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• W2973127906 abstract "This article presents a voice and acoustic activity detector that uses a mixer-based architecture and ultra-low-power neural network (NN)-based classifier. By sequentially scanning 4 kHz of frequency bands and down-converting to below 500 Hz, feature extraction power consumption is reduced by 4×. The NN processor employs computational sprinting, enabling 12× power reduction. The system also features inaudible acoustic signature detection for intentional remote silent wakeup of the system while re-using a subset of the same system components. The measurement results achieve 91.5%/90% speech/non-speech hit rates at 10-dB SNR with babble noise and 142-nW power consumption. Acoustic signature detection consumes 66 nW, successfully detecting a signature 10 dB below the noise level." @default.
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• W2973127906 date "2019-11-01" @default.
• W2973127906 modified "2023-10-11" @default.
• W2973127906 title "An Acoustic Signal Processing Chip With 142-nW Voice Activity Detection Using Mixer-Based Sequential Frequency Scanning and Neural Network Classification" @default.
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• W2973127906 doi "https://doi.org/10.1109/jssc.2019.2936756" @default.
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