高阶声学相对传递函数的参数化辨识

Parametric identification of high-order acoustic relative transfer function

针对声学相对传递函数的模型阶次高以及辨识数据信噪比低的问题,文章运用频域系统辨识方法,建立了一种相对传递函数的高精度参数化辨识方法。首先建立变量带误差辨识框架,采用周期扫频信号作为声源激励,给出声学相对传递函数的极大似然算法。然后使用正交福赛斯(Forsythe)多项式解决高阶系统带来的雅克比矩阵条件数过高的数值问题,并建立了极大似然方法所需初始值的生成策略。最后通过仿真算例和实验测试,验证文中方法在混响声场下辨识声学相对传递函数的有效性。

To solve the problems of high model order of acoustic relative transfer function and low signal to noise ratio of identification data,a highly accurate parametric identification method of acoustic relative transfer functions is proposed by means of frequency domain system identification.Firstly,an errors-in-variables identification framework is established,and by taking a periodic chirp signal as sound source excitation,the maximum likelihood formulation is given to estimate acoustic relative transfer functions.Then,the orthogonal Forsythe polynomial is used to solve the numerical problem of excessive number of Jacobian matrix conditions caused by high-order systems,and a strategy for generating the initial values required by the maximum likelihood method is provided.Finally,the effectiveness of the proposed method in identifying the acoustic relative transfer function under reverberant environment is verified by simulation example and experimental tests.