用注意力机制提升人工耳蜗信号编码，效果接近传统方法。
Enhancing Cochlear Implant Signal Coding with Scaled Dot-Product Attention
- 引入缩放点积注意力生成电极图谱，替代传统编码策略。
- 语音可懂度评分达0.6031，接近传统ACE策略的0.6126。
- 适合关注智能听力修复与个性化医疗的科研人员。
人工耳蜗（CIs）通过电刺激听觉神经，为重度至极重度感音神经性耳聋患者恢复听力发挥关键作用。尽管传统编码策略如先进组合编码器（ACE）已证明有效，但其适应性和精度存在局限。本文研究利用深度学习（DL）技术生成人工耳蜗电极图谱，提出一种新型替代方案。通过短时客观可懂度（STOI）指标评估重建语音信号的可懂度，结果表明该模型取得0.6031的STOI分数，接近ACE策略的0.6126，并展现出在灵活性与适应性方面的潜在优势。本研究强调将人工智能融入耳蜗技术的益处，如个性化和效率提升。
原文摘要 · Abstract (English)
Cochlear implants (CIs) play a vital role in restoring hearing for individuals with severe to profound sensorineural hearing loss by directly stimulating the auditory nerve with electrical signals. While traditional coding strategies, such as the advanced combination encoder (ACE), have proven effective, they are constrained by their adaptability and precision. This paper investigates the use of deep learning (DL) techniques to generate electrodograms for CIs, presenting our model as an advanced alternative. We compared the performance of our model with the ACE strategy by evaluating the intelligibility of reconstructed audio signals using the short-time objective intelligibility (STOI) metric. The results indicate that our model achieves a STOI score of 0.6031, closely approximating the 0.6126 score of the ACE strategy, and offers potential advantages in flexibility and adaptability. This study underscores the benefits of incorporating artificial intelligent (AI) into CI technology, such as enhanced personalization and efficiency.
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