The reconstruction of background noise from an error signal of an adaptive filter is a key issue for developing Variable Step-Size Normalized Least Mean Square (VSS-NLMS) algorithm in the context of Echo Cancellation ...The reconstruction of background noise from an error signal of an adaptive filter is a key issue for developing Variable Step-Size Normalized Least Mean Square (VSS-NLMS) algorithm in the context of Echo Cancellation (EC). The core parameter in this algorithm is the Background Noise Power (BNP); in the estimation of BNP, the power difference between the desired signal and the filter output, statistically equaling to the error signal power, has been widely used in a rough manner. In this study, a precise BNP estimate is implemented by multiplying the rough estimate with a corrective factor, taking into consideration the fact that the error signal consists of background noise and misalignment noise. This corrective factor is obtained by subtracting half of the latest VSS value from 1 after analyzing the ratio of BNP to the misalignment noise. Based on the precise BNP estimate, the PVSS-NLMS algorithm suitable for the EC system is eventually proposed. In practice, the proposed algorithm exhibits a significant advantage of easier controllability application, as prior knowledge of the EC environment can be neglected. The simulation results support the preciseness of the BNP estimation and the effectiveness of the proposed algorithm.展开更多
Forward-backward algorithm, used by watermark decoder for correcting non-binary synchronization errors, requires to traverse a very large scale trellis in order to achieve the proper posterior probability, leading to ...Forward-backward algorithm, used by watermark decoder for correcting non-binary synchronization errors, requires to traverse a very large scale trellis in order to achieve the proper posterior probability, leading to high computational complexity. In order to reduce the number of the states involved in the computation, an adaptive pruning method for the trellis is proposed. In this scheme, we prune the states which have the low forward-backward quantities below a carefully-chosen threshold. Thus, a wandering trellis with much less states is achieved, which contains most of the states with quite high probability. Simulation results reveal that, with the proper scaling factor, significant complexity reduction in the forward-backward algorithm is achieved at the expense of slight performance degradation.展开更多
文摘The reconstruction of background noise from an error signal of an adaptive filter is a key issue for developing Variable Step-Size Normalized Least Mean Square (VSS-NLMS) algorithm in the context of Echo Cancellation (EC). The core parameter in this algorithm is the Background Noise Power (BNP); in the estimation of BNP, the power difference between the desired signal and the filter output, statistically equaling to the error signal power, has been widely used in a rough manner. In this study, a precise BNP estimate is implemented by multiplying the rough estimate with a corrective factor, taking into consideration the fact that the error signal consists of background noise and misalignment noise. This corrective factor is obtained by subtracting half of the latest VSS value from 1 after analyzing the ratio of BNP to the misalignment noise. Based on the precise BNP estimate, the PVSS-NLMS algorithm suitable for the EC system is eventually proposed. In practice, the proposed algorithm exhibits a significant advantage of easier controllability application, as prior knowledge of the EC environment can be neglected. The simulation results support the preciseness of the BNP estimation and the effectiveness of the proposed algorithm.
基金supported in part by National Natural Science Foundation of China (61101114, 61671324) the Program for New Century Excellent Talents in University (NCET-12-0401)
文摘Forward-backward algorithm, used by watermark decoder for correcting non-binary synchronization errors, requires to traverse a very large scale trellis in order to achieve the proper posterior probability, leading to high computational complexity. In order to reduce the number of the states involved in the computation, an adaptive pruning method for the trellis is proposed. In this scheme, we prune the states which have the low forward-backward quantities below a carefully-chosen threshold. Thus, a wandering trellis with much less states is achieved, which contains most of the states with quite high probability. Simulation results reveal that, with the proper scaling factor, significant complexity reduction in the forward-backward algorithm is achieved at the expense of slight performance degradation.
