动态调整频率与参数，让语音神经计算更高效省电。
Adaptive Central Frequencies Locally Competitive Algorithm for Speech
- 自适应调节调制参数和中心频率，优化语音表示
- 在Loihi 2芯片上降低功耗，重建质量与稀疏性提升
- 适合边缘设备上的低功耗语音处理应用
类脑计算以高效低功耗为核心，通过稀疏编码提升处理效率，适用于资源受限的边缘设备。针对音频信号的局部竞争算法（LCA）结合伽马音调（Gammatone）与伽马啁啾（Gammachirp）滤波组，实现高效的神经形态语音编码。自适应LCA（ALCA）通过动态调节调制参数进一步提升重构质量与稀疏性。本文提出改进版ALCA-CF，同时动态调整调制参数与中心频率，优化语音表征。实验表明，该方法在Intel Loihi 2芯片上显著降低语音分类功耗，保持分类精度，同时提升重构质量与稀疏性。
原文摘要 · Abstract (English)
Neuromorphic computing, inspired by nervous systems, revolutionizes information processing with its focus on efficiency and low power consumption. Using sparse coding, this paradigm enhances processing efficiency, which is crucial for edge devices with power constraints. The Locally Competitive Algorithm (LCA), adapted for audio with Gammatone and Gammachirp filter banks, provides an efficient sparse coding method for neuromorphic speech processing. Adaptive LCA (ALCA) further refines this method by dynamically adjusting modulation parameters, thereby improving reconstruction quality and sparsity. This paper introduces an enhanced ALCA version, the ALCA Central Frequency (ALCA-CF), which dynamically adapts both modulation parameters and central frequencies, optimizing the speech representation. Evaluations show that this approach improves reconstruction quality and sparsity while significantly reducing the power consumption of speech classification, without compromising classification accuracy, particularly on Intel's Loihi 2 neuromorphic chip.
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