A New VAD Algorithm using Sparse Representation in Spectro-Temporal Domain

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Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:

This paper proposes two algorithms for Voice Activity Detection (VAD) based on sparse representation in spectro-temporal domain. The first algorithm was made using two-dimensional STRF (Spectro-Temporal Response Field) space based on sparse representation. Dictionaries with different atomic sizes and two dictionary learning methods were investigated in this approach. This algorithm revealed good results at high SNRs (signal-to-noise ratio). The second algorithm, whose approach is more complicated, suggests a speech detector using the sparse representation in four-dimensional STRF space. Due to the large volume of STRF's four-dimensional space, this space was divided into cubes, with dictionaries made for each cube separately by NMF (non-negative matrix factorization) learning algorithm. Simulation results were presented to illustrate the effectiveness of our new VAD algorithms. The results revealed that the achieved performance was 90.11% and 91.75% under -5 dB SNR in white and car noise respectively, outperforming most of the state-of-the-art VAD algorithms.

Language:
English
Published:
Journal of Information Systems and Telecommunication, Volume:7 Issue: 1, Jan-Mar 2019
Pages:
74 to 87
https://www.magiran.com/p2053015  
سامانه نویسندگان
  • Corresponding Author (2)
    Mohaddeseh Eshaghi
    Assistant Professor electronic, Central Tehran Branch, Islamic Azad University, Tehran, Iran
    Eshaghi، Mohaddeseh
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