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- W4387496790 abstract "Emergency vehicle sirens play a crucial role in ensuring public safety and providing an audible warning to clear the way for emergency vehicles. In this paper, we discuss an audio classification framework for identifying different types of emergency vehicle sirens using recurrent neural network (RNN) architectures. Training and testing is conducted on the proposed data set called sireNNet consisting of 1675 audio clips covering different types of emergency vehicle sirens including ambulance, firetruck, and police car sirens. A control class of traffic noise is also present in the data set. The results show that out of the three neural network architectures, namely CNN, LSTM, and GRU, RNN-based models achieved better accuracy. The GRU network architecture provided a high accuracy of 98.80% in classifying the different types of emergency vehicle sirens. Furthermore, the results obtained depict the potential of RNN architectures in solving real-world audio classification problems. This work can be useful in developing real-time systems for alerting road users of the approach of emergency vehicles and can contribute to reducing accidents caused by vehicle collisions." @default.
- W4387496790 created "2023-10-11" @default.
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- W4387496790 date "2023-01-01" @default.
- W4387496790 modified "2023-10-12" @default.
- W4387496790 title "Audio Classification of Emergency Vehicle Sirens Using Recurrent Neural Network Architectures" @default.
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- W4387496790 doi "https://doi.org/10.1007/978-981-99-4626-6_6" @default.
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