A single-channel real-time noise reduction method based on convolutional recurrent neural network

A recursive neural network and convolutional neural network technology, applied in the field of computer applications, can solve problems such as large number of network parameters and complex models, and achieve the effect of reducing the number of parameters, reducing the amount of data storage, and good noise reduction performance

Active Publication Date: 2020-10-16
ELEVOC TECH CO LTD
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Problems solved by technology

[0004] In order to solve the technical problems of the large number of network parameters and complex models of single-channel speech noise reduction in the related art, the present disclosure provides a single-channel real-time noise reduction method, device and terminal based on convolutional recurrent neural network

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  • A single-channel real-time noise reduction method based on convolutional recurrent neural network
  • A single-channel real-time noise reduction method based on convolutional recurrent neural network
  • A single-channel real-time noise reduction method based on convolutional recurrent neural network

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[0056]Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numerals in different drawings refer to the same or similar elements unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with aspects of the invention as recited in the appended claims.

[0057] figure 1 It is a flow chart of a single-channel real-time noise reduction method based on a convolutional recurrent neural network according to an exemplary embodiment. The single-channel real-time noise reduction method based on the convolutional recurrent neural network can be used in electronic devices such as smart phones and computers. Such as figure 1 As shown, the single-channel real...

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Abstract

A convolutional recurrent neural network-based single-channel real-time noise reduction method. Said method comprises: extracting acoustic features from a received single-channel sound signal (S110); performing an iterative operation on the acoustic features in a pre-trained convolutional recurrent neural network model, so as to calculate a ratio mask of the acoustic features (S120); masking the acoustic features by means of the ratio mask (S130); synthesizing the masked acoustic features and phases of the single-channel sound signal, so as to obtain a voice signal (S140). The method is able to decrease the number of neural network parameters, reduce the data storage amount and the demand for system data bandwidth, achieve good noise reduction performance, and improve the real-time performance of single-channel voice noise reduction.

Description

technical field [0001] The present disclosure relates to the field of computer application technology, and in particular to a convolutional recurrent neural network-based single-channel real-time noise reduction method, device, electronic equipment, and storage medium. Background technique [0002] Speech noise reduction refers to separating the target speech signal from the background noise so as to eliminate or suppress the background noise. Single-channel speech is a speech signal generated only by relying on a single microphone recording. Compared with beamforming-based noise reduction techniques (i.e., spatial filtering through appropriate configuration of microphone arrays), single-channel speech noise reduction can be applied to a wider range of acoustic scenarios. . Single-channel speech noise reduction is not only cost-effective, but also easier to use in practical situations. Additionally, single-channel speech separation can be used to enhance the effects of bea...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G10L21/0208G10L25/30
CPCG10L21/0208G10L25/30
Inventor 不公告发明人
Owner ELEVOC TECH CO LTD
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