Evaluation of the Phase-Inversion Signal Separation Method When Using Nonlinear Hearing Aids
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it