Adaptive active noise control system based on deep neural network and method thereof

Abstract

The invention relates to an adaptive active noise control system and method based on a deep neural network, and the system is characterized in that the system comprises a reference microphone, a controller, an actuator, and an error microphone, and the controller comprises a deep neural network module and a drive circuit; the reference microphone is arranged near a noise source and is used for collecting a reference signal; the error microphone is arranged at a control point and is used for collecting an error signal; the deep neural network module generates a control signal with the same amplitude and opposite phase as a to-be-controlled noise signal, updates network parameters, and outputs the generated control signal to the drive circuit; the drive circuit outputs the control signal tothe actuator; and the actuator converts the control signal into a control sound wave, and the control sound wave is superposed with the to-be-controlled noise at the control point to carry out activenoise elimination. According to the system and the method, an RNN recurrent neural network and an MLP multilayer perceptron network are combined, so that the defect that nonlinear noise cannot be controlled by a minimum mean square error algorithm is overcome, and the application range of an active noise control technology is expanded.

Description

technical field [0001] The invention belongs to the technical field of active noise control, in particular to an adaptive active noise control system and method based on a deep neural network. Background technique [0002] Noise pollution is an environmental problem of great concern all over the world. General noise interference will affect people's normal life and work, and long-term exposure to high noise environments will cause serious harm to people's mental and physical health. Therefore, noise control has long been an important task. [0003] From a strategic point of view, the traditional noise control is mainly based on the acoustic control method of noise, and the technical means include sound absorption treatment, sound insulation treatment, vibration isolation and reduction, etc. The mechanism of these methods is to make noise sound waves interact with acoustic materials or structures to consume sound energy, thereby achieving the purpose of reducing noise, whic...

AI Technical Summary

Problems solved by technology

[0012] The purpose of the present invention is to overcome the deficiencies of the prior art, to propose an adaptive active noise control system based on a deep neural network and its method, and to solve the problem that the mainstream minimum mean square error algorithm is not suitable for nonlinear noise

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