category, is based primarily on more urbanized areas, and provides caution in using the data for the less densely developed land use Categories 5 and 6. In this paper we supplement this Annex by providing a collection of ambient sound level data at over 150 rural locations primarily in the northeastern and midwestern U.S. As the ambient sound level is not a single metric, the results include the range of sound levels in various metrics, including the A-weighted L10, L50, L90, and Leq, for the daytime and nighttime, and DNL. Unweighted octave and 1/3 octave band ranges and averages are also presented for each metric. Where possible, we also compare these results at individual locations to the predictions made by national noise mapping programs.

Effects of sensorineural hearing loss on formant-frequency discrimination: Measurements and models

This study concerns the effect of hearing loss on discrimination of formant frequencies in vowels. In the response of the healthy ear to a harmonic sound, auditory-nerve (AN) rate functions fluctuate at the fundamental frequency, F0. Responses of inner-hair-cells (IHCs) tuned near spectral peaks are captured (or dominated) by a single harmonic, resulting in lower fluctuation depths than responses of IHCs tuned between spectral peaks. Therefore, the depth of neural fluctuations (NFs) varies along the tonotopic axis and encodes spectral peaks, including formant frequencies of vowels. This NF code is robust across a wide range of sound levels and in background noise. The NF profile is converted into a rate-place representation in the auditory midbrain, wherein neurons are sensitive to low-frequency fluctuations. The NF code is vulnerable to sensorineural hearing loss (SNHL) because capture depends upon saturation of IHCs, and thus the interaction of cochlear gain with IHC transduction. In this study, formant-frequency discrimination limens (DLFFs) were estimated for listeners with normal hearing or mild to moderate SNHL. The F0 was fixed at 100 Hz, and formant peaks were either aligned with harmonic frequencies or placed between harmonics. Formant peak frequencies were 600 and 2000 Hz, in the range of first and second formants of several vowels. The difficulty of the task was varied by changing formant bandwidth to modulate the contrast in the NF profile. Results were compared to predictions from model auditory-nerve and inferior colliculus (IC) neurons, with listeners’ audiograms used to individualize the AN model. Correlations between DLFFs, audiometric thresholds near the formant frequencies, age, and scores on the Quick speech-in-noise test are reported. SNHL had a strong effect on DLFF for the second formant frequency (F2), but relatively small effect on DLFF for the first formant (F1). The IC model appropriately predicted substantial threshold elevations for changes in F2 as a function of SNHL and little effect of SNHL on thresholds for changes in F1.

Exposure uncertainty effects on regression parameter values

Exposure uncertainty in community noise exposure-response investigations can bias the results of regression analyses, especially when the range of sound levels is limited, the standard deviation of sound level uncertainty is more than a decibel or two and the distribution of sound levels across the sample population deviates significantly from a uniform one. The bias is manifested in the solved values of the regression coefficients, leading to incorrect representations of both the slope and intercept. Methods are available to reduce the magnitude of such bias and this paper examines the regression calibration approach. A basic overview of regression calibration is provided as well as its implementation in a logistic regression analysis. The effectiveness and limitations of the procedure are discussed, as are the requisite additional field measurements necessary to quantify situational parameters required in the calibration process. While coefficient bias can be mitigated, coefficient uncertainty that might otherwise be due only to uncertainty in the predicted variable (fraction annoyed) increases with increasing exposure uncertainty. Such uncertainty impacts the ability to observe differences between sample populations in both scalar and categorical variables.

