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.
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.
