OBJECTIVES:Studies have shown that in addition to energy, kurtosis plays an important role in the assessment of hearing loss caused by complex noise. The objective of this study was to investigate how to use noise recordings and audiometry collected from workers in industrial environments to find an optimal kurtosis-adjusted algorithm to better evaluate hearing loss caused by both continuous noise and complex noise. DESIGN:In this study, the combined effects of energy and kurtosis on noise-induced hearing loss (NIHL) were investigated using data collected from 2601 Chinese workers exposed to various industrial noises. The cohort was divided into three subgroups based on three kurtosis (β) levels (K 1 : 3 ≤ β ≤ 10, K 2 : 10 <β ≤ 50, and K 3 : β > 50). Noise-induced permanent threshold shift at test frequencies 3, 4, and 6 kHz (NIPTS 346 ) was used as the indicator of NIHL. Predicted NIPTS 346 was calculated using the ISO 1999 model for each participant, and the actual NIPTS was obtained by correcting for age and sex using non-noise-exposed Chinese workers (n = 1297). A kurtosis-adjusted A-weighted sound pressure level normalized to a nominal 8-hour working day (L Aeq,8h ) was developed based on the kurtosis categorized group data sets using multiple linear regression. Using the NIPTS 346 and the L Aeq.8h metric, a dose-response relationship for three kurtosis groups was constructed, and the combined effect of noise level and kurtosis on NIHL was investigated. RESULTS:An optimal kurtosis-adjusted L Aeq,8h formula with a kurtosis adjustment coefficient of 6.5 was established by using the worker data. The kurtosis-adjusted L Aeq,8h better estimated hearing loss caused by various complex noises. The analysis of the dose-response relationships among the three kurtosis groups showed that the NIPTS of K 2 and K 3 groups was significantly higher than that of K 1 group in the range of 70 dBA ≤ L Aeq,8h < 85 dBA. For 85 dBA ≤ L Aeq,8h ≤ 95 dBA, the NIPTS 346 of the three groups showed an obvious K 3 > K 2 > K 1 . For L Aeq,8h >95 dBA, the NIPTS 346 of the K 2 group tended to be consistent with that of the K 1 group, while the NIPTS 346 of the K 3 group was significantly larger than that of the K 1 and K 2 groups. When L Aeq,8h is below 70 dBA, neither continuous noise nor complex noise produced significant NIPTS 346 . CONCLUSIONS:Because non-Gaussian complex noise is ubiquitous in many industries, the temporal characteristics of noise (i.e., kurtosis) must be taken into account in evaluating occupational NIHL. A kurtosis-adjusted L Aeq,8h with an adjustment coefficient of 6.5 allows a more accurate prediction of high-frequency NIHL. Relying on a single value (i.e., 85 dBA) as a recommended exposure limit does not appear to be sufficient to protect the hearing of workers exposed to complex noise.

Smartphones have evolved into powerful devices with computing capabilities that rival the power of personal computers. Any smartphone can now be turned into a sound-measuring device because of its built-in microphone. Many sound measuring apps exist on the market for various mobile platforms. In our earlier research, we showed that a smart device with an adequate app can achieve compliance with most of the Class 2 requirements for periodic testing. In this paper, we present the methods and results of measuring directional response of a sound level meter consisting of a smartphone and one commercially available sound level meter app in the horizontal plane. We used both the built-in smartphone microphone and an external microphone according to relevant IEC [International Electrotechnical Commission] and ANSI [American National Standards Institute] sound level meter standards. The results show that the sound level meter app and an external microphone can achieve compliance with the requirements for Class 2 of IEC 61672/ANSI S1.4–2014 standard in the horizontal plane.

