Evaluation of Smartphone Sound Measurement Apps

Transcription

1 Evaluation of Smartphone Sound Measurement Apps Chucri (Chuck) A. Kardous, M.S., P.E. Peter B. Shaw, Ph.D. U.S. Department of Health and Human Services Centers for Disease Control and Prevention National Institute for Occupational Safety and Health Cincinnati, Ohio Disclaimer: The findings and conclusions in this study are those of the authors and do not necessarily represent the views of the National Institute for Occupational Safety and Health

2 Background Smartphone penetration expected to hit 2 billion in % of all mobile phones purchased in U.S. were smartphones > 140 million users Apple ios and Google Android platforms account for 93% of smartphones purchased in 2013 Hundreds of sound measurement apps available Most are free: Either ad-supported or limited features, fee to upgrade to full features. Typical cost ~ $0.99- $3.99 though some sophisticated apps cost more

3 Crowdsourcing environmental noise! (NoiseWatch Real-time data collection and sharing using GPS!

4 Crowdsourcing environmental noise! (NoiseTube The NoiseTube mobile app extends the current usage of mobile phones by turning them into noise sensors, enabling citizens to measure the sound exposure in their everyday environment. Furthermore each user can participate in creating a collective map of noise pollution by sharing geo-localized measurement data with the NoiseTube community.

5 Why NIOSH? NIOSH Science Blog on noise at 2012 Super Bowl: Tweet noise levels! Requests about accuracy and recommendations Can they be used in an occupational setting? NIOSH initiatives: Worker Empowerment and Direct Reading Instruments

6 Study Considerations Limited number of devices acquired To calibrate or not calibrate? Smartphone built-in mics Standards and occupational guidelines for type 2 sound measurement instruments App and device selection criteria

7 Device/App selection Representative selection of devices: 4 different ios models, 4 Android models, 2 Windows models Selection Criteria: - A-weighted & unweighted (C/Z/flat) sound pressure levels - 3-dB and 5-dB exchange rates - Average/equivalent sound level or time-weighted average (Leq/Lavg or TWA) - Calibration adjustment 10 ios apps (out of 130) and 4 Android apps (out of 62) 2 cross-platforms apps selected

8 Experimental Setup ios apps selected and tested App Developer Features Adv Decibel Meter Amanda Gates A/C/SPL weighting, Int/Ext Mic, Calibration Decibel Meter Pro Performance Audio A/C/Z weighting, Calibration ispl Pro Colours Lab A/C/SPL weighting, Calibration Noise Hunter Inter.net2day A/C/SPL weighting, Int/Ext Mic, Calibration NoiSee IMS Merlini Sistemi A/C/Z weighting, ISO/OSHA Dose, Calibration Sound Level Meter Mint Muse A/C/SPL weighting, Calibration SoundMeter Faber Acoustical A/C/SPL weighting, Leq, Int/Ext Mic, Calibration (Real) SPL Meter BahnTech A/C/SPL weighting, Calibration SPL Pro Andrew Smith A/C/SPL weighting, Leq, Int/Ext Mic, Calibration SPLnFFT Fabien Lefebvre A/C/SPL weighting, Leq, Int/Ext Mic, Calibration

9 Experimental Setup Android apps selected and tested App Developer Features SPL Meter AudioControl A/C weighting, Int/Ext Mic, Calibration decibel Pro BSB Mobile Solutions A/C/Z weighting, Calibration db Sound Meter Darren Gates Int/Ext Mic, Calibration Noise Meter JINASYS A/SPL weighting, Calibration Two cross-platforms apps NoiseWatch and NoiseTube (non-commercial apps) available on both ios and Android

10 Experimental Setup Reverberant noise chamber diffuse sound field Pink noise db (20Hz -20kHz) in 5-dB increments Reference sound level: ½ Larson-Davis model 2259 random incidence microphone Noise generation and acquisition: Trident Software by ViAcoustics Larson-Davis model 831 sound level meter (type 1) and model 706 noise dosimeter (type 2)

