Research On Echo Cancellation Algorithm Of Digital Hearing Aid Based On Bp Neural Network

For hearing impaired patients,hearing aids can effectively enhance hearing level.There exists echo feedback in the use of digital deaf-aid,which seriously affects the use of patients.The LMS algorithm commonly used in the echo cancellation system has poor convergence,and the RLS algorithm with good convergence has a large amount of calculation,and the traditional algorithm is difficult to achieve the desired effect when processing non-stationary signals such as speech signals.The universal approximation theorem shows neural network has the ability to approximate any nonlinear function,which provides ideas for improving the echo cancellation system.This paper proposes an adaptive echo cancellation method based on BP neural network.The main content of the paper is as follows:1.Firstly,the human hearing system and hearing loss are introduced;some kinds of gordian technologies used in digital deaf-aid,including sound source localization,loudness compensation,speech enhancement,and echo cancellation technology,are introduced.Among them,the focus is on the research status of echo cancellation technique.2.In-depth research on echo cancellation algorithms in digital hearing aids,focusing on the principles of the LMS algorithm,NLMS algorithm and RLS algorithm,and comparing the advantages and disadvantages of the three algorithms through simulation experiments and algorithm calculation analysis.Simulation experiments prove that when processing stable signals The above algorithm works well,but when processing non-stationary signals such as speech,the convergence speed and steadystate imbalance have declined.The LMS algorithm,NLMS algorithm,and RLS algorithm cannot effectively deal with non-linear problems.3.The principle and network structure of BP network are introduced,and the learning process of BP algorithm and network parameters are analyzed.An echo cancellation algorithm based on BP neural network is proposed,that is,the adaptive filter weight coefficient is equivalent to the neuron weight value,and the problem of solving the optimal filter weight coefficient is transformed into the training process of BP neural network.An adaptive echo cancellation system based on BP neural network is established.Specific research contents include determining parameters such as input signal,number of hidden layer nodes,transfer function,and number of output layer nodes.The performance of the BP neural network echo cancellation system was studied by experimental simulation.Simulation results show that the algorithm realizes a faster convergence rate while improving stability effectively imbalance,and has a better echo cancellation effect than the traditional algorithm.4.BP neural network is a forward multilayer neural network.Because the network learning results are abnormally sensitive to the initial vector,it is easy to cause the network to fall into a local minimum solution.This paper proposes GA-BP algorithm of genetic algorithm to optimize BP neural network.The genetic algorithm is used to optimize the initial weight and threshold of the neural network,and then trained by the BP neural network.The optimization of the genetic algorithm enhances the global search ability of the BP neural network and improves the prediction accuracy of the network.Simulation results show that the algorithm optimized by BP neural network is not easy to fall into a local minimum after genetic algorithm optimization.It has significantly superior convergence performance and lower steady-state offset.The performance of this algorithm in echo feedback cancellation is better than that of LMS algorithm,NLMS algorithm and RLS algorithm.