Supervised Source Separation of Electric Vehicle Interior Noise Using a Hybrid Transformer Architecture

Philipp Diel; Zhuo Cao; Arne Hildenbrand; Marco Günther; Stefan Pischinger; Hanno Scharr; Steffen Marburg

cs.SDOct 23, 2026

Supervised Source Separation of Electric Vehicle Interior Noise Using a Hybrid Transformer Architecture

Authors: Philipp Diel, Zhuo Cao, Arne Hildenbrand, Marco Günther, Stefan Pischinger, Hanno Scharr, Steffen Marburg

Organizations: Chair of Thermodynamics of Mobile Energy Conversion Systems, RWTH Aachen University, Aachen, Germany · IAS-8, Forschungszentrum Jülich, Jülich, Germany · Chair of Vibroacoustics of Vehicles and Machines, Technical University of Munich, Garching, Germany

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.

Figures & tables

Abbrev.	Conditioning	Loss Norm.	$L_{\mathrm{STFT}}$	Description
UC-BASE	–	–	–	Baseline: unconditioned, unnormalized waveform loss, no spectrogram loss
UC-N	–	✓	–	Unconditioned with normalized waveform loss
UC-STFT	–	–	✓	Unconditioned, unnormalized waveform loss, with auxiliary spectrogram loss
UC-N-STFT	–	✓	✓	Unconditioned, normalized waveform loss, with auxiliary spectrogram loss
FiLM	FiLM	–	–	FiLM-conditioned, unnormalized waveform loss
FiLM-N	FiLM	✓	–	FiLM-conditioned with normalized waveform loss

Table 1: Overview of model variants differing in conditioning, waveform-loss normalization, and use of an auxiliary spectrogram loss.

Figure 1: SDR separation results for single virtual vehicle.

Figure 2: Spectrograms for separation of source contributions of a single virtual vehicle. Experiment UC-STFT.

Figure 3: Spectrograms for conditioned separation of source contributions of a single virtual vehicle. Experiment FiLM-N-STFT.

Figure 4: Spectrograms for separation of source contributions for one of multiple virtual vehicles. Experiment UC-STFT.

Panel of Figure 5 — Figure 5: Source contribution spectrograms for real vehicle interior noise measurements recorded in [ 43 ] . As the mixtures are normalized before inference, the magnitude scale is relative and does not reflect measured decibel values.

Property	Scope	Value Range	Distribution
Tire exterior diameter [m]	per vehicle	$[0.60,\,0.75]$	Uniform
Gear ratio	per vehicle	$[7.5,\,11.5]$	Uniform
Pole pairs $p$	per vehicle	$\{3,4,5\}$	Discrete uniform
Stator slots $N_{\text{slots}}$	per vehicle	$\{36,48,72\}$	Discrete uniform
Gear teeth count $N_{\text{teeth}}$	per vehicle	$[80,100]$	Uniform, integer
Inverter switching freq. $f_{sw}$ [Hz]	per vehicle	$[7000,\,14000]$	Uniform

Table 2: Randomly sampled properties, their value ranges and underlying distributions.

Operation Parameter	Affected Source(s)	Mechanism / Effect
Vehicle speed $v(t)$ [km/h]	Powertrain, Inverter	Sets drive unit rotational speed (see below)
	Tire, Wind (broadband)	Broadband noise (sum of tire and wind) amplitude scales linearly with vehicle speed [ 4 ]
	Wind only	Low-speed roll-off via sigmoid $1/(1+e^{-0.2(v-20)})$ gradually suppresses wind noise below $\sim$ 50 km/h
	Cavity tone (tire sub-component)	Resonance frequency shifts with speed: $f_{\text{cav}}=f_{\text{base}}+0.1\,(v-60$ km/h $)$ ; amplitude scales relative to tire RMS
Load factor $L(t)\in[0,1]$ (derived)	Powertrain	Time-varying, constructed with speed-dependence and acceleration-dependence. Modulates amplitude of all powertrain orders.
Drive unit rotational speed $f_{\text{mech}}$ (derived from $v$ via gear ratio and wheel diameter)	Powertrain, Inverter	Determines exact excitation frequencies: mechanical orders $f_{\text{m}}$ = $n_{m}\cdot f_{\text{mech}}$ ; electrical orders $f_{\text{e}}$ = $n_{e}\cdot f_{\text{mech}}\cdot p$ ( $p$ = pole pairs); slot order $f_{\text{slot}}$ = $f_{\text{mech}}\cdot N_{\text{slots}}$ ; gear mesh order $f_{\text{gear}}$ = $f_{\text{mech}}\cdot N_{\text{teeth}}$ ; inverter sidebands $f_{\text{sb}}$ = $f_{sw}\pm j\cdot f_{\text{mech}}\cdot p$ (sidebands $j=1,\dots,N_{sb}$ ); all order amplitudes increase with higher rotational speeds

Table 3: Influence of vehicle operation parameters on the synthesized sound sources.

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Audio Source Separation

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Audio source separation models must preserve fine spectral structure while keeping the cost of the separator tractable. Band-split architectures reduce this cost by manually partitioning the spectrum into predefined frequency bands and encoding each band as a token, but the same boundaries are used for every mixture. Because informative spectral structure varies with source content, fixed boundaries can merge discriminative components or spend tokens where little resolution is needed. We introduce Dynamic Band Splitting (DBS), an end-to-end framework that adapts boundary locations and band widths to each mixture under a fixed band-token budget. DBS predicts content-conditioned boundary scores within frequency blocks, selects a prescribed number of boundaries using hard Top-

K

, and applies content-dependent pooling to obtain compact band tokens. After separation, it smooths and expands these tokens to the original STFT grid, refines them along frequency, and decodes complex masks with a frequency-shared head. DBS improved separation over the corresponding static models on DnR v2, MUSDB18-HQ, and EchoSet, with DBS-BSRNN achieving relative gain of 29.0% in average SI-SDR on DnR v2. These results and boundary analyses on DnR v2 show that content-adaptive frequency partitioning benefits multiple separator backbones under a fixed band-token budget. Code and audio demos are available at https://cslikai.cn/Dynamic-Band-Splitting

Abstract

Audio Source Separation

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