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An energy-efficient voice activity detector using reconfigurable Gaussian base normalization deep neural network

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Abstract

This research paper proposed deep neural networks and approximation computation are used to create an energy-efficient voice activity detector (VDA). The proposed technique is split up into two parts: feature extraction and voice/noise classification using a deep neural network with Gaussian basis normalization (GNDNN). Pre-processing of input data initially: the digitalized speech signal’s high-frequency components are pre-emphasized, trying to make it a little less susceptible to finite precision impacts later inside the signal processing. The feature extraction module uses Mel-frequency cepstral coefficients (MFCC), time-frequency non-negative matrix factorization (TFNMF), to extract the input speech signals feature value. The TFNMF, MFCC output from feature extraction is classified by the GNDNN speech prediction phase, which evaluates whether the signal is indeed a voice or noise. The proposed approach can be dynamically changed to meet various computing accuracy demands. Our proposed approach most exciting accuracy result of 98.75%. Comparable to the CNN and DNN, which achieves the accuracy of 97.25%, 95.25%, and EERA had the worst accuracy 88.75%. The results of the experiments show that our proposed strategy outperforms previous methods.

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Data availability

Datasets for this research are included in [26].

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Brainware University, Barasat, Kolkata, West Bengal, India

    Anu Samanta

  2. Mathworks India Private Limited, Bangalore, India

    Indranil Hatai

  3. Department of Electronics and Communication Engineering, NIT Durgapur, Durgapur, West Bengal, India

    Ashis Kumar Mal

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  1. Anu Samanta
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  2. Indranil Hatai
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  3. Ashis Kumar Mal
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Correspondence to Anu Samanta.

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Samanta, A., Hatai, I. & Mal, A.K. An energy-efficient voice activity detector using reconfigurable Gaussian base normalization deep neural network. Multimed Tools Appl 82, 27861–27882 (2023). https://doi.org/10.1007/s11042-023-14699-1

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