Objective and Perceptual Sound Quality Analysis of Internal Combustion Engine and Electric Vehicles
Bibliographic record
Abstract
The sound quality of automotive interiors is one of the critical factors regarding customer satisfaction. As electric vehicles (EVs) rise in popularity rapidly, the known literature on sound qualities of internal combustion engine (ICE) automotive interiors has become less relevant. Because of this, comparing and contrasting EV and ICE vehicle's sound qualities is extremely important to have the proper foundation for studying automotive noise quality in the future. In our study, we aim to benchmark the major differences between an EV and an ICE automobile regarding interior sound quality. This study aims to understand basic sound engineering characteristics and how they differ between the two types of vehicles. We also analyzed the preferences that the public has when it comes to the two types of cars. To get as much data as we could in our time-constrained project, we tested both types of vehicles in two different environments, which were an uncontrolled road (Bluff Street) and a controlled track (the GM Mobility Research Center). We also tested three different positions in the car, including the driver's seat, passenger seat, and rear middle seat position. The interior sound was then recorded using the SQobold sound acquisition device and the Head Acoustics AACHEN Head as the microphone. Three recordings of every type of test were taken in order to confirm consistent and accurate results. We then compared and contrasted the data in ArtemisSuite, a sound analysis software. We determined the major differences between the cars, particularly in loudness and sharpness. The final step was jury testing, in which the subjective samples compare well with our conclusions regarding sound quality metrics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".