Brain-Informed Speech Separation for Cochlear Implants
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866912861333225472 |
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| author | Gajecki, Tom Althoff, Jonas Nogueira, Waldo |
| author_facet | Gajecki, Tom Althoff, Jonas Nogueira, Waldo |
| contents | We propose a brain-informed speech separation method for cochlear implants (CIs) that uses electroencephalography (EEG)-derived attention cues to guide enhancement toward the attended speaker. An attention-guided network fuses audio mixtures with EEG features through a lightweight fusion layer, producing attended-source electrodograms for CI stimulation while resolving the label-permutation ambiguity of audio-only separators. Robustness to degraded attention cues is improved with a mixed curriculum that varies cue quality during training, yielding stable gains even when EEG-speech correlation is moderate. In multi-talker conditions, the model achieves higher signal-to-interference ratio improvements than an audio-only electrodogram baseline while remaining slightly smaller (167k vs. 171k parameters). With 2 ms algorithmic latency and comparable cost, the approach highlights the promise of coupling auditory and neural cues for cognitively adaptive CI processing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_22260 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Brain-Informed Speech Separation for Cochlear Implants Gajecki, Tom Althoff, Jonas Nogueira, Waldo Audio and Speech Processing Signal Processing We propose a brain-informed speech separation method for cochlear implants (CIs) that uses electroencephalography (EEG)-derived attention cues to guide enhancement toward the attended speaker. An attention-guided network fuses audio mixtures with EEG features through a lightweight fusion layer, producing attended-source electrodograms for CI stimulation while resolving the label-permutation ambiguity of audio-only separators. Robustness to degraded attention cues is improved with a mixed curriculum that varies cue quality during training, yielding stable gains even when EEG-speech correlation is moderate. In multi-talker conditions, the model achieves higher signal-to-interference ratio improvements than an audio-only electrodogram baseline while remaining slightly smaller (167k vs. 171k parameters). With 2 ms algorithmic latency and comparable cost, the approach highlights the promise of coupling auditory and neural cues for cognitively adaptive CI processing. |
| title | Brain-Informed Speech Separation for Cochlear Implants |
| topic | Audio and Speech Processing Signal Processing |
| url | https://arxiv.org/abs/2601.22260 |