Formant-frequency discrimination in listeners with hearing loss: Behavioral results and neural models

In responses of the healthy ear to harmonic sounds, the amplitude of f0-related fluctuations of auditory-nerve (AN) responses varies along the tonotopic axis. Neural fluctuation amplitudes encode spectral peaks, including formant frequencies of vowels, because responses of inner-hair-cells (IHCs) tuned near spectral peaks are captured (or dominated) by a single harmonic, resulting in lower fluctuation amplitudes than IHCs tuned between spectral peaks. This code is robust across a wide range of sound levels and in background noise, and is converted into a rate-place representation in the auditory midbrain, where neurons are sensitive to low-frequency fluctuations. This neural-fluctuation code is vulnerable to sensorineural hearing loss (SNHL), because it depends upon saturation of IHCs and the interaction of cochlear amplification with IHC transduction. We tested formant-frequency discrimination in listeners with normal hearing or mild to moderate SNHL. The difficulty of the task was modulated by varying formant bandwidth. The relationship between spectral peaks and harmonic frequencies was also varied. Formant frequencies were 600 and 2000 Hz, in the range of first and second formants of many vowels. Results were compared to predictions made using model AN and midbrain neurons, with audiograms for the individual listeners included in the peripheral model. [Work supported by NIDCD-R01-001641]

Encoding sound in the cochlea: from receptor potential to afferent discharge.

Ribbon-class synapses in the ear achieve analog to digital transformation of a continuously graded membrane potential to all-or-none spikes. In mammals, several auditory nerve fibres (ANFs) carry information from each inner hair cell (IHC) to the brain in parallel. Heterogeneity of transmission among synapses contributes to the diversity of ANF sound-response properties. In addition to the place code for sound frequency and the rate code for sound level, there is also a temporal code. In series with cochlear amplification and frequency tuning, neural representation of temporal cues over a broad range of sound levels enables auditory comprehension in noisy multi-speaker settings. The IHC membrane time constant introduces a low-pass filter that attenuates fluctuations of the receptor potential above 1-2kHz. The ANF spike generator adds a high-pass filter via its depolarization-rate threshold that rejects slow changes in the postsynaptic potential and its phasic response property that ensures one spike per depolarization. Synaptic transmission involves several stochastic subcellular processes between IHC depolarization and ANF spike generation, introducing delay and jitter that limits the speed and precision of spike timing. ANFs spike at a preferred phase of periodic sounds in a process called phase-locking that is limited to frequencies below a few kilohertz by both the IHC receptor potential and the jitter in synaptic transmission. During phase-locking to periodic sounds of increasing intensity, faster and facilitated activation of synaptic transmission and spike generation may be offset by presynaptic depletion of synaptic vesicles, resulting in relatively small changes in response phase. Here we review encoding of spike-timing at cochlear ribbon synapses.

Medial olivocochlear reflex effects on amplitude growth functions of long- and short-latency components of click-evoked otoacoustic emissions in humans.

Functional outcomes of medial olivocochlear reflex (MOCR) activation, such as improved hearing in background noise and protection from noise damage, involve moderate to high sound levels. Previous noninvasive measurements of MOCR in humans focused primarily on otoacoustic emissions (OAEs) evoked at low sound levels. Interpreting MOCR effects on OAEs at higher levels is complicated by the possibility of the middle-ear muscle reflex and by components of OAEs arising from different locations along the length of the cochlear spiral. We overcame these issues by presenting click stimuli at a very slow rate and by time-frequency windowing the resulting click-evoked (CE)OAEs into short-latency (SL) and long-latency (LL) components. We characterized the effects of MOCR on CEOAE components using multiple measures to more comprehensively assess these effects throughout much of the dynamic range of hearing. These measures included CEOAE amplitude attenuation, equivalent input attenuation, phase, and slope of growth functions. Results show that MOCR effects are smaller on SL components than LL components, consistent with SL components being generated slightly basal of the characteristic frequency region. Amplitude attenuation measures showed the largest effects at the lowest stimulus levels, but slope change and equivalent input attenuation measures did not decrease at higher stimulus levels. These latter measures are less commonly reported and may provide insight into the variability in listening performance and noise susceptibility seen across individuals.NEW & NOTEWORTHY The auditory efferent system, operating at moderate to high sound levels, may improve hearing in background noise and provide protection from noise damage. We used otoacoustic emissions to measure these efferent effects across a wide range of sound levels and identified level-dependent and independent effects. Previous reports have focused on level-dependent measures. The level-independent effects identified here may provide new insights into the functional relevance of auditory efferent activity in humans.

