PredHonk: A Framework to Predict Vehicular Honk Count using Deep Learning Models

Increasing noise pollution levels insinuates the surging number of vehicular honking in the road traffic. Therefore, identifying the different vehicular honks and predicting the honking pattern can give a very good intuition about the noise pollution level in the environment. In this paper, a novel frame-work ‘PredHonk’ is developed that includes raw data sensing, honk identification and honk count forecasting to understand the pollution beforehand in order to avoid and minimize the effect of the pollution on daily human life. Although several researchers have done their research works on noise pollution monitoring and forecasting, but vehicular honk forecasting is not yet done. We collected the raw audio data from road traffic areas. After that, the audio files were converted into spectrogram images, which were fed into the ResNet152 model for honk identification. Identified honks were further used for predicting honk count using RNN, LSTM, Bi-LSTM, GRU models, and our proposed E-LSTM model. Results demonstrate that the proposed E-LSTM has shown good improvement in honk prediction.