A specific smartphone application was developed to collect perceptive and acoustic data in Paris. About 3400 questionnaires were analyzed, regarding the global sound environment characterization, the perceived loudness of some emergent sources and the presence time ratio of sources that do not emerge from the background. Sound pressure level was recorded each second from the mobile phone’s microphone during a 10-min period. The aim of this study is to propose indicators of urban sound quality based on linear regressions with perceptive variables. A cross validation of the quality models extracted from Paris data was carried out by conducting the same survey in Milan. The proposed sound quality general model is correlated with the real perceived sound quality (72%). Another model without visual amenity and familiarity is 58% correlated with perceived sound quality. In order to improve the sound quality indicator, a site classification was performed by Kohonen’s Artificial Neural Network algorithm, and seven specific class models were developed. These specific models attribute more importance on source events and are slightly closer to the individual data than the global model. In general, the Parisian models underestimate the sound quality of Milan environments assessed by Italian people.