A reduced complexity MFCC-based deep neural network approach for speech enhancement
Bibliographic record
Abstract
This paper focuses on a regression-based deep neural network (DNN) approach for single-channel speech enhancement. While DNN can lead to improved speech quality compared to classical approaches, it is afflicted by high computational complexity in the training stage. The main contribution of this work is to reduce the DNN complexity by introducing a spectral feature mapping from noisy mel frequency cepstral coefficients (MFCC) to enhanced short-time Fourier transform (STFT) spectrum. This approach requires much fewer input features and consequently lead to reduced DNN complexity. Exploiting the frequency domain speech features obtained from this mapping also avoids the information loss in reconstructing the speech signal back to time domain from its MFCC. Compared to the STFT-based DNN approach, the complexity of our approach for the training phase is reduced by a factor of 4.75. Moreover, experimental results of perceptual evaluation of speech quality (PESQ) and source-to-distortion ratio (SDR) show that the proposed approach outperforms the benchmark algorithms and this for various noise types, and different SNR levels.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".