Understanding the sounds that create the most positive subjective and emotional responses for customers and, importantly, linking this knowledge to objective engineering specifications is a major challenge. This paper presents research to create the tools and techniques to achieve this. It starts with work carried out to understand the language of sound perception and a means to capture it succinctly. Two perceptual dimensions were established that enabled the positioning of current, competitor, and target vehicles in a subjective space to be determined. Early work involved the study of fixed sounds, both real and simulated, in a listening room. However, this approach has been improved by enhancing the level of context and interactivity. Perception of the sounds of on-road cars is affected by stimuli for other senses (e.g. visual and vibrational), and the fact that an assessor is also concentrating on driving. Furthermore, drivers experience a full range of vehicle sounds, rather than fixed stimuli. The development and use of interactive simulation tools are therefore introduced. They provide a more confident assessment of preferences but also allow a greater understanding of the contributing factors to these preferences, e.g. those aspects of real-world driving that are most important in forming overall impressions of vehicle sound.
In order to determine how the interior of a car should sound, automotive manufacturers often rely on obtaining data from individual evaluations of vehicle sounds. Company identity could play a role in these appraisals, particularly when individuals are comparing cars from opposite ends of the performance spectrum. This research addressed the question: does company identity influence the evaluation of automotive sounds belonging to cars of a similar performance level and from the same market segment? Participants listened to car sounds from two competing manufacturers, together with control sounds. Before listening to each sound, participants were presented with the correct company identity for that sound, the incorrect identity or were given no information about the identity of the sound. The results showed that company identity did not influence appraisals of high performance cars belonging to different manufacturers. These results have positive implications for methodologies employed to capture the perceptions of individuals. STATEMENT OF RELEVANCE: A challenge in automotive design is to set appropriate targets for vehicle sounds, relying on understanding subjective reactions of individuals to such sounds. This paper assesses the role of company identity in influencing these subjective reactions and will guide sound evaluation studies, in which the manufacturer is often apparent.
The primary noise attributes in a vehicle are engine, road and wind noise.In terms of human perception, an individual can drive a vehicle over an extended period of time and be left with the feeling that one of these noise attributes dominates. This can be viewed as an imbalance of the noise attributes. Previous investigations in the literature covering primary attribute balance have a bias towards using trained assessors and restricted driving load cases. This paper approaches the optimal balance of noise attributes using customer ratings from an established 'three month in service' industry study. In doing so, this study encompasses a variety of real world driving scenarios and covers a cross-section of the customer demographic base. The aim of the research is to identify the optimal attribute balance from a customer perspective and to link the key factors influencing this perception. In approaching this aim, the underlying acoustic principles for the interaction of the primary noise attributes are first outlined. The methods used in previously published investigations are summarised and the case for a customer based investigation is made. The scope of existing customer data is reviewed and a methodology to approach the datasets is developed. The interaction of the primary noise attributes is then explored and the Kano quality model is introduced as an aid to explaining the trends seen in the data. In conclusion, the findings of the study are used to suggest a strategy to achieve optimal attribute balance from a customer perspective.
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