Speech enhancement using a reduced complexity MFCC-based Deep Neural Network
Bibliographic record
Abstract
In contrast to classical noise reduction methods introduced over the past decades, this work focuses on a regression-based single-channel speech enhancement framework using DNN, as recently introduced by Liu et al..While the latter framework 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.Leveraging MFCC not only has the advantage of mimicking the logarithmic perception of human auditory system, but this approach requires much fewer input features and consequently lead to reduced DNN complexity.Exploiting the frequency domain speech features obtained from the results of such a mapping also avoids the information loss in reconstructing the time-domain speech signal from its MFCC.While the proposed method aims to predict clean speech spectra from corrupted speech inputs, its performance is further improved by incorporating information about the noise environment into the training phase.We implemented the proposed DNN method with different numbers of MFCC and used it to enhance several different types of noisy speech files.Experimental results of perceptual evaluation of speech quality (PESQ) show that the proposed approach can outperform the benchmark algorithms including a recently proposed non-negative matrix factorization (NMF) approach, and this for various speakers and noise types, and different SNR levels.More importantly, the proposed approach with MFCC leads to a significant reduction in complexity, where the runtime is reduced by a factor of approximately five.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".