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