ERBM-SEExtended Restricted Boltzmann Machine for Multi-Objective Single-Channel Speech Enhancement

  1. Muhammad Irfan Khattak 1
  2. Nasir Saleem 2
  3. Aamir Nawaz 2
  4. Aftab Ahmed Almani 3
  5. Farhana Umer 4
  6. Elena Verdú 5
  1. 1 University of Engineering & Technology, Peshawar (Pakistan)
  2. 2 Gomal University
    info

    Gomal University

    Dera Ismāīl Khān, Pakistán

    ROR https://ror.org/0241b8f19

  3. 3 Shandong University
    info

    Shandong University

    Jinan, China

    ROR https://ror.org/0207yh398

  4. 4 Islamia University
  5. 5 Universidad Internacional de La Rioja
    info

    Universidad Internacional de La Rioja

    Logroño, España

    ROR https://ror.org/029gnnp81

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Revista:
IJIMAI

ISSN: 1989-1660

Any de publicació: 2022

Volum: 7

Número: 4

Pàgines: 185-195

Tipus: Article

DOI: 10.9781/IJIMAI.2022.03.002 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Altres publicacions en: IJIMAI

Resum

Machine learning-based supervised single-channel speech enhancement has achieved considerable research interest over conventional approaches. In this paper, an extended Restricted Boltzmann Machine (RBM) is proposed for the spectral masking-based noisy speech enhancement. In conventional RBM, the acoustic features for the speech enhancement task are layerwise extracted and the feature compression may result in loss of vital information during the network training. In order to exploit the important information in the raw data, an extended RBM is proposed for the acoustic feature representation and speech enhancement. In the proposed RBM, the acoustic features are progressively extracted by multiple-stacked RBMs during the pre-training phase. The hidden acoustic features from the previous RBM are combined with the raw input data that serve as the new inputs to the present RBM. By adding the raw data to RBMs, the layer-wise features related to the raw data are progressively extracted, that is helpful to mine valuable information in the raw data. The results using the TIMIT database showed that the proposed method successfully attenuated the noise and gained improvements in the speech quality and intelligibility. The STOI, PESQ and SDR are improved by 16.86%, 25.01% and 3.84dB over the unprocessed noisy speech.

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Fuente de los datos: Dialnet