The practicality of using a smartphone as a sound level meter
Bibliographic record
Abstract
Background & Purpose: Sound is of vital importance for human life, it is one of the main forms of communication between people. However, sound that is a nuisance to others is considered noise. Too much noise can be disruptive and affects one’s enjoyment of life and can lead to ill health effects. In some municipalities, bylaw officers or Environmental Health Officers (EHOs) are tasked with enforcing the local noise bylaw. “Sound Level Meters” (SLM) are certified instruments enforcement officers use to accurately measure sound. However, accurate SLMs can be bulky and expensive. In this technological society, almost everyone has some type of smart phone capable of installing applications (apps) that mimic SLMs. The purpose of this project was to determine the accuracy of phone SLM apps compared to real SLMs. Method: Three Android & three iOS SLM apps were downloaded from the internet and installed on two Android and one iOS smartphone. The sound source was computer generated white noise. A type 1 SLM was used to set the white noise to three different sound levels, 80db, 65 dB, & 50 dB. Each Android and iOS smartphone measured the white noise at each sound level utilizing the three different SLM apps. Results were analyzed between the different apps and smartphones. The MANOVA and ANOVA statistical tests were used to analyze the data. Results: All MANOVA and ANOVA tests showed statistically differences between the apps and the SLM (p=0.00000). The power for all MANOVA tests was 100%, therefore there is confidence that the findings reflect the truth and there really is a difference between the different applications, smartphones, and interaction of applications and smartphones. Therefore, the smartphone/app combination tested were not able to replicate the noise level as measured by the SLM. Conclusion: It can be concluded that any individual Android SLM application can have significantly different mean decibels values across different Android smartphones. Different Android smartphones can also have significantly different means decibels across different Android applications. Results for iOS smartphones can only indicate significant mean decibels across the different SLM applications. Therefore, it is not recommended that smartphones with sound level measuring apps be used in place of SLMs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.016 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".