276 research outputs found
Acoustic Echo Cancellation and their Application in ADF
In this paper, we present an overview of the principal, structure and the application of the echo cancellation and kind of application to improve the performance of the systems. Echo is a process in which a delayed and distorted version o the original sound or voice signal is reflected back to the source. For the acoustic echo canceller much and more study are required to make the good tracking speed fast and reduce the computational complexity. Due to the increasing the processing requirement, widespread implementation had to wait for advances in LSI, VLSI echo canceller appeared.
DOI: 10.17762/ijritcc2321-8169.150513
A study on adaptive filtering for noise and echo cancellation.
The objective of this thesis is to investigate the adaptive filtering technique on the application of noise and echo cancellation. As a relatively new area in Digital Signal Processing (DSP), adaptive filters have gained a lot of popularity in the past several decades due to the advantages that they can deal with time-varying digital system and they do not require a priori knowledge of the statistics of the information to be processed. Adaptive filters have been successfully applied in a great many areas such as communications, speech processing, image processing, and noise/echo cancellation. Since Bernard Widrow and his colleagues introduced adaptive filter in the 1960s, many researchers have been working on noise/echo cancellation by using adaptive filters with different algorithms. Among these algorithms, normalized least mean square (NLMS) provides an efficient and robust approach, in which the model parameters are obtained on the base of mean square error (MSE). The choice of a structure for the adaptive filters also plays an important role on the performance of the algorithm as a whole. For this purpose, two different filter structures: finite impulse response (FIR) filter and infinite impulse response (IIR) filter have been studied. The adaptive processes with two kinds of filter structures and the aforementioned algorithm have been implemented and simulated using Matlab.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .J53. Source: Masters Abstracts International, Volume: 44-01, page: 0472. Thesis (M.A.Sc.)--University of Windsor (Canada), 2005
study of adaptive signal processing
An adaptive filter is a digital filter that can adjust its coefficients to give the best match t An
adaptive filter is a digital filter that can adjust its coefficients to give the best match to a given
desired signal. When an adaptive filter operates in a changeable environment the filter
coefficients can adapt in response to changes in the applied input signals. Adaptive filters
depend on recursive algorithms to update their coefficients and train them to near the optimum
solution. An everyday example of adaptive filters is in the telephone system where, impedance
mismatches causing echoes of a signal are a significant source of annoyance to the users of the
system. The adaptive signal process is here to estimate and generate the echo path and
compensate for it. To do this the echo path is viewed as an unknown system with some impulse
response and the adaptive filter must mimic this response.
Adaptive Filters are generally implemented in the time domain which works well in most
scenarios however in many applications the impulse response become long, and increasing the
complexity of the filter beyond a level where it can no longer be implemented efficiently in the
time domain. An example of acoustic echo cancellation applications is in hands free telephony
system. However there exists an alternative solution and that is to implement the filters in the
frequency domain. The Discrete Fourier Transform or Fast Fourier Transform (FFT) allows the
conversion of signals from the time domain to the frequency domain in an efficient manner.
Despite the efficiency of the FFT the overhead involved in converting the signals to the
frequency domain does place a restriction on the use of the algorithm. When the impulse
response of the unknown system and hence the impulse response of the filter is long enough
however this is not an issue since the computational cost of the conversion is much less than that
of the time domain algorithm. The actual filtering of the signals requires little computational
cost in the frequency domain. Investigation of the so-called crossover point, the point where the
frequency domain implementation becomes more efficient than the time domain implementation
is important to establish the point where frequency domain implementation becomes practica
System Identification with Applications in Speech Enhancement
As the increasing popularity of integrating hands-free telephony on mobile portable devices
and the rapid development of voice over internet protocol, identification of acoustic
systems has become desirable for compensating distortions introduced to speech signals
during transmission, and hence enhancing the speech quality. The objective of this research
is to develop system identification algorithms for speech enhancement applications
including network echo cancellation and speech dereverberation.
A supervised adaptive algorithm for sparse system identification is developed for
network echo cancellation. Based on the framework of selective-tap updating scheme
on the normalized least mean squares algorithm, the MMax and sparse partial update
tap-selection strategies are exploited in the frequency domain to achieve fast convergence
performance with low computational complexity. Through demonstrating how
the sparseness of the network impulse response varies in the transformed domain, the
multidelay filtering structure is incorporated to reduce the algorithmic delay.
Blind identification of SIMO acoustic systems for speech dereverberation in the
presence of common zeros is then investigated. First, the problem of common zeros is
defined and extended to include the presence of near-common zeros. Two clustering algorithms
are developed to quantify the number of these zeros so as to facilitate the study
of their effect on blind system identification and speech dereverberation. To mitigate such
effect, two algorithms are developed where the two-stage algorithm based on channel
decomposition identifies common and non-common zeros sequentially; and the forced
spectral diversity approach combines spectral shaping filters and channel undermodelling
for deriving a modified system that leads to an improved dereverberation performance.
Additionally, a solution to the scale factor ambiguity problem in subband-based blind system identification is developed, which motivates further research on subbandbased
dereverberation techniques. Comprehensive simulations and discussions demonstrate
the effectiveness of the aforementioned algorithms. A discussion on possible directions
of prospective research on system identification techniques concludes this thesis
multi-band acoustic echo canceller
Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.Includes bibliographical references (leaves 68-69).by Mingxi Fan.S.B.and M.Eng
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