Analog feature extraction is a power-efficient and re-emerging signal processing paradigm for implementing the front-end feature extractor in on device keyword-spotting systems. Despite its power efficiency and re-emergence, there is little consensus on what values the architectural parameters of its critical block, the analog filterbank, should be set to, even though they strongly influence power consumption. Towards building consensus and approaching fundamental power consumption limits, we find via simulation that through careful selection of its architectural parameters, the power of a typical state-of-the-art analog filterbank could be reduced by 33.6x, while sacrificing only 1.8% in downstream 10-word keyword spotting accuracy through a back-end neural network.

翻译：模拟特征提取是一种低功耗且正在复兴的信号处理范式，用于实现设备端关键词识别系统的前端特征提取器。尽管具有低功耗优势且重新受到关注，但关于其关键模块——模拟滤波器组的架构参数应如何设定仍缺乏共识，尽管这些参数对功耗影响显著。为建立共识并逼近功耗极限，我们通过仿真发现：通过精心选择架构参数，典型先进模拟滤波器组的功耗可降低33.6倍，而通过后端神经网络进行的下游10词关键词识别准确率仅牺牲1.8%。