Implementation Of Digital Filter Bank Research Articles

Improvement of LMS adaptive noise canceller using uniform Poly-phase digital filter bank

<span>This paper presents the Least Mean Square (LMS) noise canceller using uniform poly-phase digital filter bank to improve the noise can-cellation process. Analysis filter bank is used to decompose the full-band distorted input signal into sub-band signals. Decomposition the full-band input distorted signal into sub-band signals based on the fact that the signal to noise ratio (S/N) is inversely proportional to the signal bandwidth. Each sub-band signal is fed to individual LMS algorithm to produce the optimal sub-band output. Synthesis filter bank is used to compose the optimal sub-band outputs to produce the final optimal full-band output. In this paper, m-band uniform Discrete Fourier Transform (DFT) digital filter bank has been used because its computational complexity is much smaller than the direct implementation of digital filter bank. The simulation results show that the proposed method provides the efficient performance with less and smooth error signal as compared to conventional LMS noise canceller.</span>

Signal classification and software–hardware implementation of digital filter banks based on field-programmable gate arrays and compute unified device architecture

The paper is devoted to handling wideband monitoring tasks by discrete Fourier transform (DFT) modulated filter banks. Filter bank implementation is considered using CPU (Central Processing Unit) and CUDA (Compute Unified Device Architecture) based on GPUs (Graphics Processing Units). We show that CUDA is more efficient for big signal sets due to low temporal and computational costs. The paper also discusses signal classification in filter bank channels for different signal-to-noise ratios using binary decision trees (with the iterative Adaboost procedure) and neural networks. The total classification error in our experiments does not exceed 10%. The results can be extended and applied to hydroacoustic monitoring tasks.

Design and Implementation of Digital Filter Bank to Reduce Noise and Reconstruct the Input Signals

Design and Implementation of Digital Filter Bank to Reduce Noise and Reconstruct the Input Signals

Structures for anticausal inverses and application in multirate filter banks

Anticausal or time-reversed inversion of digital filters has gained importance in the implementation of digital filter banks. Anticausal inversion has, in the past, been shown to be possible by using block processing with appropriate state initialization. With (A, B, C, D) denoting the state-space description of a structure implementing a filter G(z), the anticausal inverse can be essentially regarded as a filter structure having an inverted state-space description, which we denote as (A/spl circ/, B/spl circ/, C/spl circ/, D/spl circ/). It is usually not efficient to implement the state-space equations given by (A/spl circ/, B/spl circ/, C/spl circ/, D/spl circ/) directly because of excessive multiplier count. Rather, one seeks to find an efficient structure having the inverse description (A/spl circ/, B/spl circ/, C/spl circ/, D/spl circ/). While this can be done by inspection in simple cases such as the direct-form structure, systematic procedures for other important structures have yet to be developed. We derive anticausal inverse structures corresponding to several standard IIR filter structures such as the direct-form, cascade-form, coupled-form, and the entire family of IIR lattice structures including the tapped cascaded lattice. We introduce the notion of a causal dual, which we find convenient in the derivations. We show that the limit-cycle free property of the original structure is inherited by the causal dual in some but not all cases.

Polynomial transform implementation of digital filter banks

This paper investigates the use of polynomial transforms for the implementation of uniform digital bandpass filter banks. The technique is based upon a decomposition of the N bandpass filters into a set of real polyphase filters followed by a DCT (discrete cosine transform) of size N. The DCT is converted into a DFT (discrete Fourier transform) of size N and the polyphase filters are evaluated by DFT's. This procedure yields a two-dimensional DFT which is computed by a polynomial transform and odd DFT's. We show that this technique reduces significantly the number of arithmetic operations when compared to conventional methods, and yields a regular structure in which most of the computations are performed with FFT-type algorithms.