Dilated causal convolution generative adversarial network end-to-end bone conduction speech blind enhancement method

Abstract

The invention relates to the field of artificial intelligence and medical rehabilitation instruments, aims to provide an end-to-end bone conduction speech enhancement method, and solves the problems of the absence of high-frequency components of bone conduction speech, poor auditory perception, communication under the background of strong noise and the like. According to the dilated causal convolution generative adversarial network end-to-end bone conduction speech blind enhancement method, a bone conduction original audio sampling point is taken as input data, a pure air conduction original audio is taken as an output target of training, bone conduction speech is input into a trained dilated causal convolution generative adversarial network, the dilated causal convolution generative adversarial network comprises a generator and a discriminator, the generator adopts dilated causal convolution, and an enhanced sample is output; the discriminator inputs original audio data and the enhanced speech sample generated by the generator, and a convolution layer in the discriminator is used for extracting deep nonlinear features, thereby performing deep similarity judgment of the sample. Thedilated causal convolution generative adversarial network end-to-end bone conduction speech blind enhancement method is mainly applied to the design and manufacturing occasion of bone conduction speech enhancement equipment.

Description

technical field [0001] The invention relates to the field of artificial intelligence, in particular to a training method and system for an end-to-end speech enhancement model. Specifically, it involves an end-to-end bone-conducted speech blind enhancement method for atrous causal convolutional generative adversarial networks. Background technique [0002] The difference in the way Bone Conducted Microphone (BCM) and traditional Air Conducted Microphone (ACM) transmit sound is that the sound collected by BCM is not transmitted through the air, but collected from human bones by a highly sensitive vibration sensor. or tissue vibrations, which are then converted into audio signals. Its advantage is that the noise is shielded from the sound source, and the transmission of ambient noise at both ends of the communication system is prevented from the source, so even in a strong noise environment, useful signals can be clearly transmitted. Although the bone conduction (AC, Air Cond...

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Problems solved by technology

[0007] In order to overcome the deficiencies in the prior art, the present invention aims to propose an end-to-end bone conduction speech enhancement algorithm based on the hole causal convolution generation confrontation network, and the proposed system uses end-to-end (ie waveform input and waveform output) Speech enhancement is carried out by means of training, the best network model parameters are obtained through training, and then the trained model is used to enhance bone conduction speech, so as to solve the problems of lack of high-frequency components of bone conduction speech, poor auditory perception and communication under strong noise background

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