OBJECTIVES To evaluate (1) the accuracy of the International Organization for Standardization (ISO) standard ISO 1999 [(2013), International Organization for Standardization, Geneva, Switzerland] predictions of noise-induced permanent threshold shift (NIPTS) in workers exposed to various types of high-intensity noise levels, and (2) the role of the kurtosis metric in assessing noise-induced hearing loss (NIHL). DESIGN Audiometric and shift-long noise exposure data were acquired from a population (N = 2,333) of screened workers from 34 industries in China. The entire cohort was exclusively divided into subgroups based on four noise exposure levels (85 ≤ LAeq.8h < 88, 88 ≤ LAeq.8h < 91, 91 ≤ LAeq.8h < 94, and 94 ≤ LAeq.8h ≤ 100 dBA), two exposure durations (D ≤ 10 years and D > 10 years), and four kurtosis categories (Gaussian, low-, medium-, and high-kurtosis). Predicted NIPTS was calculated using the ISO 1999 model for each participant and the actual measured NIPTS was corrected for age and sex also using ISO 1999. The prediction accuracy of the ISO 1999 model was evaluated by comparing the NIPTS predicted by ISO 1999 with the actual NIPTS. The relation between kurtosis and NIPTS was also investigated. RESULTS Overall, using the average NIPTS value across the four audiometric test frequencies (2, 3, 4, and 6 kHz), the ISO 1999 predictions significantly (p < 0.001) underestimated the NIPTS by 7.5 dB on average in participants exposed to Gaussian noise and by 13.6 dB on average in participants exposed to non-Gaussian noise with high kurtosis. The extent of the underestimation of NIPTS by ISO 1999 increased with an increase in noise kurtosis value. For a fixed range of noise exposure level and duration, the actual measured NIPTS increased as the kurtosis of the noise increased. The noise with kurtosis greater than 75 produced the highest NIPTS. CONCLUSIONS The applicability of the ISO 1999 prediction model to different types of noise exposures needs to be carefully reexamined. A better understanding of the role of the kurtosis metric in NIHL may lead to its incorporation into a new and more accurate model of hearing loss due to noise exposure.

