Improved Noise Power Spectrum Density Estimation for Binaural Hearing Aids Operating in a Diffuse Noise Field Environment
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
The current generation of digital hearing aids allows the implementation of advanced noise reduction schemes. However, most current noise reduction algorithms are monaural and are therefore intended for only bilateral hearing aids. Recently, binaural in contrast to monaural noise reduction schemes have been proposed, targeting future high-end binaural hearing aids. Those new types of hearing aids would allow the sharing of information/signals received from both left and right hearing aid microphones (via a wireless link) to generate an output for the left and right ear. This paper presents a novel noise power spectral density estimator for binaural hearing aids operating in a diffuse noise field environment, by taking advantage of the left and right reference signals that will be accessible, as opposed to the single reference signal currently available in bilateral hearing aids. In contrast with some previously published noise estimation methods for hearing aids or speech enhancement, the proposed noise estimator does not assume stationary noise, it can work for colored noise in a diffuse noise field, it does not require a voice activity detection, the noise power spectrum can be estimated during speech activity or not, it does not experience noise tracking latency and most importantly, it is not essential for the target speaker to be in front of the binaural hearing aid user to estimate the noise power spectrum, i.e., the direction of arrival of the source speech signal can be arbitrary. Finally, the proposed noise estimator can be combined with any hearing aid noise reduction technique, where the accuracy of the noise estimation can be critical to achieve a satisfactory de-noising performance.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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