An 800 nW Switched-Capacitor Feature Extraction Filterbank for Sound Classification

D.A. Villamizar, D.G. Muratore, J.B. Wieser, B. Murmann

Research output: Contribution to journalArticleScientificpeer-review

14 Citations (Scopus)

Abstract

This paper presents a 32-channel analog filterbank for front-end signal processing in sound classification systems. It employs a passive N-path switched capacitor topology to achieve high power efficiency and reconfigurability. The circuit's unwanted harmonic mixing products are absorbed by the machine learning model during training. To enable a systematic pre-silicon study of this effect, we develop a computationally efficient circuit model that can process large machine learning datasets on practical time scales. Measured results using a 130 nm CMOS prototype IC indicate competitive classification accuracy on datasets for baby cry detection (93.7% AUC) and voice commands (92.4% average precision), while lowering the feature extraction energy compared to digital realizations by approximately 2× and 10×, respectively. The 1.44 mm 2 chip consumes 800 nW, which corresponds to the lowest normalized power per simultaneously sampled channel in recent literature.
Original languageEnglish
Pages (from-to)1578 - 1588
Number of pages11
JournalIEEE Transactions on Circuits and Systems I Regular Papers, pp. 1–11, 2021
Volume68
Issue number4
DOIs
Publication statusPublished - 2021
Externally publishedYes

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