Multichannel Speech Enhancement Using Complex-Valued Graph Convolutional Networks and Triple-Path Attentive Recurrent Networks
Bibliographic record
Abstract
Multichannel speech enhancement has gained significant attention for its capability of improving speech quality and intelligibility in noisy environments. This paper presents a novel approach to multichannel speech enhancement utilizing complex-valued graph-in-graph convolutional networks (GiGCN) and triple-path attentive recurrent networks (TPARN). The proposed model leverages complex-valued operations to capture spatial dependencies and decoupled LSTM blocks to model temporal correlations. Meanwhile, the TPARN can effectively fuse the frequency, time, and spatial features for the reconstruction of the enhanced speech. Our experimental results based on the CHiME-3 and L3DAS22 datasets show that the proposed integrated model outperforms the state-of-the-art methods in terms of the PESQ, STOI and WER performance metrics.
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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 | 0.001 | 0.001 |
| 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".