Subjective perception of hospital environments with varying dynamic ranges of noise

Individuals may perceive noise stimuli differently based on the content of the source, due to factors such as the frequency spectra, level, temporal characteristics, or even the dynamic range of the noise source (the range of sound levels from quietest to loudest in a given sample). Results from earlier phases of this research indicated subject preference for quieter overall environments with a larger dynamic range of noise (based on the analysis of noise Occurrence Rates within real hospital units). In this phase of the study, a subjective perceptual laboratory test was designed and administered to 33 subjects to assess the annoyance perception of noise stimuli with varying dynamic ranges. Simulated hospital environments were utilized as the source stimuli, selected to represent a “typical” hospital unit patient area. It was found that in a controlled setting, subjects perceived noise stimuli with a wider dynamic range and louder peak noise events more negatively than noise sources with more consistent sound levels.

Analysis of the Sound Environment and the Sound Level in the Delivery Room in the First Hour of a Newborn's Life.

ObjectiveThe aim of the study was to analyse the sound environment and the range of sound levels recorded in the delivery room immediately after the birth of a newborn.Materials and methodsThe research method was open observation combined with recording measurements of the sound intensity levels. The material was collected by means of an observation questionnaire. The research was conducted in 11 maternity hospitals in Warsaw. A total of 304 vaginal labours were analysed.ResultsThe average sound level in the delivery room after the birth of a newborn was 58.03 ± 7.66 dB, and the sound intensity ranged from 40.30 dB to 78.0 dB. Staff conversations were the most common sources of noise. A statistically significant relationship between the number of people in the delivery room and sound intensity was observed. The number of people positively correlated with the average sound level (R=0.520, p<0.001).ConclusionsBased on the tests, it was found that the average sound level in the delivery room exceeded the recommended standards. The noise was mainly caused by the activity of staff. The present study indicates the need for staff education and the use of noise reduction procedures.

Baseline assessment of underwater noise in the Ria Formosa

The Ria Formosa is a sheltered large coastal lagoon located on the Atlantic South Coast of Portugal, that has been classified as a natural park since 1987. The lagoon hosts a diverse and abundant fish community and other species of commercial importance. Several economical activities are supported by shipping, and as such, vessel traffic within the Ria Formosa lagoon is very intense at some locations during particular seasons of the year, creating high levels of underwater noise. Recently, strong efforts are being made to turn the main inlet of the lagoon, the Faro-Olhão Inlet, a testing site for small scale tidal stream turbines, which will bring an additional source of underwater noise. Underwater noise can be one of a number of factors causing habitat degradation, as it can perturb fish behavior and cause physiological damage. Therefore, in order to comply with underwater noise pollution regulations, tidal energy technology developers are very interested in minimising the introduction of acoustic energy in the environment during the operation of their devices. Under the scope of project SCORE, which involved the deployment and operation of a floating tidal energy converter, this paper presents and discusses the first baseline noise monitoring performed at Ria Formosa. The acoustic data were collected in two occasions over several days, one in the winter and the other in the summer, in 2017. The obtained analysis results highlight the potential impact of the intense boat traffic in Ria Formosa, and the wide range of sound levels introduced in that ecosystem, and the high diurnal and seasonal variability.

The role of ambient sound levels, signal-to-noise ratio, and stimulus pulse rate on behavioural disturbance of seabass in a net pen

Anthropogenic sources increasingly contribute to the underwater soundscape and this may negatively impact aquatic life, including fish. Anthropogenic sound may mask relevant sound, alter behaviour, physiology, and may lead to physical injury. Behavioural effect studies are often seen as critical to evaluate individual and population-level impact. However, behavioural responsiveness likely depends on context and characteristics of sound stimuli. We pose that ambient sound levels, signal-to-noise ratio (SNR), and pulse rate interval (PRI), could affect the behavioural response of fish. To study this, we experimentally exposed groups of tagged European seabass (Dicentrarchus labrax) to different impulsive sound treatments that varied in pulse level, elevated background level, SNR, and PRI. Upon sound exposure, the seabass increased their swimming depth. The variation in the increase in swimming depth could not be attributed to pulse level, background level, SNR or PRI. It may be that the current range of sound levels or PRIs was too narrow to find such effects.

