Add-based convolution paves a new way to reduce power consumption of KWS system, as addition is more energy efficient than multiplication at hardware level. On Google Speech Commands dataset V2, Add TC-ResNet achieves an accuracy of 97.1%, with 99% of multiplication operations are replaced by addition operations.
A convolutional neural network was used to extract pertinent features from leaf image datasets that included healthy and diseased leaves. The dataset was ...
Jun 19, 2024 · Here, we take advantage of spiking neural networks' energy efficiency and propose an end-to-end lightweight KWS model.
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Sep 20, 2023 · This paper proposes a resource-efficient keyword spotting (KWS) system based on a convolutional neural network (CNN).
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Jun 19, 2024 · Keyword Spotting (KWS) systems recognize predefined commands, which are always deployed on edge devices as an interface for human-machine ...
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Keyword spotting deals with the identification of keywords in utterances. This repo is a curated list of awesome Speech Keyword Spotting (Wake-Up Word ...
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Jun 20, 2024 · This paper presents a novel spiking neural network architecture called "Global-Local Convolution" for energy-efficient keyword spotting. · The ...
Jun 19, 2024 · Thanks to Deep Neural Networks (DNNs), the accuracy of Key- word Spotting (KWS) has made substantial progress. How- ever, as KWS systems are ...
In this paper we explore a TM based keyword spotting (KWS) pipeline to demonstrate low complexity with faster rate of convergence compared to NNs. 5.
Jul 31, 2023 · In this study, voice features are extracted by utilizing the fast discrete cosine transform (FDCT) for frequency-domain transformation.
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