Hun Choi

Subband affine projection algorithm for acoustic echo cancellation system

We present a new subband affine projection (SAP) algorithm for the adaptive acoustic echo cancellation with long echo path delay. Generally, the acoustic echo canceller suffers from the long echo path and large computational complexity. To solve this problem, the proposed algorithm combines merits of the affine projection (AP) algorithm and the subband filtering. Convergence speed of the proposed algorithm is improved by the signal-decorrelating property of the orthogonal subband filtering and the weight updating with the prewhitened input signal of the AP algorithm. Moreover, in the proposed algorithms, as applying the polyphase decomposition, the noble identity, and the critical decimation to subband the adaptive filter, the sufficiently decomposed SAP updates the weights of adaptive subfilters without a matrix inversion. Therefore, computational complexity of the proposed method is considerably reduced. In the SAP, the derived weight updating formula for the subband adaptive filter has a simple form as ever compared with the normalized least-mean-square (NLMS) algorithm. The efficiency of the proposed algorithm for the colored signal and speech signal was evaluated experimentally.

Time varying harmonics estimation of power signal based on filter banks and adaptive filtering

This paper proposes a new method which combines filter bank system and adaptive filtering for the parameters estimation of the harmonics and the interharmonics of power signals. The proposed method decomposes the input power signal by designing the filter bank system. This filter bank has binary tree structure, and uses the fundamental filter banks successively in each decomposing stage. The fundamental filter bank separates the odd harmonic and the even harmonic components to reduce the spectral leakage. The adaptive filter is applied to the each harmonic component for the accurate parameter estimation. And the interharmonic component parameters are estimated through the error signal of the adaptive filter. The amplitudes and the frequency estimation of the each harmonic and interharmonic are carried out by the recursive method. To demonstrate the performance of the method, computer simulations are performed to the synthesized signals.

Subband Adaptive Filtering with Maximal Decimation Using an Affine Projection Algorithm

Affine projection algorithms perform well for acoustic echo cancellation and adaptive equalization. Although these algorithms typically provide fast convergence, they are unduly complex when updating the weights of the associated adaptive filter. In this paper, we propose a new subband affine projection (SAP) algorithm and a facile method for its implementation. The SAP algorithm is derived by combining the affine projection algorithm and the subband adaptive structure with the maximal decimation. In the proposed SAP algorithm, the derived weight-updating formula for the subband adaptive filter has a simple form as compared with the normalized least mean square (NLMS) algorithm. The algorithm gives improved convergence and reduced computational complexity. The efficiency of the proposed algorithm for a colored input signal is evaluated experimentally.

Design and Implementation of Seismic Data Acquisition System using MEMS Accelerometer

In this paper, we design a seismic data acquisition system(SDAS) and implement it. This system is essential for development of a noble local earthquake disaster preventing system in population center. In the system, we choose a proper MEMS-type triaxial accelerometer as a sensor, and FPGA and ARM processor are used for implementing the system. In the SDAS, each module is realized by Verilog HDL and C Language. We carry out the ModelSim simulation to verify the performances of important modules. The simulation results show that the FPGA-based data acquisition module can guarantee an accurate time-synchronization for the measured data from each axis sensor. Moreover, the FPGA-ARM based embedded technology in system hardware design can reduce the system cost by the integration of data logger, communication sever, and facility control system. To evaluate the data acquisition performance of the SDAS, we perform experiments for real seismic signals with the exciter. Performances comparison between the acquired data of the SDAS and the reference sensor shows that the data acquisition performance of the SDAS is valid.

