Noise exposure has been the emerging environmental factor for human health. Yet an accurate and large-scale sound monitoring network is not available due to the expense of high-quality professional sound level meters and poorly-calibrated low-cost noise sensors. In this work, we propose a participatory sound meter calibration using smartphones. The system employs a low-cost and open-sourced calibration station to conduct side-by-side sound measurements, and all the measurement data are uploaded to the open data portal to build calibration models for different phone brands and models. We show that, using our calibration models, the MAE of calibration performance can be reduced significantly from 12.4 dbA to 2.8 dbA for the same device and 3.3 dbA for the other device of the same phone model. The results of this study can benefit crowdsourcing-based large-scale sound measurements and facilitate noise exposure, public health, and smart city researches in the future.