In a world increasingly dependent on road-based transportation, it is
essential to understand vehicles. We introduce the AI mechanic, an acoustic
vehicle characterization deep learning system, as an integrated approach using
sound captured from mobile devices to enhance transparency and understanding of
vehicles and their condition for non-expert users. We develop and implement
novel cascading architectures for vehicle understanding, which we define as
sequential, conditional, multi-level networks that process raw audio to extract
highly-granular insights. To showcase the viability of cascading architectures,
we build a multi-task convolutional neural network that predicts and cascades
vehicle attributes to enhance fault detection. We train and test these models
on a synthesized dataset reflecting more than 40 hours of augmented audio and
achieve >92% validation set accuracy on attributes (fuel type, engine
configuration, cylinder count and aspiration type). Our cascading architecture
additionally achieved 93.6% validation and 86.8% test set accuracy on misfire
fault prediction, demonstrating margins of 16.4% / 7.8% and 4.2% / 1.5%
improvement over naïve and parallel baselines. We explore experimental
studies focused on acoustic features, data augmentation, feature fusion, and
data reliability. Finally, we conclude with a discussion of broader
implications, future directions, and application areas for this work.

本研究介绍一种名为 AI 机械师的深度学习系统，利用移动设备捕捉的声音进行声学车辆特性的分析，包括多级神经网络，可用于汽车检测，故障检测和车辆属性识别。通过这种方式，我们使用叠加架构大大提高了系统准确性。