New technologies are reshaping health interventions across disciplines. This technological surge offers a clear opportunity to expand and improve hearing health, particularly in hearing loss prevention. A person's hearing health trajectory is defined by his or her overall hazardous exposures, environmental factors, and genetic determinates.1 Among the many factors that can contribute to hearing health (such as overall health, smoking, diet, and ototoxicant exposure), reducing noise exposure—particularly at work—has the greatest potential to significantly decrease the burden of hearing loss and tinnitus.2 About 24 percent of hearing impairment cases among U.S. workers is attributable to workplace noise exposures. Because noise-induced hearing loss is preventable, approximately one-fourth of hearing impairment cases in this population may be avoided by adopting preventive measures.3 While progress has been made toward the prevention of work-related hearing loss, it remains among the most common occupational illnesses. Overall, nearly one in four U.S. adults has audiometric evidence of noise-induced hearing loss—and most do not realize it.4 People continue to focus on the use of hearing protection to reduce noise exposure, even though only limited evidence is available on the effectiveness of this approach.5 However, new technologies to measure and control noise and test hearing hold the promise of expedited progress.Using the NIOSH sound level meter app to measure noise at a metal manufacturing plant. healthcare, hearing aids, digital healthIntensity levels of common sounds. Repeated exposure to sounds 85 dBA and higher can cause hearing loss. Healthcare, hearing aids, digital healthRISE OF MOBILE APPS, WEARABLES In 2018, nearly 40 percent of the world's population owned a smartphone, with smartphone penetration exceeding 70 percent in many countries, including the developing world.6 Hundreds of mobile applications (apps) related to noise and hearing loss are available, with new and improved apps appearing nearly every month. These apps can empower individuals to become more proactive in protecting their hearing health. In the past, a person needed expensive instrumentation and professional expertise to learn about their noise exposure. Nowadays, smartphone apps can accurately estimate noise exposure and make recommendations for appropriate protection.7,8 These tools, in conjunction with online information (e.g., NIOSH Sound Level Meter and Know Your Noise), provide mechanisms for individuals to manage their risk and protect their hearing. Other apps are now available to conduct hearing tests, measure daily sound allowance from music listening devices, and use crowdsourcing to provide information about environmental noise and quiet entertainment and dining venues (e.g., NoiseCapture, NoiseAdapt, NoiseScore, iHEARu, SoundPrint, etc.). Given the limitations of pure-tone audiometry for the early identification of ear damage, several groups have been looking into testing alternatives. Research has shown that audiometric thresholds obtained in untreated open rooms through mobile, wireless automated hearing test systems exhibit within subject test–retest reliability comparable to thresholds obtained using conventional computer-automated audiometry conducted in a single-walled, sound-treated booth in a mobile trailer.9 The development of portable, easily operated, economical, and reliable alternatives can facilitate hearing testing in typical worksite locations. Additional research is needed to make sure that such tools are accurate and can meet international sound and hearing measurement standards, but work is underway to establish compliance with pertinent standards.10 Wearable technologies are attracting attention in other health fields and could be useful in hearing loss prevention as well. Fitness trackers collect large amounts of data that data could be delivered directly to patient medical records, creating a mobile health (mHealth) system that gives health care professionals access to a patient's real-time data to aid in diagnosis and monitoring. At present, the primary limitation is sensor technology.11 As sensor technology improves, one could imagine monitoring noise exposure levels from a smart wristwatch and providing them directly to a hearing health or workplace safety professional—providing insight into hearing risk and enabling targeted prevention strategies. AI-POWERED HEARABLES Companies like Apple, Google, and Amazon along with established hearing aid companies are moving fast to introduce artificial intelligence into earphones and earpieces called hearables. This marketplace will not only deliver higher sound fidelity to the ear by cancelling unwanted background noise, but will also learn the user's environment and listening habits to deliver a better listening experience. In addition, hearables can be constantly connected and deliver information straight to the user's ears. Communicating in the opposite direction, the “Internet of Ears” is expanding the realm of voice-activated devices and may soon make it possible to control a myriad of household devices—including noise sources such as televisions and music players—by voice command. As these technologies become more commonplace, research into the total sound energy delivered daily to the ear needs to be studied, and recommendations from regulatory bodies need to be established to protect people from unsafe exposure to sound in recreational and work settings. ADDRESSING NOISE AT WORK The hierarchy of controls emphasizes protecting workers from the harmful effects of noise through elimination and reduction of hazards whenever possible, and emerging technologies are improving our ability to do so.12 For instance, nanomaterials offer the potential to effectively reduce noise across the frequency spectrum–including very low frequencies, where traditional sound absorption materials perform poorly.13 Cutting-edge technologies should not, however, cause us to lose sight of more basic solutions that may be very appropriate in some instances. For example, winners of the annual Safe-In-Sound http://bit.ly/2F4IWUi AwardsTM have implemented simple changes that have helped reduce noise levels such as large displays providing continuous noise monitoring results and changing from metal to rubber cart wheels. The CDC's Buy Quiet http://bit.ly/2EWwvcW is another key strategy for long-term noise reduction that relies on the purchase of new, quieter equipment.14 Hearing protection falls lower on the hierarchy of controls for noise exposure; however, many workplace hearing loss prevention programs still focus primarily on hearing protectors. One serious limitation of this approach is the inability to know exactly how much hearing protection a worker receives from the device. Fit-testing technology has changed that. Multiple systems are available that allow employers and other hearing care professionals to quickly evaluate how much sound reduction an individual is receiving from a particular device. Such information is invaluable to ensure that noise-exposed individuals are sufficiently protected and it can dramatically improve training, re-fitting, and the identification of appropriate alternatives as necessary.15 NIOSH has a web-based fit-test “screening” http://bit.ly/2EXnvUV that can be used if a full fit-test system is unavailable.16 Innovative and imaginative entrepreneurs are taking advantage of the opportunities afforded by these new technologies, bringing products to market that many hearing care professionals may never have imagined. In 2016, several U.S. federal occupational safety and health agencies launched the Hear and Now Noise Safety Challenge http://bit.ly/2EYCz4H, inviting individuals to compete for funding to develop creative ideas for reducing the occupational hearing loss problem.17 More than 30 entries were received, with winning ideas that included wearable sensors to monitor personal safety and information platforms that integrate health and safety data from different devices into a single, real-time database. Although new technologies offer many new prospects for hearing loss prevention, cutting-edge technology isn't always essential to make an intervention successful. Several Hear and Now challenge winners proposed simple ideas that could promote hearing loss prevention, such as adding logos to create fashionable hearing protection devices and using a simple, disposable device to insert foam earplugs properly and hygienically. Every hearing health professional—regardless of discipline, employment setting, or population served—has a role to play in ensuring that these emerging technologies help prevent hearing loss. Government agencies, standard-setting bodies, and professional organizations need to provide guidelines for developers to ensure that new technologies meet professional standards. Clinicians and safety professionals should implement emerging technologies however they can in their practices and provide feedback to developers on ways to improve or expand their utility. New technologies should help people make informed decisions about their noise exposure and take an active role in protecting their hearing, increase the effectiveness of hearing loss prevention and intervention programs, and reduce the burden of hearing disorders on society. Disclaimer: The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the NIOSH, CDC.