Multi-unit analysis of noise level exceedances over time in hospitals

The purpose of this project is to expand the knowledge base of health care soundscapes by analyzing noise level exceedances in a relatively large sample of hospitals and units, in order to more clearly define the range of sound levels typically experienced in modern healthcare noise environments. Occurrence rate is a newer measure of how often peak and maximum sound levels occur over a measurement period. Other metrics analyzed in this project include the occurrence rate of minimum sound levels and the difference between maximum and minimum sound levels. These and more traditional sound level metrics are evaluated along with standard percentile noise levels over time. The data analyzed in this project are from the Healthcare Acoustics Research Team (HART) library of hospital research and includes 4 hospitals, 18 units, and several measurement locations in each unit. The large sample size provides a greater degree of certainty in the development of standard levels of noise exceeded over time in hospitals.The purpose of this project is to expand the knowledge base of health care soundscapes by analyzing noise level exceedances in a relatively large sample of hospitals and units, in order to more clearly define the range of sound levels typically experienced in modern healthcare noise environments. Occurrence rate is a newer measure of how often peak and maximum sound levels occur over a measurement period. Other metrics analyzed in this project include the occurrence rate of minimum sound levels and the difference between maximum and minimum sound levels. These and more traditional sound level metrics are evaluated along with standard percentile noise levels over time. The data analyzed in this project are from the Healthcare Acoustics Research Team (HART) library of hospital research and includes 4 hospitals, 18 units, and several measurement locations in each unit. The large sample size provides a greater degree of certainty in the development of standard levels of noise exceeded over time in hospitals.

Effects of Background Sounds on Annoyance Reaction to Foreground Sounds in Psychoacoustic Experiments in the Laboratory: Limits and Consequences

In a variety of applications, e.g., psychoacoustic experiments, virtual sound propagation demonstration, or synthesized noise production, noise samples are played back in laboratories. To simulate realistic scenes or to mask unwanted background sounds, it is sometimes preferable to add background ambient sounds to the noise. However, this can influence noise perception. It should be ensured that either background sounds do not affect, e.g., annoyance from foreground noise or that possible effects can be quantified. Two laboratory experiments are reported, in which effects of mixing background sounds to foreground helicopter samples were investigated. By means of partially balanced incomplete block designs, possible effects of three independent variables, i.e., helicopter’s sound exposure level, background type, and background sound pressure level were tested on the dependent variable annoyance, rated on the ICBEN 11-point numerical scale. The main predictor of annoyance was helicopter’s sound exposure level. Stimuli with eventful background sounds were found to be more annoying than those with less eventful background sounds. Furthermore, background type and level interacted significantly. For the major part of the background sound level range, increasing the background level was associated with increased or decreased annoyance for stimuli with eventful and less eventful background sounds, respectively.

Predicting acoustic dose associated with marine mammal behavioural responses to sound as detected with fixed acoustic recorders and satellite tags.

To understand the consequences of underwater noise exposure for cetaceans, there is a need for assessments of behavioural responses over increased spatial and temporal scales. Bottom-moored acoustic recorders and satellite tags provide such long-term and large spatial coverage of behaviour compared to short-duration acoustic-recording tags. However, these tools result in a decreased resolution of data from which an animal response can be inferred, and no direct recording of the sound received at the animal. This study discusses the consequence of the decreased resolution of data from satellite tags and fixed acoustic recorders on the acoustic dose estimated by propagation modelling and presents a method for estimating the range of sound levels that animals observed with these methods have received. This problem is illustrated using experimental results obtained during controlled exposures of northern bottlenose whales (Hyperoodon ampullatus) exposed to naval sonar, carried out near Jan Mayen, Norway. It is shown that variability and uncertainties in the sound field, resulting from limited sampling of the acoustic environment, as well as decreased resolution in animal locations, can lead to quantifiable uncertainties in the estimated acoustic dose associated with the behavioural response (in this case avoidance and cessation of foraging).

