In this paper we present preliminary results of an audio-based traffic density estimation application, developed within the EU-FP7 project EAR-IT [1]. 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 [2], development setup was established to support the audio related algorithm deployment, testing, and assessment.
https://www.aes.org/e-lib/browse.cfm?elib=17154
Click to purchase paper as a non-member or login as an AES member. If your company or school subscribes to the E-Library then switch to the institutional version. If you are not an AES member and would like to subscribe to the E-Library then Join the AES!
This paper costs $33 for non-members and is free for AES members and E-Library subscribers.
Learn more about the AES E-Library
The Engineering Briefs at this Convention were selected on the basis of a submitted synopsis, ensuring that they are of interest to AES members, and are not overly commercial. These briefs have been reproduced from the authors' advance manuscripts, without editing, corrections, or consideration by the Review Board. The AES takes no responsibility for their contents. Paper copies are not available, but any member can freely access these briefs. Members are encouraged to provide comments that enhance their usefulness.
Start a discussion about this paper!
