4 edition of **Adaptive microphone arrays using FIR and IIR filters** found in the catalog.

Adaptive microphone arrays using FIR and IIR filters

Khoman S. Phang

- 391 Want to read
- 32 Currently reading

Published
**1993**
by National Library of Canada = Bibliothèque nationale du Canada in Ottawa
.

Written in English

**Edition Notes**

Series | Canadian theses = Thèses canadiennes |

The Physical Object | |
---|---|

Format | Microform |

Pagination | 2 microfiches : negative. |

ID Numbers | |

Open Library | OL14778045M |

ISBN 10 | 0315784709 |

OCLC/WorldCa | 30356942 |

The intake system in an automotive engine has a short duct compared with that of the exhaust system. The filtered-x LMS (FX-LMS) algorithm has been applied to the active noise control (ANC) system in a short acoustic duct. This algorithm design is based on the FIR (finite impulse response) filter; however, it has a slow convergence issue due to a large number of zero coefficients. Cited by: The digital filters are offset free and offeran answer of greater precision. Also the adaptive filters can be a combination of different types of filters, like single-input or multi-input filters, linear or nonlinear, and finite impulse response FIR or infinite impulse response IIR by: 9.

implemented using fractional arithmetic. Unlike IIR filters, it is always possible to implement a FIR filter using coefficients with magnitude of less th an (The overall gain of the FIR filter can be adjusted at its output, if desired.) This is an imp ortant consideration when using fixed-point DSP's. Adaptive filtering techniques must be implemented to promote accurate solutions and a timely convergence to that solution. Adaptive Filtering System Configurations There are four major types of adaptive filtering configurations; adaptive system identification, adaptive noise cancellation, adaptive linear prediction, and adaptive inverse Size: KB.

Most time-domain algorithms, such as FIR and IIR filters, fall into this category. The alternative is frame-by-frame processing, which is required for frequency-domain techniques. In the frame-by-frame method, a group of samples is read from the input, calculations . Some common applications of adaptive filters (Chapter 1). Adaptive FIR filters: Some algorithms and their limitations (Chapter 2). Adaptive IIR filters: Some motivation from system identification theory (Chapter 3). Some useful tools: Concepts on approximation and stability theory (Chapter 4).

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While using FIR usually consists of long filter lengths, infinite impulse response (IIR) filters of lower orders have also been proposed to be effective in active noise control (ANC) signal. Adaptive IIR filters present some advantages as compared with the adaptive FIR filters, including reduced computational complexity.

If both have the same number of coefficients, the frequency response of the IIR filter can approximate much better a desired by: 5. The choice of filter structure to adapt, algorithm design and the approximation properties for each type of algorithm are also addressed.

This work recasts the theory of adaptive IIR filters by concentrating on recursive lattice filters, freeing systems from the need for direct-form filters.;A solutions manual is available for instructors only.5/5(1). Adaptive Filtering 2 • For a number of applications, adaptive IIR filters may have a compuatational and modelling advantage.

Consider the inverse sys id: • Using adaptive FIR filter, the inverse has many weights: • Using adaptive IIR filter, the inverse may have only two weights: s(k) 1 y(k) Σ +-Adaptive e(k) Filter G(z) d(k) x(k) Hz()= + z–1 Adaptive Algorithm.

tion, noise cancellation are some of the important applications of adaptive filters. Adaptive linear filters can be further classified in terms of length of the impulse response as finite-length impulse response (FIR) filters and infinite-length impulse re sponse (IIR) filters.

The main feature that distinguishes an IIR filter from an FIR filter. Integrates rational approximation with adaptive filtering, providing viable, numerically reliable procedures for creating adaptive infinite impulse response (IIR) filters. The choice of filter structure to adapt, algorithm design and the approximation properties for each type of algorithm are also addressed.

Douglas, S.C. “Introduction to Adaptive Filters” (FIR) or inﬁnite-impulse-response (IIR) ﬁlter. Figure shows the structure of a direct-form FIR ﬁlter, also known as a tapped-delay-line or transversal ﬁlter, where z−1 denotes the unit delay element and each wi.n/File Size: KB.