11 Reverberant Noise Chamber

12 Smartphones and Reference Mic Reference microphone

13 Smartphones and SLM/Dosimeter

14 Statistical Design Difference between noise levels measured reference microphone and apps Determine effect of app, device, and noise level on outcome Analysis of Variance (ANOVA) on differences between apps and reference in A-weighted and unweighted sound levels Small difference = Good agreement Large difference = Poor agreement Difference of zero = Perfect agreement

15 Results A-weighted sound levels by app

16 Results Unweighted sound levels by app

17 Results A-weighted sound levels by device

18 Results Unweighted sound levels by device

19 Results App Sound Level (db) Samsung S3 HTC One X Motorola Droid Samsung Note SPL Meter ± ± ± ± ± ± ± ± ± ± ± ± 1.6 decibel Pro (dba) ± ± ± ± ± ± ± ± ± ± ± ± 1.5 db Sound Meter 70 71±1 80 ± ± ± ±1 91 ± ± ± ± 1 92 ± ± ± 1 Noise Meter ± ± ± ± ± ± ± ± ± ± ± ± 1.2 Unweighted sound levels by Android app

20 Results NoiseWatch: cross-platform app

21 Discussion ios Apps SoundMeter had best agreement in A-weighted sound levels (mean difference of dba from reference) SPLnFFT had best agreement in un-weighted sound levels (mean difference of 0.07 db from reference) Noise Hunter, NoiSee, and SoundMeter had mean differences within ± 2 dba NoiSee, SoundMeter, and SPLnFFT had mean differences within ±2 db

22 For Devices Discussion ios devices iphone 3Gs had best agreement with mean difference of 0.44 db and dba New microphone supplier for iphone 5 and ipad 4 th gen ios 6 allows developers to bypass speech filters and input gain control

23 Discussion Android Apps/Devices Open ecosystem, many manufacturers, many suppliers No statistical significance can be established Same app not consistent across different devices (different manufacturers) Different apps not consistent on same device (developers access to different devices) Android users expectations for free or low priced apps Android developers expectations for lower revenues

24 Occupational Relevancy ANSI S1.4 (specification for sound level meters) states the following: the expected total allowable error for a sound level meter measuring steady broadband noise in a reverberant sound field is approximately ± 1.5 db for a type 1 instrument and ± 2.3 db for a type 2 instrument. OSHA noise standard [29 CFR ] considers type 2 instruments to have an accuracy of ± 2 dba. Several apps and devices were within ± 2 dba!

25 Crowdsourcing of Occupational Noise? Many challenges remain: Accuracy Privacy Dealing with bad or corrupted data Mechanisms for storing and accessing data Motivation to participate in studies Calibration standard

26 Future Research Testing with calibrated devices Using external microphones MicW i436 (type 2 compliant) Occupational noise app, freely distributed Occupational noise exposure database

27 Contact information Chuck Kardous National Institute for Occupational Safety and Health 4676 Columbia Parkway, C27 Cincinnati, Ohio NIOSH science blog: Kardous CA, Shaw PB [2014]. Evaluation of smartphone sound measurement applications, J. Acou. Soc. Am., 135 (3).

28 Source DF ANOVA SS Mean Square F Value Pr > F block noise <.0001 block*noise device <.0001 noise*device <.0001 block*device (noise) app <.0001 noise*app <.0001 device*app <.0001 noise*device*app <.0001 Model <.0001 Error Corrected Total ANOVA on differences between measurements of noise levels from apps and from the lab meter using the dba outcome (R 2 = 0.977)

29 App Mean Standard Error N Tukey Grouping Sound Level Meter A SPL Pro B SoundMeter C NoiSee D Noise Hunter E SPLnFFT F E ispl Pro F Adv Decibel Meter G Real SPL Meter H Decibel Meter pro H Multiple pairwise comparisons (Tukey) of the mean differences in dba for the ten apps