In this paper we present preliminary results of an audio-based traffic density estimation application, developed within the EU-FP7 project EAR-IT . The algorithm exploits that the energy of environmental noise, generated by vehicles, is related to the prevalent traffic conditions. Noise analysis and derived restrictions were made to improve the solution, which was implemented on an embedded platform. This approach follows the current trends—distributed and local processing—and directly targets the requirements for smart cities and wireless sensor networks. Using traffic monitoring wireless sensors, provided by the testbed SmartSantander , development setup was established to support the audio related algorithm deployment, testing, and assessment.
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