Evoked Potential Recordings of Auditory Brainstem Activity in the Mouse: An Optimized Method for the Assessment of Hearing Function of Mice.

Hearing loss is a common sensory deficiency suffered by millions worldwide. It is a heterogeneous condition and genetics plays a critical role in its etiology. Gene variants can fundamentally alter hearing function, or predispose the auditory system towards loss of function resulting from other factors. In mouse studies of hearing loss and gene function, an evoked potential electrophysiological recording, the auditory brainstem response (ABR), is now considered the optimal way to screen large numbers of individuals, either with normal hearing sensitivity or with hearing impairment. Other routinely used methods to assess hearing function (such as acoustic startle responses, or otoacoustic emissions) do not allow assessment of the same broad spectrum of dysfunction nor readily allow the threshold sensitivity of the neural output of the cochlea to be assessed and are less ideal. An optimized recording system to rapidly and reproducibly record high-quality ABRs from mutant mice as part of a high-throughput phenotyping pipeline was developed. Click-evoked ABRs and ABRs evoked by pure-tone frequencies over a range of sound levels from 0 dB to 95 dB, sound pressure levels (SPL) are recorded. This takes approximately 15-20 min per mouse (with 5 tone frequencies), allowing a large number of mutant mice to be screened. This method has been used to measure ABRs on a high-throughput mutant mouse phenotyping pipeline and in laboratory tests to follow-up the hearing loss phenotypes identified on that pipeline.

Low Frequency Noise Level Assessment in Vienna

The present contribution reports on the results of sound level measurements in a number of locations in the city of Vienna, Austria. Thereby, a primary objective was to determine the degree to which the measurement results agree with corresponding information in the E.N.D. (Environmental Noise Directive 2002/49/EC) maps. Moreover, the relationship between the low-frequency segment of the acoustical exposure to the broad-band data was investigated. The results point to traffic as the main source of urban noise exposure. E.N.D. maps appear to provide a reasonable general overview of the urban noise circumstances. However, measurement results at individual locations can considerably deviate from E.N.D. data. Numeric values of low-frequency sound level range were found to be generally higher than those of the broad-frequency levels. The results revealed also a strong correlation between measurement-based L50R and NR (Noise Rating) values.

Nonlinear auditory models yield new insights into representations of vowels.

Studies of vowel systems regularly appeal to the need to understand how the auditory system encodes and processes the information in the acoustic signal. The goal of this study is to present computational models to address this need, and to use the models to illustrate responses to vowels at two levels of the auditory pathway. Many of the models previously used to study auditory representations of speech are based on linear filter banks simulating the tuning of the inner ear. These models do not incorporate key nonlinear response properties of the inner ear that influence responses at conversational-speech sound levels. These nonlinear properties shape neural representations in ways that are important for understanding responses in the central nervous system. The model for auditory-nerve (AN) fibers used here incorporates realistic nonlinear properties associated with the basilar membrane, inner hair cells (IHCs), and the IHC-AN synapse. These nonlinearities set up profiles of f0-related fluctuations that vary in amplitude across the population of frequency-tuned AN fibers. Amplitude fluctuations in AN responses are smallest near formant peaks and largest at frequencies between formants. These f0-related fluctuations strongly excite or suppress neurons in the auditory midbrain, the first level of the auditory pathway where tuning for low-frequency fluctuations in sounds occurs. Formant-related amplitude fluctuations provide representations of the vowel spectrum in discharge rates of midbrain neurons. These representations in the midbrain are robust across a wide range of sound levels, including the entire range of conversational-speech levels, and in the presence of realistic background noise levels.

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

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. 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. 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. 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.