Automotive audio systems rely on complex signal-processing algorithms. Technologies such as noise reduction, acoustic echo cancellation, beamforming, and in-car communication processing must be meticulously tuned across vehicle platforms and their unique microphone and loudspeaker layouts. This optimization process requires constant evaluation of where and how specific algorithm parameters alter the output. Pinpointing these deviations remains a major challenge, as no existing tool is designed for such granular comparison. We introduce a comparison methodology that adapts the blink comparator — an instrument invented in astronomy over a century ago and used to discover Pluto — to the analysis of spectrograms of automotive audio signals. This technique rapidly alternates between aligned spectrograms of different processing variants in a flicker-free display, making differences in time, frequency, and intensity immediately visible, even when scalar metrics such as Echo Return Loss Enhancement or wideband Signal to Noise ratio improvement indicate similar performance. To further expose subtle changes that might otherwise remain hidden, the method incorporates spectral difference masking, which highlights local spectral deviations between processing variants. We also extend the classical visual blink comparator with synchronized audio playback switching, enabling the engineer to see and hear the difference simultaneously. We demonstrate its effectiveness through automotive case studies involving noise reduction tuning, echo cancellation artifacts, and beamformer comparisons.
We propose a machine-learning method that predicts trained-listener panel ratings of vehicle audio systems from in-situ microphone-array measurements. Using a dataset collected over more than 10 years from 1177 vehicle audio systems, the model is trained to predict listener-score distributions, not only mean scores, for subjective sound-quality attributes on a 1–10 scale. In this paper we focus on the timbral/spectral attribute and evaluate performance on a held-out set of 237 systems. Results show that prediction accuracy improves consistently with dataset size and that distribution-aware modelling captures not only expected score level but also listener disagreement and uncertainty. The study highlights both the feasibility of virtual listener panels and the importance of large-scale, reliable benchmarking data.
A method is described for measuring and comparing the perceived sound quality of vehicle audio systems in situ. The process has been refined through use at company facilities for over two decades and has been applied across a variety of internal use cases within the organization. Panels of experienced evaluators, aligned processes, and dedicated facilities are used at multiple sites around the globe to support consistent data collection. The collected data are benchmarked against a periodically refreshed reference population of recent vehicle evaluations, allowing each vehicle to be positioned relative to the contemporary competitive landscape. Hundreds of current vehicle models are available for comparison at any given time. The resulting information is used within the organization to support sound system target setting and validation, competitor analysis, sales pursuits, and product development and prototyping. More recently, the method has also been explored as a basis for sound-quality predictor modeling. This paper describes the overall method, the associated analysis workflow, and a set of anonymized case studies intended to illustrate practical comparability and discrimination in routine use.
A core challenge of acoustically optimizing an automotive sound system is realizing a balance between ideal and viable loudspeaker placement. Placement is largely influenced by structural, aesthetic and safety design considerations leading to non-ideal acoustic response characteristics within a given car cabin. These constrains often force drive units to be oriented off-axis from the listening region. To fully benefit from a transducer’s performance, the on-axis response is desirable at the listening location. This work presents the design and evaluation of design of broadband acoustic meta-material lenses to perform beam steering on an incoming wavefront. The parameters of the meta-material lens are learned via a differentiable acoustic simulation, evaluating the far-field directivity of the lens by solving the inhomogeneous Helmholtz equation via Fourier spectral methods coupled with a far field approximation of the boundary integral method. To realise a physical lens from the theoretical lens, a differential evolutionary algorithm is used to optimize the geometry of a parametric 2D model for each element of the metamaterial lens’s cross-section, matching the target effective density and bulk modulus via Finite Element Method (FEM) analysis. The performance of the whole lens is then evaluated via FEM analysis. After this, resin 3D printing is used to construct the metamaterial lens which is used to verify the results in real world.
The automotive industry is rapidly migrating from 12 V to 48 V electrical architectures to support growing power demands from compute and electrified subsystems. Audio amplifiers appear well positioned to benefit through reduced current, lower conductor losses, increased instantaneous power capability, and an additional 12 dB of voltage headroom for improved transient reproduction in non-boosted amplifier systems. However, directly scaling conventional Class D amplifier architectures and ICs to 48 V introduces significant and often underestimated challenges. Usage weighted analysis of real audio content shows that over 95% of playback occurs below 5 W output power, where conduction losses are minimal and voltage dependent switching losses dominate efficiency and thermal behavior. The resulting impacts on efficiency, EMI, noise floor, thermal design, and system architecture motivate a re examination of long standing assumptions for next generation 48 V automotive audio systems.
Engine order enhancement is central in automotive sound design, where selective harmonics are synthesized to shape perceptual qualities such as sportiness, refinedness, or power. This paper investigates a neural network-based approach to combustion engine sound modeling that extends conventional engine order analysis and enhancement by deriving synthesis parameters from audio data with machine learning and incorporating stochastic components into the synthesis framework. The system parameterizes engine sounds as a compact representation capturing per-order and broadband timbral variation across the full RPM-torque operating range, while remaining manually tunable and compatible with established automotive audio frameworks. The approach leverages gradient-based optimization and analysis-by-synthesis through an end-to-end differentiable implementation. The resulting synthesis parameter set is directly transferable to conventional DSP implementations for deployment on embedded targets. Spectral metrics and listening tests confirm high reconstruction fidelity, and integration into an established automotive audio framework EVx Suite demonstrates technical feasibility on deployment-ready embedded systems.
Automotive audio systems require precise spectral tuning to achieve consistent sound quality across vehicle models, trim levels, and seating positions. Cabin acoustics introduce strong resonances and seat-dependent responses, while manufacturing tolerances, loudspeaker variability, and interior materials create additional differences between vehicles. As modern infotainment platforms incorporate multi-speaker playback, active sound design, speech communication, and personalized audio processing, manual equalization becomes impractical for production environments. This paper presents an automatic parametric equalization approach for production calibration of automotive audio systems. The method estimates the minimal number and determines the parameters of a cascade of biquad filters such that the resulting frequency response matches a predefined target curve while satisfying constraints typical for embedded DSP implementations. The solution is formulated as a constrained non-linear optimization problem tailored for stable and efficient biquad coefficient estimation. The proposed approach enables automated tuning workflows in which measured acoustic responses are directly converted into equalization parameters without manual intervention. Applications include factory calibration, premium audio tuning, and speech communication optimization, resulting in improved repeatability, reduced tuning time, and consistent spectral performance across vehicles.
Tactile transducers, or "shakers", are increasingly being included within automotive seats, both as a safety feature for driver alerts, and as part of the in-vehicle sound system. High sound pressure level auditory experiences such as live sound events are often accompanied by tactile sensations, and so the inclusion of tactile excitation alongside the audio rendered by the vehicle's loudspeakers can enhance the listener experience. In audio-tactile systems, the micro-dynamic properties of the driving signals can be manipulated in order to enhance either transient or steady-state elements. This can be carried out both as part of the tuning of the system, or in order to cater to different user preferences. This study reports a subjective test where 50 subjects rated audio-tactile experiences with differing balance of transient and steady-state elements, in order to determine whether there is a relationship between user preference for transient-steady-state balance and musical genre. The results suggest that there are no discernible trends for user preference versus genre.
