Reduction of Diffuse Noise in Mobile and Vehicular Applications
Bibliographic record
Abstract
In this chapter, we describe a hybrid subband adaptive speech enhancement system, implemented on an efficient ultra-low resource hardware platform utilizing oversampled generalized DFT filterbanks. Two analysis filterbanks decompose the two inputs (reference noise and noisy speech) into two sets of subband signals. In each subband, a subband adaptive filtering noise reduction block processes the two subband signals to reduce the noise producing a single signal which is followed by further noise reduction through Wiener filtering. Next, a synthesis filterbank converts the processed subband signals back into the time-domain. We have evaluated the performance of the hybrid noise reduction system in various real-life noise fields occurring in mobile and vehicular applications. Two closely spaced microphones make recordings in these noise fields. Signals from one microphone are used directly and represent the reference noise signal while signals from the other microphone are added to speech materials chosen from the TIMIT database before being used as the contaminated primary signal. It is demonstrated that all the noise recordings closely obey a diffuse noise field model. As the hybrid enhancement system is specifically designed to handle diffuse noise fields, it outperforms both the SAF and standard Wiener filtering in all sets of recordings. The superiority of the hybrid system is especially noted in the case of lowpass noise and intense noise conditions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".