The LIFE+ DYNAMAP project aims at creating dynamic road traffic noise maps automatically upon the levels measured by a low cost sensors network. To ensure these maps reflect the acoustic impact of road infrastructures, it is necessary to exclude other acoustic sources (e.g. aircrafts, industries, etc.) from the noise level computation. To that end, an anomalous noise event detection algorithm based on supervised learning techniques is designed. This paper describes the recording campaign conducted in the two pilot areas of the DYNAMAP project in Rome and Milan, aimed at collecting road traffic and anomalous noise event samples to train the algorithm.