KTX Noise ANC Performance Evaluation using a Multiple-LMS-based Neural Network

Hyeon Seok Jang, Kung Wan Koo, Young Min Kim, Young Jin Lee, and Kwon Soon Lee


Active Noise Control, Least Mean Square (LMS), Neural Network (NN), High-Speed Rail, Numerical simulation


This paper presents the active noise control (ANC) system based on the least mean square (LMS) algorithm and the neural network algorithm for reducing the interior KTX noise. First, the pure noise of the KTX interior without passengers was measured. Then an LMS framework was constructed as a nominal ANC system, and an artificial single-layered perceptron model was designed as an auxiliary ANC to reduce the real-time residual noise due to the non-stationary and uncertain nature of noise. The parameter vector of the hybrid ANC was determined through online estimation to realize an adaptive ANC configuration by means of the steepest descent algorithm. A simulation experiment was performed to demonstrate the proposed ANC system using the previously mentioned realistic acoustic noise.

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