Hardware–Software Co-Design of an Audio Feature Extraction Pipeline for Machine Learning Applications.

Autor:	Vreča, Jure, Pilipović, Ratko, Biasizzo, Anton
Předmět:	MACHINE learning SPEECH perception PARTICIPATORY design
Zdroj:	Electronics (2079-9292); Mar2024, Vol. 13 Issue 5, p875, 14p
Abstrakt:	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. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
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