DeHonk: A deep learning based system to characterize vehicular honks in presence of ambient noise

Noise pollution, a growing problem in urban cities, causes severe and long-term health problems. Vehicles on the road and ceaseless honking add to incessant vehicular assault and are the main reasons behind increasing noise pollution. However, analyzing and characterization of honking is not an easy task, and it varies with road traffic, vehicle types, speed, etc. In this paper, we propose a framework DeHonk that detects vehicular honks from traffic noise and analyzes various honking features to build a honking profile using only audio samples, which can be further used to infer the context of a particular location, design micro-services, etc. However, the existing methods used only fast fourier transform(FFT) or mel-frequency cepstral coefficients(MFCC) in honk identification, the accuracy lies between 40% to 60% in the presence of ambient noise. Similarly, we failed to obtain good accuracy in recognizing honks at noisy environments by applying existing sound classification models. In the proposed work, we have modeled the sound signal as a spectrogram image to detect vehicular honks. We have collected audio data by traveling 461km of road from various spatio-temporal locations in Durgapur, a sub-urban city in India. Furthermore, the generated spectrogram images are pre-processed, labeled, and fed into various deep learning models for training and testing. In this study, convolutional neural network(CNN), visual geometry group with a very deep convolutional network(VGGNets), efficient convolutional neural networks for mobile vision applications(MobileNet), residual neural network(ResNets), and Inception V3 models are used to train the system to identify honks and honk related features. Experiment results demonstrate that the ResNet152 model has achieved higher accuracy (97.69%) in honk detection as compared to other models. Furthermore, as a micro-service, a honk-aware route recommendation system is developed based on the derived honking features.