Sean will summarize different methods used for subjective evaluation of automotive audio systems, with emphasis on perceived sound quality, spatial impression, distortion spectral balance, and listener preference inside the vehicle cabin. He will highlight the challenges of measuring audio in a complex acoustic environment, the strengths and weaknesses of each approach and shows how controlled evaluations can connect human perception with engineering metrics. The presentation also discusses how these insights guide system tuning, design validation, and the creation of more engaging in-car listening experiences.
"Acoustic Power Volume Density as a Target for Vehicle Cabin Equalization", Yoshi Asahi and Danny Asahi (Eilex)
"Advantage of multi-core architectures for automotive audio systems", Sebastian Und Wendt (Pioneer Electronics)
"A Layered Approach for In-Car Sound Personalization", Alexander Goldin, Alex Radzishevsky and Suhail Habib Alla (Alango Technologies)
"The role of listener position sensing in dynamic crosstalk cancellation with headrest speakers", Davi Carvalho, Yury Ushakov, Jacob Hollebon and Marcos Simon (Auduiosenic)
"Shaping the In-Car Listening Experience: Perceptual Insights and Dynamic Evaluation Methods for Modern Automotive Audio", Magnus Schäfer, Jacob Soendergaard, Anuj Sethi, Adèle Bachmann and Christoph Nelke (HEAD Acoustics)
"A2B-Enabled Wideband MEMS Accelerometer: Design and Characterization", Nicholas Rocchi, Marco Olivieri, Simone Talucci, Tommaso Botti, Matteo Furlan, Matteo Cataldi and Alfonso Oliva (Ferrari S.p.A.)
"Ultra Low Latency Audio Networking for Road-Noise-Cancellation Applications in Zonalised Vehicle Architectures", Peter Hall and Michael Chandler Page (Cirrus Logic)
"Overview of the World’s First Automotive Qualified Electrostatic Audio System", Mike Grant and Ben Lisle (Warwick Acoustics Ltd)
"Cloud-based audio features on demand", Victor Kalinichenko (Yandex)
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.
Intermodulation Distortion in automotive loudspeakers has degraded the quality of the listening experience in vehicles since the onset of audio systems in cars. Even in the current state-of-the-art systems of 30 or more loudspeakers, it can still rear its ugly head. This tutorial will define the especially egregious Amplitude Modulation distortion, show what it looks like in scope captures and simple twin-tone IMOD measurements. It will demonstrate how to use common audio tools like Room EQ Wizard (REW), Smaart, and SpectraFoo to discover what are the danger frequencies in given loudspeaker by using the Spectragraph function. It will show how even expensive speakers within their linear Bl(x) (magnet strength) and linear Kms(x) (stiffness) curves can still suffer greatly from this AM distortion, even at lower excursions than their parameters would predict. We will show how typical automotive speaker mounting and protective speaker grills actually increase the presence and audibility of this distortion. We will show a technique to measure this distortion in the vehicle. The tutorial will show visualizations of distortion-causing cone motion recorded by a laser vibrometer. Finally, we will listen to the audible effects of this distortion on different music examples. The format will allow questions during any point in the tutorial to allow for full understanding and explore how diaphragm simulation could help solve some of these issues.
The rapid adoption of Dolby Atmos in automotive audio systems marks a fundamental shift from traditional channel-based reproduction toward object-based, content-driven spatial audio. Unlike legacy surround upmixers, which algorithmically derive envelopment from stereo or limited multichannel sources, Dolby Atmos enables playback of artist-authored spatial intent through dynamic rendering tailored to the vehicle’s speaker layout. This transition introduces both new opportunities and significant engineering challenges for OEMs and suppliers. This workshop explores the evolving role of immersive audio in the automotive environment, comparing the perceptual, technical, and implementation differences between Atmos rendering and advanced upmixing approaches. Key topics include system architecture implications such as height channel integration, the continued necessity of upmixers for non-Atmos content, and the balance between spatial accuracy and brand-specific sound tuning. Emphasis is placed on real-world constraints unique to vehicles, including asymmetric cabin geometries, seat-to-seat variability, limited speaker placement options, and the impact of reflective surfaces on spatial perception. The discussion will also address computational and cost considerations, content variability, and the challenges of validating spatial audio performance using both objective and subjective methods. Through a combination of technical insights and practical examples, this workshop aims to provide a comprehensive understanding of how immersive audio technologies are reshaping automotive sound system design. Attendees will gain perspective on best practices, current limitations, and future directions for delivering compelling spatial audio experiences in production vehicles. As automotive audio systems evolve into high-channel-count, software-defined platforms, OEM decision-makers face a critical question: how to deliver compelling spatial audio experiences from a content ecosystem still dominated by stereo. This workshop examines the role of sound quality evaluation in guiding that transition, tracing the evolution of upmixing technologies from early matrix decoding to modern content-adaptive and object-inspired approaches. While upmixers remain essential for scalability across legacy content, they introduce inherent limitations—including spatial ambiguity, tonal artifacts, and content-dependent performance—that directly impact perceived quality and brand differentiation. In contrast, discrete multichannel and object-based formats such as Dolby Atmos offer improved localization, stability, and creative intent preservation, but introduce challenges in content availability, system cost, and integration complexity. Through a combination of technical insights, perceptual evaluation frameworks, and real-world automotive case studies, this workshop will explore how these trade-offs manifest in production vehicles. Special emphasis will be placed on how sound quality metrics—both objective and listener-based—can inform system tuning strategies, feature prioritization, and platform decisions.
