Researchers are exploring novel computational paradigms such as sparse coding and neuromorphic computing to bridge the efficiency gap between the human brain and conventional computers in complex tasks. A key area of focus is neuromorphic audio processing. While the Locally Competitive Algorithm has emerged as a promising solution for sparse coding, offering potential for real-time and low-power processing on neuromorphic hardware, its applications in neuromorphic speech classification have not been thoroughly studied. The Adaptive Locally Competitive Algorithm builds upon the Locally Competitive Algorithm by dynamically adjusting the modulation parameters of the filter bank to fine-tune the filters' sensitivity. This adaptability enhances lateral inhibition, improving reconstruction quality, sparsity, and convergence time, which is crucial for real-time applications. This paper demonstrates the potential of the Locally Competitive Algorithm and its adaptive variant as robust feature extractors for neuromorphic speech classification. Results show that the Locally Competitive Algorithm achieves better speech classification accuracy at the expense of higher power consumption compared to the LAUSCHER cochlea model used for benchmarking. On the other hand, the Adaptive Locally Competitive Algorithm mitigates this power consumption issue without compromising the accuracy. The dynamic power consumption is reduced to a range of 4 to 13 milliwatts on neuromorphic hardware, three orders of magnitude less than setups using Graphics Processing Units. These findings position the Adaptive Locally Competitive Algorithm as a compelling solution for efficient speech classification systems, promising substantial advancements in balancing speech classification accuracy and power efficiency.

翻译：研究人员正在探索稀疏编码与神经形态计算等新型计算范式，以弥合人脑与常规计算机在复杂任务中的效率差距。神经形态音频处理是其中的一个关键研究领域。尽管局部竞争算法已成为稀疏编码领域一种颇具前景的解决方案，为神经形态硬件上的实时低功耗处理提供了潜力，但其在神经形态语音分类中的应用尚未得到深入研究。自适应局部竞争算法在局部竞争算法的基础上，通过动态调整滤波器组的调制参数来微调滤波器的灵敏度。这种自适应性增强了侧向抑制，从而改善了重构质量、稀疏性和收敛时间，这对实时应用至关重要。本文论证了局部竞争算法及其自适应变体作为神经形态语音分类鲁棒特征提取器的潜力。实验结果表明，与用于基准测试的LAUSCHER耳蜗模型相比，局部竞争算法以更高的功耗为代价获得了更好的语音分类准确率。而自适应局部竞争算法在不损失准确率的前提下缓解了此功耗问题。在神经形态硬件上，其动态功耗可降低至4至13毫瓦范围，比使用图形处理单元的配置低三个数量级。这些发现表明自适应局部竞争算法是高效语音分类系统的一种引人注目的解决方案，有望在平衡语音分类准确率与能效方面取得重大进展。