电子器件2024，Vol.47Issue(2)：536-543,8.DOI:10.3969/j.issn.1005-9490.2024.02.037
基于神经网络的Bark域图形均衡器的设计
Design of Graphic Equalizer of Bark Domain Based on BP Neural Network
沈子扬 1王明玉 1戴海玲2
作者信息
- 1. 南京理工大学新能源学院,江苏 江阴 214443
- 2. 东南大学信息科学与工程学院,江苏南京 210096
- 折叠
摘要
Abstract
A method based on back propagation(BP)neural network is described to simplify the design of graphic equalizer without sacrifi-cing approximation accuracy.Its core idea is to train a neural network to predict the mapping relationship between the target gain and the optimized bandpass gain at the specified center frequency.In the case of a 24-channel Bark band graphic equalizer,the data fitting func-tion of the BP neural network with a hidden layer of 48 neurons is used to realize the prediction.Then,the closed formula is used to calcu-late the coefficients of the band filter quickly and easily.The precise control method of using a least square method to obtain the optimal gain of the infinite impulse response(IIR)filter is introduced and continued to be improved.BP neural network and target gain are used to obtain the optimal gain of the parameter equalizer,greatly reducing the amount of calculation and making the approximation error less than 0.1 dB.The resulting neural controlled 24-channel Bark domain graphic equalizer is very useful in audio conference equalization requiring time-varying equalization.关键词
图形均衡器/BP神经网络/IIR滤波器Key words
graphic equalizer/BP neural network/IIR filter分类
电子信息工程引用本文复制引用
沈子扬,王明玉,戴海玲..基于神经网络的Bark域图形均衡器的设计[J].电子器件,2024,47(2):536-543,8.
