Neural Wavelet Packet-Based Bidirectional Autoencoder for Multi-Resolution Speech Enhancement
Bibliographic record
Abstract
Speech enhancement is a critical challenge in signal processing, particularly in noisy environments where preserving intelligibility and perceptual quality is essential. Unlike conventional deep learning-based models that operate exclusively in either the time or frequency domain, we present an adaptive multi-resolution approach that enables superior noise suppression while meticulously preserving critical speech structures across diverse frequency bands. To this end, we introduce the Neural Wavelet Packet-Based Bidirectional Autoencoder (NWPA), a novel framework for multi-resolution speech enhancement. NWPA leverages the Fast Discrete Wavelet Packet Transform with trainable filters that jointly decompose both approximation and detail sub-bands, capturing richer time-frequency features than traditional fixed-wavelet approaches. A bidirectional autoencoder design reduces parameter overhead by unifying the encoding and decoding stages, while an improved Learnable Asymmetric Hard Thresholding function adaptively suppresses noise in the wavelet domain. Furthermore, a Sparsity-Enforcing Loss Function balances reconstruction fidelity with wavelet sparsity, preserving critical speech components across multiple resolutions. Comprehensive evaluations on the VoiceBank-DEMAND dataset demonstrate NWPA’s state-of-the-art performance, underscoring its effectiveness in both noise reduction and intelligibility preservation. These results highlight NWPA’s potential as a robust and scalable solution for speech enhancement under diverse noise conditions. The source code is available at: https://github.com/alaaNfissi/Neural-Wavelet-Packet-Based-Bidirectional-Autoencoder-for-Multi-Resolution-Speech-Enhancement.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".