Deep clustering voice separation method based on improvement

Abstract

The invention relates to a deep clustering voice separation method based on improvement, wherein the method comprises the steps: step 1, mixing experiment data, and extracting logarithm power spectrumfeatures; step 2, building an improved deep clustering voice separation model, and carrying out model training by using a training set; step 3, obtaining an embedded space vector vi from the mixed voice of the test set through the deep clustering speech separation model trained in the step 2, clustering the embedded space vector vi in an embedded subspace through a mean shift clustering method, taking an obtained result as an ideal binary masking value of a training target, and calculating to obtain feature estimation of the two separated voice signals by utilizing the ideal binary masking value and input speech signal features; and step 4, carrying out waveform reconstruction and recovering a voice signal. According to the method, the current voice separation method based on deep clustering is improved, so that the effect of the method is improved under the condition of low signal-to-noise ratio mixed voice input.

Description

technical field [0001] The invention relates to the technical field of speech separation, in particular to a speech separation method based on and improved deep clustering. Background technique [0002] The "cocktail party problem" has always been a difficult problem in the speech separation task, mainly because this problem belongs to a speaker-independent speech separation problem, and the speaker does not know its prior information in advance. By referring to the ideal binary masking-based deep-clustering method (deep-clustering) proposed by Jonathan et al., the present invention improves the clustering method on the basis of it, using a bidirectional long-short-term memory network model and mean shift clustering, And experiments were carried out under the TIMIT speech data set. The final separation results show that in the case of low input signal-to-noise ratio, the separation effect is improved compared with the previous model. [0003] The term speech separation ori...

AI Technical Summary

Problems solved by technology

However, the separation effect of this method is not ideal in the case of low signal-to-noise ratio, so it is necessary to improve the speech separation effect of the deep clustering method in the case of low input signal-to-noise ratio

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