Signal Processing
Multirate systems and filter banks
Multirate systems and filter banks
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Adaptation of a memoryless preprocessor for nonlinear acoustic echo cancelling
Signal Processing - Special issue on current topics in adaptive filtering for hands-free acoustic communication and beyond
Time series: data analysis and theory
Time series: data analysis and theory
Nonlinear acoustic echo cancellation with 2nd order adaptive Volterra filters
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02
Discrete-time speech signal processing: principles and practice
Discrete-time speech signal processing: principles and practice
Adaptive nonlinear system identification in the short-time fourier transform domain
IEEE Transactions on Signal Processing
Modeling and identification of nonlinear systems in the short-time fourier transform domain
IEEE Transactions on Signal Processing
Efficient algorithms for Volterra system identification
IEEE Transactions on Signal Processing
Penalized least squares estimation of Volterra filters and higherorder statistics
IEEE Transactions on Signal Processing
Nonlinear system identification using Gaussian inputs
IEEE Transactions on Signal Processing
A new approach to subband adaptive filtering
IEEE Transactions on Signal Processing
System Identification in the Short-Time Fourier Transform Domain With Crossband Filtering
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Audio, Speech, and Language Processing
Quantifying the accuracy of Hammerstein model estimation
Automatica (Journal of IFAC)
Identification of linear systems with nonlinear distortions
Automatica (Journal of IFAC)
| Hi-index | 35.68 |
In this paper, we introduce an estimation error analysis for quadratically nonlinear system identification in the short-time Fourier transform (STFT) domain. The identification scheme consists of a parallel connection of a linear component, represented by crossband filters between subbands, and a quadratic component, which is modeled by multiplicative cross-terms. We mainly concentrate on two types of undermodeling errors. The first is caused by employing a purely linear model in the estimation process (i.e., nonlinear undermodeling), and the second is a consequence of restricting the number of estimated crossband filters in the linear component. We derive analytical relations between the noise level, nonlinearity strength, and the obtainable mean-square error (mse) in subbands. We show that for low signal-to-noise ratio (SNR) conditions, a lower mse is achieved by allowing for nonlinear undermodeling and utilizing a purely linear model. However, as the SNR increases, the performance can be generally improved by incorporating a nonlinear component into the model. We further show that as the SNR increases, a larger number of crossband filters should be estimated to attain a lower mse, whether a linear or nonlinear model is employed. Experimental results support the theoretical derivations.