An adaptive filter is a system with a linear filter that has a transfer function controlled by variable parameters and a means to adjust those parameters according to an optimization e of the complexity of the optimization algorithms, almost all adaptive filters are digital ve filters are required for some applications because some parameters of the desired.

Therefore, generally IIR filters are more efficient in memory and computational requirements than FIR filters. A drawback of IIR filters is that they have non-linear phase responses. Finite impulse response (FIR) filters FIR filters are also known as convolution filters, or moving-average filters because the output values of an FIR filter are.

Comparison of convergence characteristics of adaptive IIR and FIR filters for active noise control in a duct Article (PDF Available) in Applied Acoustics 68(7) July with Reads. Adaptive Control Using IIR Lattice Filters Stephen J. Hevey (ABSTRACT) This work is a study of a hybrid adaptive controller that blends fixed feedback control and adaptive feedback control techniques.

This type of adaptive controller removes the requirement that information about the disturbance is. In [3], an estimation-based approach to the design of adap- tive FIR filters is proposed.

Reference [3] uses an adaptive ANC scenario to explain how an estimation interpretation of the adaptive control problem provides a framework for the systematic synthesis and analysis of adaptive FIR fil- ters.

adaptive IIR filters where both poles and zeros are adjustable. Of the various FIR filter structures available, the direct form (transversal), the symmetric transversal form, and the lattice form are the ones often employed in adaptive filtering applications.

Applications Of Adaptive FiltersFile Size: KB. 7 Adaptive Filters • Adaptive structures • The least mean squares (LMS) algorithm • Programming examples for noise cancellation and system identiﬁcation using C code Adaptive ﬁlters are best used in cases where signal conditions or system parameters are slowly changing and the ﬁlter is to be adjusted to compensate for this change.

Frequency Domain FIR and IIR Adaptive Filters D. Lynn Department of Electrical Engineering University of Idaho Moscow, Idaho Abstract A discussion of the LMS adaptive filter relating to its convergence characteristics and the problems associated with disparate eigenvalues is presented.

This is used to introduce the concept of proportional File Size: 1MB. Echo cancellation solutions are most often based on a linear FIR adaptive filtering approach. In fact all real-life echo paths are IIR filters and their approximate representation by FIR filters is dictated by conceptual simplicity (as only B-type filter coefficients have to be taken into account, as opposed to B and A-type coefficients needed for IIR filters) and FIR filter stability property.

Adaptive IIR filters offer a simple method for in-situ tracking of loudspeaker parameters. The convergence properties of standard adaptive IIR algorithms can, however, be sub-optimal, when applied to loudspeaker system identification using electrical impedance measurement.

The normalized-LMS IIR algorithm described by Widrow and Stearns () [ ] either converges slowly or creates excess.

The purpose of this thesis is to study the adaptive filters theory for the noise cancellation problem. Firstly the paper presents the theory behind the adaptive filters. Secondly it describes three most commonly adaptive filters which were also used in computer experiments, the LMS, NLMS and RLS algorithms.

Common Applications System Identification –– Using an Adaptive Filter to Identify an Unknown System. One common adaptive filter application is to use adaptive filters to identify an unknown system, such as the response of an unknown communications channel or the frequency response of an auditorium, to pick fairly divergent applications.

Filter Design Techniques explains how to design analog, digital, and matched filters. It is intended for practicing engineers and scientists who have a background in Fourier, Laplace, and z transforms. Part 1 is concerned with analog Butterworth and Chebyshev Author: Dwight F. Mix PhD.

The design of FIR filters using windowing is a simple and quick technique. There are many pages on the web that describe the process, but many fall short on providing real implementation details. Hopefully, this page contains all the required information to put together your own algorithm for creating low pass, high pass, band pass and band.In this project, the adaptive notch filter for single and Multiple narrow-band interference is implemented using simplified LMS algorithm.

Performances of the LMS adaptive algorithms is evaluated and analysed through simulation on the computer using MATLAB. The algorithm are then written in C. For the Love of Physics - Walter Lewin - - Duration: Lectures by Walter Lewin.

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