Engine Sound Quality Development Using Engine Vibration
Bibliographic record
Abstract
Automotive companies are trying to enhance the customer’s impression by improving engine sound quality. The target of this sound quality is to create a brand sound that is preferred by their customers as well as quietness of interior noise. Over the past decade there have been many studies in the field of automotive sound quality. These have included the technologies such as tuning of intake orifice and exhaust orifice, tuning of structure-borne, intake feedback devices, active exhaust valves, ANC (Active Noise Cancellation) and ASD (Active Sound Design). The three elements of the sound that affect the feeling of the customer are known as engine order arrangement, frequency balance, and linearity. Here, the most important thing in sound quality development is the order arrangement. With traditional development methods by the tuning of existing vehicle systems, it is not possible to achieve the desired order arrangement, and technologies such as ASD and ESG (Engine Sound Generator) have recently been applied to vehicles. However, since ASD reproduces a synthetic sound and ESG creates a desired sound by exciting the body with a vibration exciter, it is hard to say that sound produced by these technologies is real engine sound. This paper describes the ESEV (Engine Sound by Engine Vibration) technology that uses engine vibration to provide real engine sound. The vibration level of the engine reacts instantaneously to the speed and load of the vehicle reflecting the driver’s request. The engine order components are extracted from the measured vibration in real time to make the desired engine sound. Using additional signal processing, the desired engine sound is implemented in the vehicle interior through the loudspeaker system. This sound is unquestionably authentic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".