Summary
Electric vehicle cabins contain different types of noises like wind, tire, electric drive, and auxiliary noises, all mixed together. The paper presents a method to separate these mixed noises into their original parts using a special AI model called a transformer. The model uses information about how the vehicle is running to better identify each noise source. Because real-world examples of separated noises are hard to get, the authors trained their model on synthetic data created by simulating different vehicle sounds. Tests on both simulated and real vehicle sounds show this method can help engineers analyze vehicle noise more effectively.
Authors
Philipp Diel, Zhuo Cao, Arne Hildenbrand, Marco Günther, Stefan Pischinger, Hanno Scharr, Steffen Marburg
Abstract
The interior sound of an electric vehicle is a superposition of several distinct noise contributions - wind noise, tire noise, powertrain (electric drive unit and inverter) noise, and auxiliary-drive noise - whose relative balance governs both the overall loudness and the perceived sound quality in the vehicle interior. Decomposing a recorded vehicle interior signal into these source contributions is a prerequisite for targeted acoustic engineering, but requires effortful measurement campaigns as well as expertise in the field of transfer path analysis. This work formulates the decomposition as a supervised, binaural source-separation problem with four target source contributions and adapts a hybrid time-spectrogram transformer architecture, originally developed for music demixing, to the domain of sound source separation for technical systems. To disambiguate source contributions, conditioning is introduced in the form of operating-state information, including electric drive speed, electric drive load, vehicle speed, and auxiliary-drive speed. Since isolated real-world reference stems are costly to obtain, the model is trained on a physics-based parametric data generator that synthesizes labeled component sounds from randomized virtual-vehicle hardware configurations and driving scenarios, so that the ground-truth contribution of each source is known by construction. Validation on both synthetic and measured vehicle interior sounds probes the achieved separation performance and underscores the feasibility of applying the proposed method for acoustic contribution analysis in technical systems.