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About this paper
Philipp Diel, Zhuo Cao, Arne Hildenbrand, Marco Günther, Stefan Pischinger, Hanno Scharr, Steffen Marburg
The authors develop a learned method to separate electric-vehicle cabin recordings into wind, tire, powertrain, and auxiliary-noise contributions. It adapts HT Demucs and trains on labeled mixtures generated by a physics-based simulator, including varied virtual vehicle hardware. It separates sources in synthetic tests, but measured-vehicle results remain unsatisfactory, so synthetic training alone does not provide reliable real-world decomposition.
Summary by alphaXiv.
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