Dimensionality Reduction of Room Acoustic Impulse Responses and Applications to System Identification
Bibliographic record
Abstract
A room Acoustic Impulse Response (RAIR), which represents the sound propagation channel via direct and reflection paths from a source position to a microphone, plays a leading role in a broad range of acoustic signal processing applications, e.g., echo cancellation. In practical acoustic environments, it is not uncommon that an RAIR may consist of hundreds or even thousands of coefficients, making it challenging to identify and handle. This paper investigates the RAIR dimensionality reduction problem inspired from the concepts of dynamic mode decomposition. The objective is to find effective lower-dimensional representations of RAIRs, which are easier and more robust to identify and equalize. There are two main contributions of this work. First, we present an RAIR dimensionality reduction method. Second, we show how to apply this technique to the problem of acoustic system identification. Simulation results demonstrate that the proposed method is able to improve significantly the performance of acoustic system identification.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".