Many mobile sound measurement applications (apps) have been developed to take advantage of the built-in or fit-in sensors of the smartphone. One of the concerns is the accuracy of these apps when compared to professional sound measurement instruments. Previously, a research team from the National Institute for Occupational Safety and Health (NIOSH) developed the NIOSH Sound Level Meter (SLM) app for iOS smart devices. The team found the average accuracy of this app to be within ±1 dBA when using calibrated external microphones with a type 1 reference device and measuring pink noise at levels from 65 to 95 dBA in 5-dBA increments. The studies were conducted in a reverberant noise chamber at the NIOSH Acoustics Laboratory in Cincinnati. However, it is still unknown how this app performs in measuring industrial/mining sound levels outside of a controlled laboratory environment. The current NIOSH study evaluates the NIOSH SLM app to measure sound levels from a jumbo drill (a large mining machine). The study was conducted in a hemi-anechoic chamber at the NIOSH Pittsburgh Mining Research Division and followed by a field evaluation in an underground metal mine. Six different iOS smart devices were used with two types of external microphones chosen from previous studies to measure sound levels during jumbo drill operations, and the results were compared with a reference device. Results show that the average sound levels measured by the NIOSH SLM app are within ±1 dBA of the reference device both in the laboratory and field. However, the type of operation being performed, the selection and use of external microphones, distance from a noise source, and environmental factors (e.g., air movement) may all influence the accuracy of the app's performance. Although additional validation is still needed, the results from this study suggest a potential for using the NIOSH SLM app, with calibrated external microphones, to measure sound levels in mining operations.

Wildland firefighters are exposed to numerous noise sources that may be hazardous to their hearing. This study examined the noise exposure profiles for 264 wildland firefighters across 15 job categories. All 264 firefighters completed questionnaires to assess their use of hearing protection devices, enrollment in hearing conservation programs, and their overall perception of their noise exposure. Roughly 54% of firefighters' noise exposures exceeded the National Institute for Occupational Safety and Health recommended exposure limit of 85 decibels, A-weighted, over 8 hr, and 32% exceeded the Occupational Safety and Health Administration permissible exposure limit of 90 decibels, A-weighted, over 8 hr. Questionnaire results indicated good agreement between noise exposures and firefighters' perceptions of the noise hazard. Approximately 65% reported that they used some form of hearing protection; however, only 19% reported receiving any proper training regarding the use of hearing protection devices, with the majority of those firefighters relying on earplugs, including electronic and level-dependent earplugs, over earmuffs or other forms of hearing protectors. The results also suggest that improved communication and situational awareness play a greater role in the consistent use of hearing protection devices than other factors such as risk of developing noise-induced hearing loss. The study highlighted the challenges facing wildland firefighters and their management and the need for a comprehensive wildland fire agencies' hearing conservation program especially for firefighters who were exempt based on their occupational designations.

