Virtual Engine Sound Tuning With Review-Driven Emotion Models
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Solution Overview
Problem
Electric vehicles lack customizable and trend-reflected virtual engine sounds, as existing electronic sound generators produce monotonous sounds that do not cater to individual user preferences or the latest trends.
Innovation Solution
A system and method that include a server and virtual engine sound generator, which update preset driver emotion models based on user reviews to customize virtual engine sounds, adjusting parameters like mode, sound volume, tone, and reaction degree, using a user interface and emotion evaluation areas such as Idle Volume, Engine Main, Dynamics, Rumble, or Whine, and extracting keywords from reviews to refine the sound generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If an electronic sound generator is used to produce virtual engine sound in electric vehicles, then the basic function of providing engine noise is achieved, but the sound is monotonous and cannot be customized for individual users or updated to reflect latest trends
Solution Approach 1:
The sound generation system is segmented into multiple independent emotion models (e.g., angry, sad, happy, neutral) that can be individually updated and combined. Each emotion model corresponds to specific sound characteristics, allowing flexible customization without redesigning the entire system.
Solution Approach 2:
The system dynamically adjusts virtual engine sound by selecting and combining different emotion models based on driving conditions, user preferences, and latest trends. The sound characteristics change in real-time to reflect different emotional states, making the system adaptable and customizable.
2Loss of information
If the virtual engine sound is updated based on user reviews and emotion models, then user satisfaction and trend reflection are improved, but the system complexity and processing requirements increase
Solution Approach 1:
The system incorporates user reviews as feedback to continuously update and refine emotion models. User preferences and feedback are processed to adjust sound characteristics, ensuring the system reflects latest trends and individual user preferences while maintaining manageable complexity through iterative improvement.
Solution Approach 2:
The system changes sound parameters (volume, tone, frequency) based on different emotion models and user preferences. By adjusting existing parameters rather than creating entirely new sound generation mechanisms, the system retains user preference information while controlling processing complexity.
3Manufacturing precision
If multiple emotion evaluation areas (Idle Volume, Engine Main, Dynamics, Rumble, Whine) are used to customize sound, then the precision and quality of virtual engine sound is improved, but the complexity of sound adjustment increases
Solution Approach 1:
Multiple emotion models serve universal functions across different emotion evaluation areas. Each emotion model can influence multiple sound characteristics (volume, tone, dynamics) simultaneously, allowing precise customization through a unified interface rather than separate controls for each parameter.
Solution Approach 2:
The system merges multiple sound adjustment functions into integrated emotion models. Instead of independently controlling Idle Volume, Engine Main, Dynamics, Rumble, and Whine, users select emotion models that automatically coordinate adjustments across all these areas, maintaining precision while simplifying operation.
Data Source
AI summary
A system for generating a virtual engine sound may include a server having information on at least one review of the virtual engine sound, and a virtual engine sound generator to generate “my sound” by tuning the virtual engine sound based on information on customized design settings, to update a preset driver emotion model for each emotion evaluation area of the virtual engine sound, based on the information on the at least one review of the virtual engine sound, which is received from the server, and to update the virtual engine sound based on the “my sound” and the driver emotion model updated for each emotion evaluation area.