Convex combination of adaptive filters for a variable tap-length LMS algorithm

A convex combination of adaptive filters is utilized to improve the performance of a variable tap-length least-mean-square (LMS) algorithm in a low signal-to-noise environment (SNR 0 dB). As shown by our simulations, the adaptation of the tap-length in the variable tap-length LMS algorithm is highly affected by the parameter choice and the noise level. Combination approaches can improve such adaptation by exploiting advantages of parallel adaptive filters with different parameters. Simulation results support the good properties of the proposed method

Variable tap-length adaptive algorithm which exploits both second and fourth order statistics.

A new variable tap-length adaptive algorithm which exploits both second and fourth order statistics is proposed in this paper. In this algorithm, the tap-length of the adaptive filter is varying rather than fixed, and controlled by fourth order statistics, whereas the coefficient update retains a conventional least mean square (LMS) form. As will be seen in the simulation results, the proposed algorithm has a faster convergence rate as compared with an existing variable tap-length LMS algorithm which is based only on second order statistics in sub-Gaussian noise environments

A new gradient based variable step-size LMS algorithm

A new gradient-based variable step-size LMS algorithm (VSSLMS) is proposed in this paper. The step size of the proposed algorithm is proportional to the squared norm of the smoothed gradient vector, which is a proximity measure of the adaptive process. In comparison with the existing methods, the proposed VSSLMS algorithm has improved convergence properties. Furthermore, the parameter choice of the proposed algorithm can be easily determined according to the analysis. Simulation results show the good properties of the proposed algorithm and support the performance analysis

Variable length adaptive filtering within incremental learning algorithms for distributed networks

In this paper we propose the use of variable length adaptive filtering within the context of incremental learning for distributed networks. Algorithms for such incremental learning strategies must have low computational complexity and require minimal communication between nodes as compared to centralized networks. To match the dynamics of the data across the network we optimize the length of the adaptive filters used within each node by exploiting the statistics of the local signals to each node. In particular, we use a fractional tap-length solution to determine the length of the adaptive filter within each node, the coefficients of which are adapted with an incremental-learning learning algorithm. Simulation studies are presented to confirm the convergence properties of the scheme and these are verified by theoretical analysis of excess mean square error and mean square deviation

Multimodal blind source separation for moving sources

A novel multimodal approach is proposed to solve the problem of blind source separation (BSS) of moving sources. The challenge of BSS for moving sources is that the mixing filters are time varying, thus the unmixing filters should also be time varying, which are difficult to track in real time. In the proposed approach, the visual modality is utilized to facilitate the separation for both stationary and moving sources. The movement of the sources is detected by a 3-D tracker based on particle filtering. The full BSS solution is formed by integrating a frequency domain blind source separation algorithm and beamforming: if the sources are identified as stationary, a frequency domain BSS algorithm is implemented with an initialization derived from the visual information. Once the sources are moving, a beamforming algorithm is used to perform real time speech enhancement and provide separation of the sources. Experimental results show that by utilizing the visual modality, the proposed algorithm can not only improve the performance of the BSS algorithm and mitigate the permutation problem for stationary sources, but also provide a good BSS performance for moving sources in a low reverberant environment

Multimodal blind source separation for moving sources

A novel multimodal approach is proposed to solve the problem of blind source separation (BSS) of moving sources. The challenge of BSS for moving sources is that the mixing filters are time varying, thus the unmixing filters should also be time varying, which are difficult to track in real time. In the proposed approach, the visual modality is utilized to facilitate the separation for both stationary and moving sources. The movement of the sources is detected by a 3-D tracker based on particle filtering. The full BSS solution is formed by integrating a frequency domain blind source separation algorithm and beamforming: if the sources are identified as stationary, a frequency domain BSS algorithm is implemented with an initialization derived from the visual information. Once the sources are moving, a beamforming algorithm is used to perform real time speech enhancement and provide separation of the sources. Experimental results show that by utilizing the visual modality, the proposed algorithm can not only improve the performance of the BSS algorithm and mitigate the permutation problem for stationary sources, but also provide a good BSS performance for moving sources in a low reverberant environment

Steady-state performance analysis of a variable tap-length LMS algorithm

A steady-state performance analysis of the fractional tap-length (FT) variable tap-length least mean square (LMS) algorithm is presented in this correspondence. Based on the analysis, a mathematical formulation for the steady-state tap length is obtained. Some general criteria for parameter selection are also given. The analysis and the associated discussions give insight into the performance of the FT algorithm, which may potentially extend its practical applicability. Simulation results support the theoretical analysis and discussions

Steady-state performance of incremental learning over distributed networks for non-Gaussian data.

In this paper, the steady-state performance of the distributed least mean-squares (dLMS) algorithm within an incremental network is evaluated without the restriction of Gaussian distributed inputs. Computer simulations are presented to verify the derived performance expressions

A new variable tap-length LMS algorithm to model an exponential decay impulse response

This letter proposes a new variable tap-length least-mean-square (LMS) algorithm for applications in which the unknown filter impulse response sequence has an exponential decay envelope. The algorithm is designed to minimize the mean-square deviation (MSD) between the optimal and adaptive filter weight vectors at each iteration. Simulation results show the proposed algorithm has a faster convergence rate as compared with the fixed tap-length LMS algorithm and is robust to the initial tap-length choice

Evaluation of emerging frequency domain convolutive blind source separation algorithms based on real room recordings

This paper presents a comparative study of three of the emerging frequency domain convolutive blind source separation (FDCBSS) techniques i.e. convolutive blind separation of non-stationary sources due to Parra and Spence, penalty function-based joint diagonalization approach for convolutive blind separation of nonstationary sources due to Wang et al. and a geometrically constrained multimodal approach for convolutive blind source separation due to Sanei et al. Objective evaluation is performed on the basis of signal to interference ratio (SIR), performance index (PI) and solution to the permutation problem. The results confirm that a multimodal approach is necessary to properly mitigate the permutation in BSS and ultimately to solve the cocktail party problem. In other words, it is to make BSS semiblind by exploiting prior geometrical information, and thereby providing the framework to find robust solutions for more challenging source separation with moving speakers