Single-Channel Speech Quality Enhancement in Mobile Networks Based on Generative Adversarial Networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F24%3APU151188" target="_blank" >RIV/00216305:26220/24:PU151188 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1007/s11036-024-02300-4" target="_blank" >https://link.springer.com/article/10.1007/s11036-024-02300-4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11036-024-02300-4" target="_blank" >10.1007/s11036-024-02300-4</a>

Result language

angličtina

Original language name

Single-Channel Speech Quality Enhancement in Mobile Networks Based on Generative Adversarial Networks

Original language description

A large amount of randomly generated noise in mobile networks leads to a lack of targeting and gaming processes in the speech enhancement process, and the enhancement process from the perspective of acoustic features alone suffers from major drawbacks. Propose a single-channel speech quality enhancement method based on generative adversarial networks in mobile networks. Explain the principle of generative adversarial network to realize single-channel speech quality enhancement in mobile networks and clarify its shortcomings. Design an improved Mel frequency cepstral coefficient extraction method to extract 12 base features as the enhancement basis. Use the relative average least squares loss instead of the traditional loss function to enhance the training efficiency, use the hybrid penalty term to enhance the generator's ability to generate single-channel speech, and optimize the discriminator through the multi-layer convolution and the addition of fully connected layers to enhance the speech quality enhancement ability of adversarial generative networks in various aspects, forming a relative average generative adversarial network (RaGAN) based on hybrid penalty term to realize single-channel speech quality enhancement processing. Through the experiment, when the discriminator is applied with the size of a 3*3 convolutional kernel, the best effect of speech quality enhancement is achieved in the mobile network. This method can complete the enhancement of single-channel speech quality in the mobile network, and the effect is significant, which can effectively reduce the noise in the original single-channel speech.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20200 - Electrical engineering, Electronic engineering, Information engineering

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Mobile Networks and Applications

ISSN

1383-469X

e-ISSN

1572-8153

Volume of the periodical

2024

Issue of the periodical within the volume

neuvedeno

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

15

Pages from-to

1-15

UT code for WoS article

001195684700001

EID of the result in the Scopus database

2-s2.0-85189202848