VSE knjižnice (vzajemna bibliografsko-kataložna baza podatkov COBIB.SI)
  • Hardware–software co-design of an audio feature extraction pipeline for machine learning applications [Elektronski vir]
    Vreča, Jure ; Pilipović, Ratko ; Biasizzo, Anton, (računalništvo)
    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.
    Vir: Electronics [Elektronski vir]. - ISSN 2079-9292 (13, 5, 24. Feb. 2024, str. 1-14)
    Vrsta gradiva - e-članek ; neleposlovje za odrasle
    Leto - 2024
    Jezik - angleški
    COBISS.SI-ID - 186803203

    Povezava(-e):

    https://www.mdpi.com/2079-9292/13/5/875
    DiRROS - Digitalni repozitorij raziskovalnih organizacij Slovenije
    DOI

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