Time-Delay-Neural-Network-Based Audio Feature Extractor for Ultra-Low Power Keyword Spotting

Abstract

In this brief, we propose an audio feature extractor, based on a time delay neural network (TDNN), for ultra-low power keyword spotting (KWS). Conventionally, mel-frequency cepstrum coefficients (MFCCs) are widely used as features for KWS. However, an analog-to-digital converter (ADC) with high precision and high sampling rate is required for computing MFCCs, which consumes large amount of power. In our proposed feature extractor, on the other hand, input audio signals are band-pass filtered, and then the filtered signals are processed in TDNN in the analog domain, which can substitute a high precision ADC with simple clocked comparators. Simulation results show that the power dissipation of the proposed feature extractor can be reduced by 88% compared to the conventional MFCC feature extractor.