Cannons, small firearms, and starter pistols are sometimes used with blank charges during sporting events, ceremonies, and historical re-enactments. The sound levels produced by such devices are not widely known, and it is possible that the personnel discharging them could underestimate the potential risk to hearing. Depending upon the proximity to participants and spectators, the sound levels produced by such devices can be potentially hazardous. The exposures have not been widely examined because they fall outside of regulations and standards that cover typical occupational or military exposures. This presentation describes the acoustic characteristics and exposure limits for two large-caliber ceremonial cannons and a signal cannon. The cannons produced impulses between 150 and 174 dB peak sound pressure level (SPL) and 8-h equivalent A-weighted levels ranging between 60 and 108 dBA, respectively. In addition, measurements from small firearms and starter pistols are presented, which produced sound levels between 145 and 165 dB SPL. Such sound levels exceed the various occupational recommended and permissible exposure limits. This paper provides recommendations for noise and administrative controls for all personnel within 15 m of the ceremonial cannons and 10 m of the signal cannon. Double hearing protection should be required during all activities.

Introduction In 2013, NIOSH received requests from stakeholders to evaluate sound measurement mobile applications (apps) and their potential to characterise occupational noise exposures. NIOSH researchers conducted the first ever evaluation of such apps, and in 2014, published their findings in The Journal of Acoustical Society of America. NIOSH simultaneously promoted the study on the NIOSH science blog, the NIOSH social media, and other communication channels. Methods A plan was developed in collaboration with NIOSH communication staff to disseminate the latest findings on the NIOSH studies and product development through various social media and communication channels. The plan included the use of the NIOSH science blog, Twitter, Facebook, Instagram, NIOSH e-Newsletter, and promoting the new content to national OSH and media outlets. Results The NIOSH peer-reviewed journal articles became the most frequently read and downloaded JASA articles, the science blog is the all-time most viewed NIOSH science blog, and a top engagement and viewed topic on NIOSH social media channels. As a result of the continuous interaction with stakeholders through the science blog and social media, and to address the need for an occupationally-centric noise exposure app, NIOSH started working on a sound level metre app aimed at the safety and health professional. In January 2017, we launched the NIOSH Sound Level Metre (SLM) mobile application for iOS devices. The app is already the most downloaded and fastest-adopted NIOSH mobile application, with more 70 000 downloads in 6 months. Conclusion The successful launch and adoption of the NIOSH SLM app demonstrates the value of collaboration between NIOSH scientific and communication staff and the importance of continuous engagement between NIOSH researchers and its stakeholders.

Smartphones have evolved into powerful devices with computing capabilities that rival the power of personal computers. Any smartphone can now be turned into a sound-measuring device because of its built-in microphone. The ubiquity of these devices allows the noise measuring apps to expand the base of people being able to measure noise. Many sound measuring apps exist on the market for various mobile platforms, but only a fraction of these apps achieve sufficient accuracy for assessing noise levels, let alone be used as a replacement for professional sound level measuring instruments. In this paper, we present methods and results of calibrating our in-house developed NoiSee sound level meter app according to relevant ANSI (American National Standards Institute) and IEC (International Electrotechnical Commission) sound level meter standards. The results show that the sound level meter app and an external microphone can achieve compliance with most of the requirements for Class 2 of IEC 61672/ANSI S1.4-2014 standard.

