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Robustness of LSTM Neural Networks for the Enhancement of Spectral Parameters in Noisy Speech Signals

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Advances in Computational Intelligence (MICAI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11289))

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Abstract

In this paper, we carry out a comparative performance analysis of Long Short-term Memory (LSTM) Neural Networks for the task of noise reduction. Recent work in this area has shown the advantages of this kind of network for the enhancement of noisy speech, particularly when the training process is performed for specific Signal-to-Noise (SNR) levels.

For application in real-life environments, it is important to test the robustness of the approach without the a priori knowledge of the SNR noise levels, as classical signal processing-based algorithms do. In our experiments, we conduct the training stage with single and multiple noise conditions and perform the comparison of the results with the specific SNR training presented previously in the literature.

For the first time, results give a measure on the independence of the training conditions for the task of noise suppression in speech signals, and shows remarkable robustness of the LSTM for different SNR levels.

Supported by the University of Costa Rica.

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Acknowledgments

This work was supported by the Universidad de Costa Rica.

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Authors and Affiliations

  1. PRIS-Lab, Escuela de Ingeniería Eléctrica, San Pedro, Costa Rica

    Marvin Coto-Jiménez

  2. Universidad de Costa Rica, San José, Costa Rica

    Marvin Coto-Jiménez

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  1. Marvin Coto-Jiménez
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Correspondence to Marvin Coto-Jiménez .

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Editors and Affiliations

  1. Instituto Politécnico Nacional, Mexico City, Mexico

    Ildar Batyrshin

  2. Universidad Panamericana, Mexico City, Mexico

    María de Lourdes Martínez-Villaseñor

  3. Faculty of Engineering, Universidad Panamericana, Mexico City, Mexico

    Hiram Eredín Ponce Espinosa

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Coto-Jiménez, M. (2018). Robustness of LSTM Neural Networks for the Enhancement of Spectral Parameters in Noisy Speech Signals. In: Batyrshin, I., Martínez-Villaseñor, M., Ponce Espinosa, H. (eds) Advances in Computational Intelligence. MICAI 2018. Lecture Notes in Computer Science(), vol 11289. Springer, Cham. https://doi.org/10.1007/978-3-030-04497-8_19

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  • DOI: https://doi.org/10.1007/978-3-030-04497-8_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04496-1

  • Online ISBN: 978-3-030-04497-8

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