Training method of noise reduction model, speech noise reduction method, device and electronic equipment

By performing reverberation processing and training on the source noisy speech data, generating multi-channel reverberation noisy speech data, and training the speech noise reduction model, the problems of slow noise reduction speed and poor real-time performance in the existing technology are solved, and efficient speech noise reduction effect is achieved.

CN115240701BActive Publication Date: 2025-10-10BEIJING XIAOMI MOBILE SOFTWARE CO LTD
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Patent Information

Application Number
CN202210828584.6
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2022-07-13
Publication Date
2025-10-10
Estimated Expiration
2042-07-13

AI Technical Summary

Technical Problem

The existing technology has a slow processing speed for noise reduction of noisy speech, poor real-time performance, and cannot guarantee noise reduction efficiency.

Method used

By obtaining the source noisy speech data and performing reverberation processing, multi-channel reverberation noisy speech data is generated as training samples. The multi-channel reverberation noisy speech data is used to train the speech denoising model, including short-time Fourier transform, time-frequency masking and parameter adjustment, to generate a lightweight speech denoising model.

Benefits of technology

It improves the real-time and robustness of the speech noise reduction model, enhances the accuracy of voice data information transmission, reduces the impact of noise, and is suitable for multi-scenario applications.

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Abstract

The present disclosure provides a method for training a noise reduction model, a speech noise reduction method, an apparatus and an electronic device. The method comprises: obtaining source noisy speech data, and performing reverberation processing on the source noisy speech data to generate corresponding training samples, wherein the training samples comprise a plurality of samples, and each sample comprises multi-channel reverberation noisy speech data; training a to-be-trained speech noise reduction model based on the multi-channel reverberation noisy speech data included in the training samples to obtain a trained first speech noise reduction model. In the present disclosure, the human ear has a good listening experience for the noise reduction speech output by the trained speech noise reduction model, improves the propagation accuracy of the information carried in the speech data, reduces the influence of noise on the propagation of the information carried in the speech data, improves the robustness of the speech noise reduction model, strengthens the applicability and practicality of the speech noise reduction model, and optimizes the training method and training effect of the speech noise reduction model.
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