@inproceedings{00a532bcb6af4a6895873ad91775075d,
title = "Large-Scale Acoustic Automobile Fault Detection: Diagnosing Engines Through Sound",
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.",
keywords = "audio, classification, engine fault detection, multi-modal feature fusion, vibration",
author = "Dennis Fedorishin and Justas Birgiolas and Mohan, \{Deen Dayal\} and Livio Forte and Philip Schneider and Srirangaraj Setlur and Venu Govindaraju",
note = "Publisher Copyright: {\textcopyright} 2022 ACM.; 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022 ; Conference date: 14-08-2022 Through 18-08-2022",
year = "2022",
month = aug,
day = "14",
doi = "10.1145/3534678.3539066",
language = "English",
series = "Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
publisher = "Association for Computing Machinery ",
pages = "2871--2881",
booktitle = "KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining",
address = "United States",
}