<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>Many speech processing systems’ crucial frontends include speech enhancement. Single-channel speech enhancement experiences a number of technological challenges. Due to the advent of cloud-based technology and the use of deep learning systems in big data, deep neural networks in particular have recently been seen as a potent means for complex classification and regression. In this work, spectral gating noise filter is combined with deep neural network U-Net to enhance the performance of speech enhancement network. Further, for performance analysis three distinct objective functions namely, Mean Square Error, Huber Loss and Mean Absolute Error are considered as loss functions. In addition, comparison of three different optimizers Adam, Adagrad and Stochastic Gradient Descent is presented. Proposed system is tested and evaluated on LibriSpeech and NOIZEUS datasets and compared to other state-of-the-art systems. It demonstrates that, in comparison to other state-of-the-art models, the proposed network outperformed them with PESQ scores of 2.737420 for training and 2.67857 for testing, along with better generalization ability.</p>
</abstract>

Many speech processing systems’ crucial frontends include speech enhancement. Single-channel speech enhancement experiences a number of technological challenges. Due to the advent of cloud-based technology and the use of deep learning systems in big data, deep neural networks in particular have recently been seen as a potent means for complex classification and regression. In this work, spectral gating noise filter is combined with deep neural network U-Net to enhance the performance of speech enhancement network. Further, for performance analysis three distinct objective functions namely, Mean Square Error, Huber Loss and Mean Absolute Error are considered as loss functions. In addition, comparison of three different optimizers Adam, Adagrad and Stochastic Gradient Descent is presented. Proposed system is tested and evaluated on LibriSpeech and NOIZEUS datasets and compared to other state-of-the-art systems. It demonstrates that, in comparison to other state-of-the-art models, the proposed network outperformed them with PESQ scores of 2.737420 for training and 2.67857 for testing, along with better generalization ability.

Performance analysis of speech enhancement using spectral gating with U-Net

Journal of Electrical Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Performance analysis of speech enhancement using spectral gating with U-Net"}

Many speech processing systems’ crucial frontends include speech enhancement. Single-channel speech enhancement experiences a number of technological...

Performance analysis of speech enhancement using spectral gating with U-Net

Data publikacji: 21 paź 2023

Zakres stron: 365 - 373

Otrzymano: 26 lip 2023

DOI: https://doi.org/10.2478/jee-2023-0044

Słowa kluczowe
speech enhancement, spectral gating, deep neural network, U-Net, optimizers

© 2023 Jharna Agrawal et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Performance analysis of speech enhancement using spectral gating with U-Net

Data publikacji: 21 paź 2023

Zakres stron: 365 - 373

Otrzymano: 26 lip 2023

DOI: https://doi.org/10.2478/jee-2023-0044

Słowa kluczowespeech enhancement, spectral gating, deep neural network, U-Net, optimizers

© 2023 Jharna Agrawal et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
speech enhancement, spectral gating, deep neural network, U-Net, optimizers