Environment adaptive voice enhancement algorithm based on attention-driven circulating convolution network

Abstract

The invention discloses an environment adaptive voice enhancement algorithm based on an attention-driven circulating convolution network. The environment adaptive voice enhancement algorithm comprisesthe following steps that 1, a voice enhancement task database is selected, and input data preparation is conducted; 2, amplitude information and environment information of voice are extracted, wherein the environment information of the voice is extracted by adopting a weight prediction error (WPE) method, and the amplitude information of the voice is voice spectrum information extracted through Fourier transform; 3, a depth model is constructed and trained; and 4, voice reconstructing is conducted, specifically, voice amplitude predicted in the step 3 is converted into a voice waveform. According to the environment adaptive voice enhancement algorithm, the environment information of the voice is considered, and environmental adaptability and robustness of the algorithm in different environments are improved; and in the aspect of real voice signal retention, an attention mechanism is fused to construct the attention-driven circulating convolution network, time-sequence context information of the voice is depicted more precisely, and performance of voice enhancement is effectively improved.

Description

technical field [0001] The invention belongs to the technical field of speech enhancement, in particular to an environment-adaptive speech enhancement algorithm based on an attention-driven circular convolution network. Background technique [0002] With the popularization of smart devices and the rapid development of speech recognition technology, speech processing technology has attracted more and more public attention. In a common near-field environment (the speaker is relatively close to the microphone), the performance of speech recognition has reached over 95%, and many speech recognition and speech synthesis technologies have been commercialized. However, in the far-field environment (the speaker is far away from the microphone), there is often the influence of reverberation and various background noises, and the performance of speech recognition drops sharply. In the far-field environment, since the speaker does not need to hold a microphone or wear a microphone dev...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the problem that the existing speech enhancement model is difficult to adapt to different noise environments, the present invention proposes an environment-adaptive speech enhancement algorithm based on attention-driven circular convolution network, thereby improving the environmental adaptability of the algorithm in different environments Algorithmic Robustness

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