iCal: Intervention-free Calibration for Measuring Noise with Smartphones

It is valuable for the public to get access to real-time noise level information. Unfortunately, it is generally difficult for ordinary people to access real-time noise level information because of limited noise information stations and increased burden of carrying professional noise level meters. Being equipped with a high-quality microphone, a smartphone can potentially serve as a handy noise level meter. However, the straightforward use of sound measurements from smartphones leads to large measurement errors. As a result, it is essential to calibrate a smartphone before it can be used for noise level measurements. Little work has been done on automatic smartphone calibration for noise measurement purposes. In this paper we design a system called iCal for calibrating smartphones for accurate noise level measurements. The system consists of two key components: node-based calibration and crowdsourcing-based calibration. The node-based calibration enables an individual smartphone to do offline calibration, but suffers a slow-start issue. Complementing the node-based calibration, the crowdsourcing-based calibration leverages the power of crowdsourcing to maintain a lookup table, which a smartphone user can consult to find an approximate offset specific to its smartphone model. Thus, the slow-start issue can be effectively mitigated. The salient feature of iCal is human intervention free. We have implemented iCal on the android platform and experimental results show that the calibration error is as low as 3 dbA.