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Recent studies emphasize that vehicular honking contributes to over 50% ofnoise pollution in developing urban and suburban areas. Frequent honkingnegatively impacts health, road safety, and the environment. Recognizing andclassifying different vehicle honks could offer valuable insights intoenvironmental noise pollution. Existing research on outdoor soundclassification and honk detection lacks the ability to classify honks based onvehicle types, limiting contextual information inference for locations, areas,or traffic. Therefore, it becomes imperative to design a system that can detectand classify honks of different types of vehicles from which we can infer somecontextual information. In this paper, we have developed a novel frameworkAClassiHonk that performs raw vehicular honk sensing, data labeling andclassifies the honk into three major groups, i.e., light-weight vehicles,medium-weight vehicles, and heavy-weight vehicles. We collected the raw audiosamples of different vehicular honking based on spatio-temporal characteristicsand converted them into spectrogram images. We have proposed a deeplearning-based Multi-label Autoencoder model (MAE) for automated labeling ofthe unlabeled data samples, which provides 97.64% accuracy in contrast toexisting deep learning-based data labeling methods. Further, we have usedvarious pre-trained models, namely Inception V3, ResNet50, MobileNet,ShuffleNet, and proposed an Ensembled Transfer Learning model (EnTL) forvehicle honks classification and performed comparative analysis. Results revealthat EnTL exhibits the best performance compared to pre-trained models andachieves 96.72% accuracy in our dataset. In addition, we have identified acontext of a location based on these classified honk signatures in a city.

AClassiHonk: A System Framework to Annotate and Classify Vehicular Honk from Road Traffic