University of Strathclyde

Braincodec, an encoding algorithm for auditory-cortex implants, compresses natural sound onto 256 or 512 stimulation channels at a reconstruction SI-SNR near 10, against roughly -30 for cochlear-implant encoders. Its stimulation patterns also resemble auditory-cortex responses, and implanted mice discriminated pure tones through those patterns as precisely as mice hearing real tones.

Braincodec, an encoding algorithm for auditory-cortex implants, compresses natural sound onto 256 or 512 stimulation channels at a reconstruction SI-SNR near 10, against roughly -30 for cochlear-implant encoders. Its stimulation patterns also resemble auditory-cortex responses, and implanted mice discriminated pure tones through those patterns as precisely as mice hearing real tones.

Starting from a previously trained temporal convolutional network sperm whale click-train detector, the study compared four transfer approaches across four sources (BAL, CS, ICE, MED)—cross-dataset baseline evaluation, training from scratch on only 500 target recordings, pretraining with random fine-tuning, and pretraining with active (uncertainty-based) fine-tuning—and found that pretrained models dropped in performance on unseen data but that fine-tuning with 500 target recordings effectively mitigated the drop, with active fine-tuning consistently outperforming the other approaches although its gain over random fine-tuning was marginal.

Starting from a previously trained temporal convolutional network sperm whale click-train detector, the study compared four transfer approaches across four sources (BAL, CS, ICE, MED)—cross-dataset baseline evaluation, training from scratch on only 500 target recordings, pretraining with random fine-tuning, and pretraining with active (uncertainty-based) fine-tuning—and found that pretrained models dropped in performance on unseen data but that fine-tuning with 500 target recordings effectively mitigated the drop, with active fine-tuning consistently outperforming the other approaches although its gain over random fine-tuning was marginal.