Subband IPNLMS for blind adaptive MIMO filtering with sparse impulse response systems

In acoustic signal processing, the improved proportionate normalized least mean square (IPNLMS) is known as an effective adaptive algorithm for sparse impulse response systems. And it is known that the subband adaptive filter provides fast convergence rate, because of its data prewhitening characteristic. In this paper, we propose a subband blind adaptive algorithm for multi-inputs multi-outputs (MIMO) systems. The proposed algorithm (subband IPNLMS) combines subband filtering technique with IPNLMS algorithm. In this approach, subband adaptive filtering is employed to overcome the problems in long adaptive filters such as computational complexity and slow convergence rate. Simulation results show that the subband IPNLMS performs better than the subband NLMS, when the blind channel impulse response is sparse.

Variable block LMS adaptive filters with Gaussian input

In this paper, we present a new approach for block LMS algorithm using variable block length, which provides improved convergence speed while maintaining the misadjustment of conventional LMS algorithm. The block length increases or decreases as the averaged square error during the block length increases or decreases. And we derive the optimum initial step size under the white Gaussian input signal environment. This optimum step size is used to prove the performance of the proposed algorithm. Simulation results which comparing the proposed method to the block LMS algorithm and the normalized LMS algorithm indicate its better performance on convergence rate and misadjustment.

Seismic Noise Reduction Using Micro-Site Array Stacking

Abstract - This paper presents a new approach to improve the signal to noise ratio (SNR) for local seismic disaster preventing system in densely populated area. The seismic data measured in the local site includes various sensing noises (offset or measurement noise) and man-made/natural noises (road and rail traffic noise, rotating or hammering machinery noise, human activity noise such as walking and running, wind/atmospheric pressure-generated noise, etc.). These additive noises are different in time and frequency characters. The proposed method uses 3-stages processing to reduce these different additive noises. In the first stage, misalignment offset noise are diminished by time average processing, and then the second and third stages, coherent/incoherent noises such as man-made/natural noises are suppressed by array stacking. In addition, we derived the theoretical equation of the SNR gain improved by the proposed method. To evaluate the performance of the proposed method, computer simulations were performed with real seismic data and test equipment generated data as the input.Key Words : Earthquake event, Seismic noise, Signal to noise ratio, Array processing, Stacking†Corresponding Author : Dept. of Electronic Engineering, Dong-Eui University, KoreaE-mail : hchoi@deu.ac.kr* HVDC CP Accepted : December 20, 2013

Convex combination of subband adaptive filters for sparse impulse response systems

It is well known, combination scheme is suitable for improving the performance of adaptive algorithms. In this paper, we propose a subband combination scheme for sparse impulse response system. The combination is carried out in subband domain. In this convex combination, SIPNLMS and SNLMS are derived for fast convergence and small steady state error respectively. And mixing parameters are described by minimum mean square error and stochastic gradient algorithm. In adaptive system identification scenario, the advantages of this proposed method are illustrated.

Subband IPNLMS Adaptive Filter for Sparse Impulse Response Systems

In adaptive filtering, the sparseness of impulse response and input signal characteristics are very important factors of it`s performance. This paper presents a subband improved proportionate normalized least square (SIPNLMS) algorithm which combines IPNLMS for impulse response sparseness and subband filtering for prewhitening the input signal. As drawing and combining the advantage of conventional approaches, the proposed algorithm, for impulse responses exhibiting high sparseness, achieve improved convergence speed and tracking ability. Simulation results, using colored signal(AR(4)) and speech input signals, show improved performance compared to fullband structure of existing methods.

Subband adaptive filters with pre-whitening scheme for acoustic echo cancellation

The signal-decorrelating properties of the AP algorithm and the subband filtering improve the convergence speed of the conventional AP adaptive filter. In this paper, a new subband adaptive filtering with pre-whitening scheme for acoustic echo cancellation is presented. The proposed algorithm provides fast convergence and reduced computational complexity by combining merits of the affine projection (AP) algorithm and the subband filtering. The projection order of AP adaptive filter can be decreased by subband partitioning with the polyphase decomposition and the noble identity. The decreased projection order reduces the computational complexity in the proposed algorithm. Computer simulations illustrate the convergence rate improvements and the computational efficiency of the proposed algorithm and show the validity of the theoretical results.