Sound Quality Evaluation Difference to Vehicle Interior Noise between Automatic and Manual Driving Condition

Yudai Irie･Nawoki Okabayashi･Junji Yoshida (Osaka Institute of Technology)

In near future, autonomous vehicles are expected to be more popular due to the technology development. This transition from active to passive driving condition may change the drivers’ impression to vehicle interior sound. In this study, we investigated whether drivers’ impression to interior sound change or not according to the condition through subjective evaluation test using a driving simulator. As the result, interior sounds were found to be evaluated as more discomfort at passive condition than that at active condition.

Feature Extraction of Car Driving Sound using Neural Network with Acoustic Multi-parameters

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

Time-frequency analysis, correlation between carrier frequency and modulation frequency, and multiple psychoacoustic metrics are defined as acoustic multi-parameters, and a neural network that simultaneously inputs these parameters is used to classify car driving sounds. The data classification contribution of each parameter and the characteristics of the sound for that parameter are extracted by visualizing the data classification basis of the neural network.

Study of Engine Operating Point Optimization Incorporating Human Cognitive Processing (2nd Report)
-Comparison of Cognitive Processing Characteristics for Engine Noise during Driving between Japan and U.S.-

Shimpei Nagae･Takaaki Yamanaka･Toshio Enomoto (Nissan Motor)

The authors showed that the evaluation of the engine noise of e-POWER varies depending on various cognitive influences during driving, and utilized for optimization of the operating point. In this report, we developed a virtual e-POWER engine noise environment that can be installed in BEVs, and collected and compared engine noise evaluation data during driving in Japan and U.S. to investigate whether there are any differences in such changes by country.

Development of Automatic Evaluation System for BSR

Kazutaka Yonemori･Shoki Tsuchibuchi･Takaaki Yamanaka･Yoshinari Tokunaga･Yohei Kurami･Takayuki Kume (Nissan Motor)

In our company, the load cycle testing to reproduce and evaluate BSR (buzz, squeak, and rattle) comes to be long our testing. It is a hard for evaluators to confirm BSR throughout the entire duration of the testing. In this report, we developed the automatic evaluation system to achieve BSR detection throughout the entire duration of the testing.