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Speech enhancement system based on time modeling generative adversarial network

A first-time, time-based technology, applied in biological neural network models, speech analysis, instruments, etc., can solve the problems of speech time-domain feature time dependence and insufficient consideration of the overall situation, so as to achieve reliable design principles, improve auditory quality, The effect of broad application prospects

Active Publication Date: 2022-05-13
QILU UNIV OF TECH
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

[0004] Aiming at the above-mentioned deficiencies in the prior art, the present invention provides a speech enhancement system based on temporal modeling to generate an adversarial network to solve the problem of insufficient consideration of the time-dependence and global aspects of generating adversarial network speech time-domain features

Method used

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  • Speech enhancement system based on time modeling generative adversarial network
  • Speech enhancement system based on time modeling generative adversarial network
  • Speech enhancement system based on time modeling generative adversarial network

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Embodiment Construction

[0036] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described The embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0037] Key terms appearing in the present invention are explained below.

[0038] GRU: Gated Recurrent Unit, a gated recurrent unit, uses a gating mechanism to control input, memory and other information, and makes predictions at the current time.

[0039] figure 1 It shows a speech enhancement system based on temporal modeling genera...

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Abstract

The invention provides a speech enhancement system based on a time modeling generative adversarial network, which belongs to the technical field of speech signal processing and comprises a data acquisition unit used for acquiring noisy speech signals and downsampling the noisy speech signals; and the signal enhancement unit is used for inputting the noisy voice signal into a generative adversarial network based on time modeling, compressing and extracting a global time domain feature of the voice signal, linking the time domain feature and random noise into a feature vector, and decoding the feature vector to obtain an enhanced voice signal. According to the method, the problem that time dependence and global consideration of voice time domain features are insufficient is solved, the noise influence in voice signals is reduced, and therefore the auditory quality of enhanced voice is improved.

Description

technical field [0001] The invention belongs to the technical field of speech signal processing, and in particular relates to a speech enhancement system based on time modeling to generate an adversarial network. Background technique [0002] Speech enhancement is a key technology to improve speech quality and intelligibility. It uses audio signal processing technology to eliminate noise and extract pure speech signals from a noisy observation signal. Currently, speech intelligibility or intelligibility is not reduced. Introducing significant speech distortions remains a formidable challenge. [0003] In recent years, with the rapid development of artificial intelligence technology and computer processing power, deep learning has become a hot technology in many research fields and has achieved many remarkable research results. Due to the limited performance of traditional speech enhancement algorithms such as Wiener filtering and spectral subtraction, deep learning technolo...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G10L21/02G10L25/30G06N3/04
CPCG10L21/02G10L25/30G06N3/045
Inventor 董安明张德辉禹继国韩玉冰李素芳张丽邱静刘洋张滕刘宗银
Owner QILU UNIV OF TECH
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