Feature Extraction of Engine Sound with Time Variability by Machine Learning

Yota Oshima･Soichiro Tanabe･Takeshi Toi (Chuo University)

The time-frequency analysis images are classified based on a machine learning model that combines a Convolutional Neural Network and a Quasi-Recurrent Neural Network, and features in each frequency component of engine sound with time variability are visualized by using Gradient-weighted Class Activation Mapping, which is a middle layer extraction method of a neural network.

A Study on Machine Learning and Preprocessing of Parts Operating Sound Data to Establish Quantitative Standards Evaluation for Automotive Parts Noise

Sang Heon Wang･Nak Kyoung Kong･Dong Eun Cha･Ho Wan Jang (Hyundai Motor)

As the electrification of automobiles and the mobility revolution, the noise of the interior space is becoming important. Due to these factors, the standards for operating sound of parts are also becoming stricter. But in most cases, the standards depend on emotional evaluations that human hear and judge. In this paper, we propose a method to establish quantitative standard evaluations for emotional evaluations as a process for performing machine learning after pre-processing frequency analysis of operating sound. Using this method, it is possible to establish quantitative standard evaluations for the operation sound of parts without human intervention.

Prediction of High Frequency Noises of an EV using Machine Learning
-Machine Learning for the Prediction of Both Structure-Borne and Airborne Noises-

Ji Woo Yoo･Yong Dae Kim (Hyundai Motor)･Kwangsoo Yoon･Chanhee Jeong･Hyosik Jung･Dohyeon Oh (Hexagon Korea)

Whine noise of an electric vehicle occurs due to electro-magnetic force or gears in an e-motor, which lies usually at higher frequencies. This paper deals with a proof of concept for machine learning methods predicting the whine and shows the relevant procedure of machine learning. The study investigates two topics: efficient encapsulation on the e-motor to handle acoustic radiation (airborne), and prediction of whine noise in the cabin (structure-borne). Particularly a novel superposition method is proposed to quickly realize various combination of encapsulation. It is shown that machine learning could be a good alternative to predict high frequency whine but also reveals weak points.