Voice noise reduction method and device, equipment and medium
A speech noise reduction and speech technology, applied in the field of machine learning, can solve the problems of high system requirements, difficult to control the model scale, occupying a large amount of resources, etc., to achieve high noise reduction efficiency, reduce computational complexity, and reduce the amount of computation.
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Embodiment 1
[0050] Embodiment 1 provides a speech noise reduction method, aiming to realize speech noise reduction through frequency band gain coefficients.
[0051] Please refer to figure 1 Shown, a kind of speech denoising method comprises the following steps:
[0052] S110, acquiring voice data;
[0053] In order to realize real-time speech noise reduction, a frame of speech data is collected every 10ms in this embodiment, and the sampling rate is 48kHz.
[0054] Of course, in the case of non-real-time speech noise reduction, it is only necessary to divide the speech data into frames and perform noise reduction processing on the speech data frame by frame.
[0055] The source of the voice data is, for example, a voice data stream in a noisy environment obtained by a robot microphone, and this embodiment does not limit the specific source.
[0056] S120. Perform preprocessing on the voice data, and extract multi-dimensional features of the preprocessed voice data;
[0057] The above...
Embodiment 2
[0089] Embodiment 2 mainly explains and explains the construction process of the preset speech noise reduction model, and aims to maintain all necessary basic signal processing without neural network simulation by combining traditional signal processing methods and deep learning methods of recurrent neural networks , and learn all the work that needs repeated parameter adjustment through the neural network to realize the construction of the speech noise reduction model.
[0090] Compared with other deep learning neural networks, the recurrent neural network (RNN) adds time series and can be better applied in the field of speech processing technology; therefore, this embodiment selects the recurrent neural network as the preset speech noise reduction model.
[0091] Please refer to image 3 As shown, the training process of the preset speech noise reduction model includes the following steps:
[0092] S210. Obtain a pre-built cyclic neural network, which includes 3 fully conne...
Embodiment 3
[0111] Embodiment 3 discloses a device corresponding to the speech noise reduction method of the above embodiment, which is the virtual device structure of the above embodiment, please refer to Figure 4 shown, including:
[0112] Obtaining module 310, for obtaining voice data;
[0113] The filtering module 320 is used to preprocess the voice data, extract the multi-dimensional features and voice activity detection parameters of the preprocessed voice data; when the voice activity detection parameter is 1, divide the voice data into For several frequency bands, filter the noise data in the frequency band according to the frequency band gain coefficient; when the voice activity detection parameter is 0, set the frequency band gain coefficient to 0, and filter the noise data in the frequency band;
[0114] The output module 330 is configured to restore the filtered voice data into a voice data stream, and output the voice data stream.
[0115] Preferably, the preset speech noi...
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