This paper describes a study conducted at U.S. Marine Corps Base Quantico to determine firing range impulse noise levels and assess noise exposures. Measurements were performed with M16 rifles at an outdoor firing range using a 113-channel array of 6.35 and 3.18 mm microphones that spanned potential locations for both shooters and instructors. Data were acquired using 24-bit cards at a sampling rate of 204.8 kHz. Single weapon measurements were made with and without an occupied range, with a shooter and with a remotely triggered gun stand. In addition, measurements were made with multiple shooters to simulate exposures for a realistic range environment. Results are shown for the various range configurations as a function of angle and distance. Analyses include waveforms, spectra, and peak levels, as well as the 100 ms A-weighted equivalent levels required by military standard MIL-STD-1474E. [Sponsored by US Office of Naval Research.]

In-the-ear hearing protectors are often used in high-noise environments, such as in military operations and during weapons training. The fit of such a hearing protection device in the ear canal can cause significant variability in the noise dose experienced, an important factor in characterizing the auditory health risk. Our approach for improved dose estimates under these conditions involves a portable noise recorder used to capture in-the-ear noise behind a hearing protector, and on-body noise, while assessing hearing protection fit throughout recording. In this presentation we describe evaluation of this system using ANSI S12.42 testing using a shock tube and an acoustic test fixture, to evaluate impulse peak reduction and for measurement validation. Also explored were angle dependent effects on the peak insertion loss and measurement accuracy of the on-body recorder. An exploratory study was conducted with a small sample of experimenters during a recent Navy-sponsored noise survey conducted at Marine Corps Base Quantico. Results will be presented from a shooter’s ear and a nominal instructor’s position as well as from bystanders observing at a firing range. Our results show good correspondence between behavioral fit-testing of insertion loss and estimated protection from the in-ear and on-body microphones. [Work supported by ONR.]

Introduction The aim of this exploratory study was to examine whether the kurtosis metric can contribute to investigations of the effects of combined exposure to noise and solvents on human hearing thresholds. Methods Twenty factory workers exposed to noise and solvents along with 20 workers of similar age exposed only to noise in southern China were investigated using pure-tone audiometry (1000–8000 Hz). Exposure histories and shift-long noise recording files were obtained for each participant. The data was used in the calculation of their Cumulative Noise Exposure (CNE) which was adjusted using the kurtosis data recorded for each worker. Passive samplers were used to collect solvent concentrations for each worker exposed to solvents over the full work shift. Results We observed an interaction between noise exposure and solvents for the hearing threshold at 6000 Hz. This effect was observed only when the CNE level was adjusted by the kurtosis metric. Disclaimer The views expressed in this publication are those of the authors and do not necessarily represent the views of the National Institute for Occupational Safety and Health.

The aim of this exploratory study was to examine whether the kurtosis metric can contribute to investigations of the effects of combined exposure to noise and solvents on human hearing thresholds. Twenty factory workers exposed to noise and solvents along with 20 workers of similar age exposed only to noise in eastern China were investigated using pure-tone audiometry (1000-8000 Hz). Exposure histories and shift-long noise recording files were obtained for each participant. The data were used in the calculation of the cumulative noise exposure (CNE) and CNE adjusted by the kurtosis metric for each participant. Passive samplers were used to measure solvent concentrations for each worker exposed to solvents over the full work shift. Results showed an interaction between noise exposure and solvents for the hearing threshold at 6000 Hz. This effect was observed only when the CNE level was adjusted by the kurtosis metric.

Wildland fire fighters use many tools and equipment that produce noise levels that may be considered hazardous to hearing. This study evaluated 174 personal dosimetry measurements on 156 wildland fire fighters conducting various training and fire suppression tasks. Noise exposures often exceeded occupational exposure limits and suggest that wildland fire fighters may be at risk of developing noise-induced hearing loss, particularly those operating chainsaws, chippers, and masticators. The authors recommend a comprehensive approach to protecting these fire fighters that includes purchasing quieter equipment, noise and administrative controls, and enrolling these fire fighters into a hearing conservation program.

