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Siren detection and driver assistance using modified minimum mean square error method

Authors: S. L. Kiran; M. Supriya;

Siren detection and driver assistance using modified minimum mean square error method

Abstract

Emergency vehicles always hold a higher priority in traffic. These days it has become difficult to hear the siren of an emergency vehicle from inside a car. Thus, it has become a necessity to ensure the swift movement of these vehicles. The proposed method uses peak detection algorithm coupled with minimum mean square error to detect acoustic siren signals. This method is implemented in frequency domain helps in differentiating the emergency signal from other surrounding noises. The detector performance is analyzed with various real time audios in order to avoid any false alarms. The existence of a siren signal is notified to the driver through visual indication on the dashboard display thus assisting the hearing impaired people. The entire idea is simulated in MATLAB. This system not only helps in reducing the travel time for an emergency vehicle, but also decreases the number of accidents during any such hassle. Thus, the Acoustic Siren Detector System is beneficial to both emergency vehicle and the public.

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    influence
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Powered by OpenAIRE graph
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Top 10%
Top 10%
Average
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