Adaptive algorithms for sparse echo cancellation
Signal Processing
Efficient multichannel NLMS implementation for acoustic echo cancellation
EURASIP Journal on Audio, Speech, and Music Processing
Time-domain convolutive blind source separation employing selective-tap adaptive algorithms
EURASIP Journal on Audio, Speech, and Music Processing
An adaptive penalized maximum likelihood algorithm
Signal Processing
Selective partial update and set-membership subband adaptive filters
Signal Processing
Frequency-domain adaptive algorithm for network echo cancellation in VoIP
EURASIP Journal on Audio, Speech, and Music Processing - Intelligent Audio, Speech, and Music Processing Applications
Computers and Electrical Engineering
Adaptive combination of proportionate filters for sparse echo cancellation
IEEE Transactions on Audio, Speech, and Language Processing
Journal of Signal Processing Systems
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Computers and Electrical Engineering
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Multi-delay block frequency domain adaptive filters with sparse partial subblock update
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
EURASIP Journal on Advances in Signal Processing
EURASIP Journal on Advances in Signal Processing - Special issue on recent advances in theory and methods for nonstationary signal analysis
A variable step-size selective partial update LMS algorithm
Digital Signal Processing
Digital Signal Processing
Hi-index | 35.68 |
This article proposes an algorithm for partial update of the coefficients of the normalized least mean square (NLMS) finite impulse response (FIR) adaptive filter. It is shown that while the proposed algorithm reduces the complexity of the adaptive filter, it maintains the closest performance to the full update NLMS filter for a given number of updates. Analysis of the MSE convergence and steady-state performance for independent and identically distributed (i.i.d.) signals is provided for the extreme case of one update/iteration