Echo and noise suppression algorithm based on BLSTM Neural Network

Considering the influence of nonlinear echo and non-stationary noise on the echo cancellation algorithm of intelligent equipment, this paper proposes an echo and noise suppression algorithm based on bidirectional long short-term memory neural network. Firstly, the multi-target preprocessing model is used to estimate the amplitude spectrum of echo and noise signals synchronously. Then it is used as the input feature of echo and noise suppression model to estimate the ideal ratio mask of target speech signal. Finally, the optimal echo and noise suppression models are obtained through the joint training of the two models. The experimental results show that the proposed algorithm has better echo and noise suppression effects and less speech distortion in the environment of nonlinear echo and non-stationary noise.