Evaluation of the Phase-Inversion Signal Separation Method When Using Nonlinear Hearing Aids
Bibliographic record
Abstract
Using two measurements with simultaneous speech and noise presentation, Hagerman and Olofsson have suggested a time-domain method to estimate the speech and noise signals at the output of a hearing device. The method, which uses a simple phase-inversion scheme, has gained popularity in hearing-aid research, although receiving only limited validation. In this work, we present an evaluation of this signal-separation method using simulated measurements with different hearing aids and listening conditions. Estimates of the speech and noise spectra from the phase-inversion method are compared to those obtained using the coherence function. New measures of speech and noise distortion are proposed as tools to evaluate the phase-inversion method. Additionally, we analyze the intelligibility predictions computed from the recovered spectral estimates, while accounting for the proposed speech distortion measure. Under additive-noise conditions, the phase-inversion method provides ideal signal separation without suffering any biases at low signal-to-noise ratios. For conditions involving automatic gain control, compressive output limiting, and peak clipping, the intelligibility predictions based on the phase-inversion method are found to agree with relevant findings from the literature.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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 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".