Acoustic System Identification with Partially Time-Varying Models Based on Tensor Decompositions
Bibliographic record
Abstract
Acoustic system identification, which aims at estimating the channel impulse response from a source of interest to the microphone position, plays an important role in many applications, e.g., echo cancellation for full-duplex speech communication. Generally, an acoustic channel impulse response is modeled as a linear finite-impulse-response (FIR) filter, so the objective of system identification is to identify it. While much effort has been devoted to this topic over the last five decades, identifying the room FIR filters accurately with only a small number of observation data snapshots remains a significant challenge. This paper studies this problem and proposes to model the acoustic impulse response, i.e., the FIR filter, with a tensor decomposition, which can be expressed as a multidimensional Kronecker product of a series of shorter filters. Then, a partially time-varying model is applied to acoustic system identification, where the global filter is decomposed into two parts: a time-invariant part, which captures the common properties of acoustic channels, and a time-varying part, which, as its name indicates, represents the components of acoustic channels that change with time. During the identification process, the time-invariant filters can be identified or learned in advance, while the time-varying filters are optimized through an iterative procedure. Simulation results demonstrate that the proposed technique can achieve better acoustic system identification performance with a small number of data snapshots.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".