为助听器压缩设计了能适配剪枝的损失函数，提升语音可懂度。
Pruning-aware Loss Functions for STOI-Optimized Pruned Recurrent Autoencoders for the Compression of the Stimulation Patterns of Cochlear Implants at Zero Delay
- 训练时引入剪枝感知损失，提前考虑模型压缩影响。
- 剪枝率55%时，语音可懂度几乎无下降，45%以上优于传统方法。
- 适合资源受限的植入式听力设备，兼顾压缩与性能。
人工耳蜗（CIs）是植入式助听装置，可恢复重度听力损失患者的听觉功能。外部设备无线向耳蜗信号处理器传输音频已成常态。通过深度循环自编码器对耳蜗刺激模式进行专用压缩，可在零延迟下降低比特率，减少功耗。以往研究虽实现显著比特率压缩，但未考虑模型大小，而助听设备计算资源有限，模型尺寸至关重要。本文旨在最大化压缩后刺激模式的客观语音可懂度，同时最小化模型规模。为此提出一种剪枝感知损失函数，捕捉剪枝对训练的影响。相比传统的基于幅度的剪枝方法，该方法在高剪枝率下显著提升语音可懂度。经微调后，在约55%的剪枝率下，客观可懂度几乎无损失；当剪枝率超过45%时，新方法相较基线有显著提升。
原文摘要 · Abstract (English)
Cochlear implants (CIs) are surgically implanted hearing devices, which allow to restore a sense of hearing in people suffering from profound hearing loss. Wireless streaming of audio from external devices to CI signal processors has become common place. Specialized compression based on the stimulation patterns of a CI by deep recurrent autoencoders can decrease the power consumption in such a wireless streaming application through bit-rate reduction at zero latency. While previous research achieved considerable bit-rate reductions, model sizes were ignored, which can be of crucial importance in hearing-aids due to their limited computational resources. This work investigates maximizing objective speech intelligibility of the coded stimulation patterns of deep recurrent autoencoders while minimizing model size. For this purpose, a pruning-aware loss is proposed, which captures the impact of pruning during training. This training with a pruning-aware loss is compared to conventional magnitude-informed pruning and is found to yield considerable improvements in objective intelligibility, especially at higher pruning rates. After fine-tuning, little to no degradation of objective intelligibility is observed up to a pruning rate of about 55\,\%. The proposed pruning-aware loss yields substantial gains in objective speech intelligibility scores after pruning compared to the magnitude-informed baseline for pruning rates above 45\,\%.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。