SCSS (Single Channel Speech Separation) algorithm based on DNN (Deep Neural Network)

Abstract

The invention provides an SCSS (Single Channel Speech Separation) algorithm based on a DNN (Deep Neural Network). The SCSS algorithm based on the DNN mainly comprises the following steps of preprocessing a training speech sample, and extracting feature information of the training speech sample; training the DNN by using a loss function so as to obtain a DNN model; preprocessing a speech sample tobe tested; extracting feature information of the speech sample to be tested; performing speech separation through the trained DNN model; and then obtaining a separation result through speech reconstruction. The DNN is trained by using the nonlinear relationship between input and output. Compared with a conventional separation method based on single-output DNN, the SCSS algorithm has the advantagesthat the combined relationship between the output is sufficiently mined; the separation efficiency is high; two source speech signals can be separated in one step; the voice distortion rate is effectively reduced; and meanwhile, the understandability of the separation speech is improved.

Description

technical field [0001] The invention relates to a single-channel speech separation algorithm based on a deep neural network, belonging to the field of speech separation. Background technique [0002] Single channel speech separation (Single channel speech separation, SCSS) is the process of recovering multiple speech from one-dimensional mixed speech. Single-channel speech separation technology is widely used in speech enhancement, preprocessing of speech recognition, hearing aids or smart home and other fields. In these fields, the sensor usually receives a mixed voice from a microphone, and the human ear can easily obtain useful information from this mixed voice, but it is difficult for a computer to accurately obtain the desired voice. Therefore, it has very important practical significance to obtain the target speech accurately and efficiently. [0003] Deep neural network (DNN) has powerful data mining capabilities. In the field of speech separation, it is mainly used...

AI Technical Summary

Problems solved by technology

When using a single-output DNN to separate mixed voices, only one voice can be separated at a time. When using this method to separate multiple voices, it takes a long time; the traditional method based on a multi-output deep neural network can separate multiple voices at the same time. However, for this reason, the output mapped by the multi-output DNN is not as targeted as the single-output DNN, and the separation effect is worse than that of the single-output DNN.

Both of the above two deep neural networks need to be trained by a loss function. The basic loss function used by the traditional dual-output DNN is only used to map the relationship between input and output, but ignores the joint relationship between outputs, and this joint relation has a large impact on the final speech separation performance

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