Maity Biswajit, Alim Abdul, Rama Charan Popuri Sree, Nandi Subrata, Bhattacharjee Sanghita
Computer Application and Science, Institute of Engineering and Management, Kolkata, 700091, West Bengal, India.
CSE, National Institute and Technology, Durgapur, 713209, West Bengal, India.
Environ Monit Assess. 2024 Sep 27;196(10):983. doi: 10.1007/s10661-024-13101-3.
Some recent studies highlight that vehicular traffic and honking contribute to more than 50% of noise pollution in urban or sub-urban areas in developing countries, including Indian cities. Frequent honking has an adverse effect on health and hampers road safety, the environment, etc. Therefore, recognizing the various vehicle honks and classifying the honk of different vehicles can provide good insights into environmental noise pollution. Moreover, classifying honks based on vehicle types allows for the inference of contextual information of a location, area, or traffic. So far, the researchers have done outdoor sound classification and honk detection, where vehicular honks are collected in a controlled environment or in the absence of ambient noise. Such classification models fail to classify honk based on vehicle types. Therefore, it becomes imperative to design a system that can detect and classify honks of different types of vehicles to infer some contextual information. This paper presents a novel framework lassi onk 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. Raw audio samples of different vehicular honking are collected based on spatio-temporal characteristics and converted them into spectrogram images. A deep learning-based multi-label autoencoder model (MAE) is proposed for automated labeling of the unlabeled data samples, which provides 97.64% accuracy in contrast to existing deep learning-based data labeling methods. Further, various pre-trained models, namely Inception V3, ResNet50, MobileNet, and ShuffleNet are used 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, context of a location is identified based on these classified honk signatures in a city.
一些近期研究强调，在包括印度城市在内的发展中国家的城市或郊区，车辆交通和喇叭声造成了超过50%的噪音污染。频繁鸣笛对健康有不利影响，并妨碍道路安全、环境等。因此，识别各种车辆喇叭声并对不同车辆的喇叭声进行分类，可以为环境噪音污染提供很好的见解。此外，基于车辆类型对喇叭声进行分类可以推断出一个地点、区域或交通的上下文信息。到目前为止，研究人员已经进行了户外声音分类和喇叭声检测，其中车辆喇叭声是在受控环境或无环境噪音的情况下收集的。这样的分类模型无法根据车辆类型对喇叭声进行分类。因此，设计一个能够检测和分类不同类型车辆喇叭声以推断一些上下文信息的系统变得势在必行。本文提出了一个新颖的框架lassi onk，它执行原始车辆喇叭声感知、数据标注，并将喇叭声分为三大类，即轻型车辆、中型车辆和重型车辆。基于时空特征收集不同车辆喇叭声的原始音频样本，并将其转换为频谱图图像。提出了一种基于深度学习的多标签自动编码器模型（MAE）用于对未标注数据样本进行自动标注，与现有的基于深度学习的数据标注方法相比，其准确率达到了97.64%。此外，使用了各种预训练模型，即Inception V3、ResNet50、MobileNet和ShuffleNet，并提出了一种集成迁移学习模型（EnTL）用于车辆喇叭声分类并进行了对比分析。结果表明，与预训练模型相比，EnTL表现出最佳性能，在我们的数据集中达到了96.72%的准确率。此外，基于城市中这些分类的喇叭声特征来识别一个地点的上下文。
