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Hardware–software co-design of an audio feature extraction pipeline for machine learning applicationsJure Vreča,
Ratko Pilipović,
Anton Biasizzo, 2024, original scientific article
Abstract: Keyword spotting is an important part of modern speech recognition pipelines. Typical contemporary keyword-spotting systems are based on Mel-Frequency Cepstral Coefficient (MFCC) audio features, which are relatively complex to compute. Considering the always-on nature of many keyword-spotting systems, it is prudent to optimize this part of the detection pipeline. We explore the simplifications of the MFCC audio features and derive a simplified version that can be more easily used in embedded applications. Additionally, we implement a hardware generator that generates an appropriate hardware pipeline for the simplified audio feature extraction. Using Chisel4ml framework, we integrate hardware generators into Python-based Keras framework, which facilitates the training process of the machine learning models using our simplified audio features.
Keywords: FPGA, MFCC, keyword spotting, chisel
Published in DiRROS: 25.03.2024; Views: 112; Downloads: 52
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