Echo elimination method based on deep learning

Abstract

The invention discloses an echo elimination method based on deep learning, an echo elimination device based on deep learning, electronic equipment and storage medium, and belongs to the technical field of a computer. The method comprises the following steps of extracting acoustic features from received microphone signals, wherein the microphone signals comprise near-end signals and remote-end signals; performing iterative operation on the acoustic features in a pretrained recursive neural network model with long short-term memory; calculating a ratio membrane of the acoustic features; using the ratio membrane for shielding the acoustic features; and synthesizing the shielded acoustic features with the phase of the microphone signals to obtain near end signals subjected to echo elimination.The echo elimination method based on deep learning and the echo elimination device based on deep learning can realize the echo elimination under the conditions of the background noise, double talking, nonlinear distortion and the like; the echo elimination effect is greatly improved; the application scenes are greatly increased; the use of a post-filter is not needed; the electronic equipment iseffectively simplified; and the electronic equipment cost is reduced.

Description

Technical field [0001] The present disclosure relates to the field of computer application technology, and in particular to an echo cancellation method, device, electronic equipment, and storage medium based on deep learning. Background technique [0002] In a communication system, when a speaker and a microphone are coupled, the microphone will pick up the signal from the speaker and its reverberation, thereby generating echo. For example, conference calls, hands-free phones and mobile communications all suffer from echo. [0003] Echo cancellation faces many problems, such as dual talk, background noise, and nonlinear distortion. First of all, dual talk is a typical way of dialogue in communication systems, and speakers at both ends sometimes speak at the same time. However, near-end speech signals will seriously affect the convergence of the adaptive algorithm and may cause them to diverge. In addition, the signal received at the microphone contains not only echo and near-end...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the technical problem that the effect of echo cancellation in the related art is not good and a post filter is req

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