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Speech enhancement is always a hot topic in the field of speech processing. In real life, interference signals are usually non-stationary or even burst. Therefore, it is of great significance to study speech enhancement technology in non-stationary noise scene to solve practical problems. The speech enhancement algorithm for the following questions, first, is currently on the market more studies on speech enhancement based on the assumption of stationary noise, but in real life, the environment is rapidly changing, noise cannot be ideally unchanged, second, because of the non-stationary noise is hard to predict, in the case of low SNR, it is difficult to maintain the signal distortion after speech enhancement. Aiming at the above problems, this paper proposes an adaptive speech enhancement method based on SNR perception in non-stationary noise scenes to improve the adaptability of speech enhancement in complex non-stationary scenes. At the same time, the neural network is designed to calculate the signal-to-noise ratio and improve the processing capability of the model. |
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Keywords:artificial intelligence; time-domain speech enhancement; attention mechanism; full convolutional neural network |
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