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.
"Acoustic Power Volume Density as a Target for Vehicle Cabin Equalization", Yoshi Asahi and Danny Asahi (Eilex)
"Advantage of multi-core architectures for automotive audio systems", Sebastian Und Wendt (Pioneer Electronics)
"A Layered Approach for In-Car Sound Personalization", Alexander Goldin, Alex Radzishevsky and Suhail Habib Alla (Alango Technologies)
"The role of listener position sensing in dynamic crosstalk cancellation with headrest speakers", Davi Carvalho, Yury Ushakov, Jacob Hollebon and Marcos Simon (Auduiosenic)
"Shaping the In-Car Listening Experience: Perceptual Insights and Dynamic Evaluation Methods for Modern Automotive Audio", Magnus Schäfer, Jacob Soendergaard, Anuj Sethi, Adèle Bachmann and Christoph Nelke (HEAD Acoustics)
"A2B-Enabled Wideband MEMS Accelerometer: Design and Characterization", Nicholas Rocchi, Marco Olivieri, Simone Talucci, Tommaso Botti, Matteo Furlan, Matteo Cataldi and Alfonso Oliva (Ferrari S.p.A.)
"Ultra Low Latency Audio Networking for Road-Noise-Cancellation Applications in Zonalised Vehicle Architectures", Peter Hall and Michael Chandler Page (Cirrus Logic)
"Overview of the World’s First Automotive Qualified Electrostatic Audio System", Mike Grant and Ben Lisle (Warwick Acoustics Ltd)
"Cloud-based audio features on demand", Victor Kalinichenko (Yandex)
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.
Modern automotive audio systems have evolved into distributed, software-defined platforms that combine traditional DSP with machine learning. These systems are continuously trained, tuned, and updated using real-world data, creating complex workflows that extend beyond the vehicle. As a result, AI-driven audio technologies have become valuable intellectual property. Development and deployment typically occur across distributed environments. Engineers capture real-world data, refine models, and validate performance, while finalized algorithms are deployed onto embedded vehicle hardware for real-time operation. This lifecycle introduces significant security risks. Development tools may expose proprietary models outside controlled settings, and deployed systems are vulnerable to reverse engineering, extraction, or unauthorised reuse—especially when physical access to hardware is possible. This workshop focuses on safeguarding AI-driven audio across its lifecycle, identifying where IP is most exposed, and examining common attack methods. It also presents practical strategies to secure both development workflows and in-vehicle systems, ensuring innovation, collaboration, and long-term business value are protected.
Automotive audio systems have historically been engineered around a driver-centric model, with fixed seating geometry and a well-defined listening reference. In contrast, emerging autonomous vehicle interiors introduce reconfigurable seating, multi-user scenarios, and use cases that extend beyond driving to include work, entertainment, and social interaction. These changes fundamentally shift the role of in-cabin audio from a playback system to a component of a broader user experience. This panel explores how audio system design must evolve to support cabin-centric, multi-occupant experiences. Key challenges include managing multiple concurrent use cases, balancing shared and personalized audio, and adapting to dynamic seating configurations and listener orientations. The discussion will also address how audio integrates with human-machine interfaces, including voice interaction, auditory alerts, and contextual feedback, particularly in the absence of a clearly defined driver role. Panelists will examine system-level approaches that bridge UX and engineering, including zonal audio strategies, adaptive rendering informed by occupant sensing, and experience-driven system tuning. Emphasis will be placed on practical design trade-offs, such as isolation versus social cohesion, system complexity versus robustness, and personalization versus consistency. The session aims to outline frameworks for designing audio systems that function as adaptive, user-centered components within next-generation autonomous vehicle cabins.
