Large-Scale Acoustic Automobile Fault Detection: Diagnosing Engines Through Sound

Dennis Fedorishin, Justas Birgiolas, Deen Dayal Mohan, Livio Forte, Philip Schneider, Srirangaraj Setlur, Venu Govindaraju

Abstract: In this paper we present AMPNet, an acoustic abnormality detection model deployed at ACV Auctions to automatically identify engine faults of vehicles listed on the ACV Auctions platform. We investigate the problem of engine fault detection and discuss our approach of deep-learning based audio classification on a large-scale automobile dataset collected at ACV Auctions. Specifically, we discuss our data collection pipeline and its challenges, dataset preprocessing and training procedures, and deployment of our trained models into a production setting. We perform empirical evaluations of AMPNet and demonstrate that our framework is able to successfully capture various engine anomalies agnostic of vehicle type. Finally we demonstrate the effectiveness and impact of AMPNet in the real world, specifically showing a 20.85% reduction in vehicle arbitrations on ACV Auctions' live auction platform.