End-to-End Brain-Driven Speech Enhancement in Multi-Talker Conditions
Why this work is in the frame
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Bibliographic record
Abstract
Single-channel speech enhancement algorithms have seen great improvements over the past few years. Despite these improvements, they still lack the efficiency of the auditory system in extracting attended auditory information in the presence of competing speakers. Recently, it has been shown that the attended auditory information can be decoded from the brain activity of the listener. In this paper, we propose two novel end-to-end deep learning methods referred to as the Brain Enhanced Speech Denoiser (BESD) and the U-shaped Brain Enhanced Speech Denoiser (U-BESD) respectively, that take advantage of this fact to denoise a multi-talker speech mixture without considering further background noises or reverberations. We use a Feature-wise Linear Modulation (FiLM) between the brain activity and the sound mixture, to better extract the features of the attended speaker to perform speech enhancement. We show, using electroencephalography (EEG) signals recorded from the listener, that both BESD and U-BESD successfully extract the attended speaker without any prior information about this speaker. Moreover, U-BESD also outperforms a current state-of-the-art approach that also uses brain activity to perform enhancement. The proposed neural network-based methods would thus make great candidates for realistic applications where no prior information about the attended speaker is available, such as hearing aids, cellphones, or noise cancelling headphones.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it