Name: Máté Tóth
Company: Master Student
Presentation Title: Psychoacoustic Characteristics of Non-Linear Automotive Disk Brake Creep Groan (EB2021-STP-011)
Session: NVH Vehicle Applications, Wednesday 19th May 2021, 11:30 - 13:10 (Central European Summer Time - CEST)
What do you find most interesting about the topic of your presentation?
The quality of the simulated cabin noise signal, which was calculated from the measured accelerometer data by using adaptive FIR-filters. We did not expect our results to be this good, especially the simulated roughness of the sound seems really promising.
When discussing this topic with industry peers, what question are you most frequently asked? How do you answer it?
Colleagues from industry and research, especially if they are not into NVH, are mostly interested in the mechanical effects and processes during creep groan. What are the effects, which components vibrate the strongest, how can you solve the problem? Certainly, our experience from numerous experiments and simulations comes in handy when answering these questions.
Who do you think will be most interested in your presentation, and who would you most like to ask questions about it?
I would guess experimental brake engineers should be most interested in this topic, as it could really improve the robustness of psychoacoustic noise evaluations. Vice versa, I would really be interested in the remarks and thoughts of a highly-experienced expert in (psycho-)acoustics.
What specific topics or technology are you hoping to see in other presentations or in the exhibition?
Low-frequency brake noise topics would of course hit the core of our interest as a research group. Regarding technology, I’m specifically interested in further applications of AI methods.
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About Máté Tóth.
Máté Tóth is a Mechanical Engineering master student at Graz University of Technology in Austria with special focus on Business Economics and Automotive Engineering.
He currently works on his master thesis at the Institute of Automotive Engineering. The thesis focuses on low-frequency creep groan noise and its psychoacoustic characteristics, especially its annoyance level for a human vehicle driver. Machine learning and deep learning models are used to get a deeper understanding about the effect of the psychoacoustic parameters on the annoyance level.