This follow-up study examines the accuracy of selected smartphone sound measurement applications (apps) using external calibrated microphones. The initial study examined 192 apps on the iOS and Android platforms and found four iOS apps with mean differences of ±2 dB of a reference sound level measurement system. This study evaluated the same four apps using external microphones. The results showed measurements within ±1 dB of the reference. This study suggests that using external calibrated microphones greatly improves the overall accuracy and precision of smartphone sound measurements, and removes much of the variability and limitations associated with the built-in smartphone microphones.

Occupational noise exposure is one of the most frequent hazards present in the workplace; up to 22 million workers have potentially hazardous noise exposures in the U.S. As a result, noise-induced hearing loss is one of the most common occupational injuries in the U.S. Workers in manufacturing, construction, and the military are at the highest risk for hearing loss. Despite the large number of people exposed to high levels of noise at work, many occupations have not been adequately evaluated for noise exposure. The objective of this experiment was to investigate whether or not iOS smartphones and other smart devices (Apple iPhones and iPods) could be used as reliable instruments to measure noise exposures. For this experiment three different types of microphones were tested with a single model of iPod and three generations of iPhones: the internal microphones on the device, a low-end lapel microphone, and a high-end lapel microphone marketed as being compliant with the International Electrotechnical Commission's (IEC) standard for a Class 2-microphone. All possible combinations of microphones and noise measurement applications were tested in a controlled environment using several different levels of pink noise ranging from 60-100 dBA. Results were compared to simultaneous measurements made using a Type 1 sound level measurement system. Analysis of variance and Tukey's honest significant difference (HSD) test were used to determine if the results differed by microphone or noise measurement application. Levels measured with external microphones combined with certain noise measurement applications did not differ significantly from levels measured with the Type 1 sound measurement system. Results showed that it may be possible to use iOS smartphones and smart devices, with specific combinations of measurement applications and calibrated external microphones, to collect reliable, occupational noise exposure data under certain conditions and within the limitations of the device. Further research is needed to determine how these devices compare to traditional noise dosimeter under real-world conditions.

Worldwide adoption rate for smartphones is expected to hit 2 billion devices by 2015. As of the end of 2013, smartphone ownership in the U.S. market has reached more than 67% of all mobile subscribers, or more than 140 million devices. Apple iOS and Google Android platforms account for 93% of those devices [Nielsen, 2014]. Smartphones have evolved into powerful computing machines with exceptional capabilities; most now have built-in sensors such as microphones, cameras, global positioning system (GPS) receiver, accelerometers, gyroscopes, and proximity and light sensors. Smartphone developers now offer many sound measurement applications (apps) using the devices' built-in microphone (or through an external microphone for more sophisticated applications). Interest in such sound measurement applications is growing among audio enthusiasts, educators, acoustic and environmental researchers, and the general public.

NIOSH conducted two studies to examine the accuracy of smartphone sound measurement applications (apps). The first study examined 192 sound measurement apps on the Apple (iOS) and Google (Android) platforms. Only 10 iOS apps met our selection criteria for functionality, measurement metrics, and calibration capability. The studies compared the performance of the apps with a reference microphone and with a professional type 1 sound level meter and a type 2 noise dosimeter. The results showed 4 iOS apps with means of differences within ± 2 dB(A) of the reference microphone. The Android-based apps lacked the features and functionalities found in iOS apps and showed a wide variance between the same app measurements on different devices. A follow-up study of the 4 iOS apps using calibrated external microphones (MicW i436 and Dayton Audio iMM-6), showed an even closer agreement with professional meters. Overall, the studies suggest that certain apps may be used for some occupational noise assessments but only if properly calibrated and used within the hardware limits of the mobile devices. NIOSH and EA LAB are collaborating to develop an occupational sound measurement app for iOS devices in an effort to improve awareness of the noise hazards in the workplace.