On the practical application of the impulse response measurement method with swept-sine signals in building acoustics
Bibliographic record
Abstract
Impulse response (IR) measurement methods with deterministic signals have been used in various fields of acoustics for decades, yet there is still a hesitancy to apply them by some practitioners, especially in the building acoustics community. This hesitancy is also the topic of discussion in ASTM standards committees because IR methods are not allowed in ASTM standards. The criticism that prevents a more widespread adoption is that the description of IR methods in existing standards such as ISO 18233 is not sufficient to enable practitioners and equipment manufacturers to reliably implement them. Previous publications have described the theoretical background well, but they have not given sufficient guidance for the practical application with respect to the parameters of the measurement signal. To provide more guidance and show the practical advantages of IR measurement methods, this paper investigates the effects of the design parameters of swept-sine signals. Measurements in a reverberant chamber are used to highlight potential problems and appropriate solutions derived from a theoretical background. Suggestions for the design of measurement signals and for the post-processing of the measured data are provided to achieve optimal and reliable results. This contribution hopefully gives practitioners more confidence in applying the method in the future.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".