Perceptual time-frequency subtraction algorithm for noise reduction in hearing aids
Bibliographic record
Abstract
Sensorineural hearing disorders are a major and universal community health problem. In many cases, hearing aids offer the only solution for people suffering from such disorders. Unfortunately existing aids do not provide any improvement in intelligibility of the signal when background noise is present. A hearing aid system should ideally simulate auditory processes including those aspects of the speech signal that are perceptually important. This work presents a new integrated approach to the design of a digital hearing aid, based on a wavelet transform, as well as a formulation of the temporal and spectral psychoacoustic model of masking. Within the model, the Perceptual Time-Frequency Subtraction (PTFS) algorithm is developed to simulate the masking phenomena and reduce noise in single-input systems. Results show that the use of the PTFS yields a significant improvement in speech quality especially in unvoiced portions. Additionally, the noise component during periods of silence has been attenuated by up to 20 dB. This new noise reduction method is expected to be applicable in a variety of applications, including digital hearing aids and portable communication systems (e.g., cellular telephones).
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".