Evaluation of noise reduction techniques for digital hearing aids
Bibliographic record
Abstract
Individuals with sensorineural hearing loss have increased difficulty in understanding speech in noisy backgrounds. To combat this issue, there has been a major thrust in recent years toward the development of noise reduction algorithms. The goals of this paper are to quantify the relative benefits of different single-microphone noise reduction algorithms, and to investigate the interaction between the noise reduction and dynamic range compression algorithms. Noise reduction techniques evaluated in this paper include spectral subtraction-based techniques, a wavelet-packet-based technique and a matching pursuit-based technique. All algorithms were tested with HINT signals with SNR levels ranging from −5 to 15 dB, and two different noise types viz. the speech-shaped noise and multi-talker babble. Performance was quantified using the ITU standardized PESQ measure which computes the perceptual similarity between the enhanced signal and the original signal. Initial PESQ results showed that the spectral subtraction-based techniques perform superior to that of the wavelet-packet and matching pursuit-based approaches and that the compression time constants have an impact on the overall performance. Perceptual data collected from hearing impaired listeners on sound quality and noise reduction performance will be presented and their correlation with the objective measurements will be discussed.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".