Speech Enhancement Algorithm Based On Deep Learning In Complex Background

Traditional speech enhancement algorithms are often based on the assumption of stationary noise and often fail in complex contexts.enhancement algorithm based on deep learning can suppress non-stationary noise well,but it also shows performance degradation in the unmatched environment.Improving generalization performance requires a lot of data,which means more time and computing resources.Compared with the prior art,this paper attempts to use the Generative adversarial network(GAN)to deal with the problems in speech enhancement,and uses an appropriate amount of training data and common computing resources to construct an end-to-end model,which is generally used for low signal-to-noise and complex noise background.Most of the existing techniques are based on Fourier analysis,which usually ignores the phase information and directly reconstructs the enhanced speech using the phase of the noisy speech,this is not conducive to improving the speech quality at low SNR.In this paper,we use the generative adversarial setting and operate at the original waveform level,try to use the fine-grained information in the waveform(such as phase,alignment,etc.)to obtain higher-quality speech.At the same time,the convolutional neural network is used to share weights and biases to achieve faster train and enhancement.Compared with the original model,the optimized model has a more excellent effect on objective evaluation.Relative to the baseline DNN model,it has performance competing with it,and better generalization performance.The results of the subjective assessment indicate that GAN has gained more audience preferences in the given specific context.