1,002 research outputs found
Multichannel receivers from ofdm and tdma in mobile communications
This paper addresses the use of multichannel receivers for blind equalization in TDMA under frequency selective channels and OFDM systems in frequency flat fading channels. A new criteria is proposed for blind equalization of finite length mobile channels.Peer ReviewedPostprint (published version
Fractional biorthogonal partners in channel equalization and signal interpolation
The concept of biorthogonal partners has been introduced recently by the authors. The work presented here is an extension of some of these results to the case where the upsampling and downsampling ratios are not integers but rational numbers, hence, the name fractional biorthogonal partners. The conditions for the existence of stable and of finite impulse response (FIR) fractional biorthogonal partners are derived. It is also shown that the FIR solutions (when they exist) are not unique. This property is further explored in one of the applications of fractional biorthogonal partners, namely, the fractionally spaced equalization in digital communications. The goal is to construct zero-forcing equalizers (ZFEs) that also combat the channel noise. The performance of these equalizers is assessed through computer simulations. Another application considered is the all-FIR interpolation technique with the minimum amount of oversampling required in the input signal. We also consider the extension of the least squares approximation problem to the setting of fractional biorthogonal partners
Blind MultiChannel Identification and Equalization for Dereverberation and Noise Reduction based on Convolutive Transfer Function
This paper addresses the problems of blind channel identification and
multichannel equalization for speech dereverberation and noise reduction. The
time-domain cross-relation method is not suitable for blind room impulse
response identification, due to the near-common zeros of the long impulse
responses. We extend the cross-relation method to the short-time Fourier
transform (STFT) domain, in which the time-domain impulse responses are
approximately represented by the convolutive transfer functions (CTFs) with
much less coefficients. The CTFs suffer from the common zeros caused by the
oversampled STFT. We propose to identify CTFs based on the STFT with the
oversampled signals and the critical sampled CTFs, which is a good compromise
between the frequency aliasing of the signals and the common zeros problem of
CTFs. In addition, a normalization of the CTFs is proposed to remove the gain
ambiguity across sub-bands. In the STFT domain, the identified CTFs is used for
multichannel equalization, in which the sparsity of speech signals is
exploited. We propose to perform inverse filtering by minimizing the
-norm of the source signal with the relaxed -norm fitting error
between the micophone signals and the convolution of the estimated source
signal and the CTFs used as a constraint. This method is advantageous in that
the noise can be reduced by relaxing the -norm to a tolerance
corresponding to the noise power, and the tolerance can be automatically set.
The experiments confirm the efficiency of the proposed method even under
conditions with high reverberation levels and intense noise.Comment: 13 pages, 5 figures, 5 table
Robust equalization of multichannel acoustic systems
In most real-world acoustical scenarios, speech signals captured by distant microphones from a source are reverberated due to multipath propagation, and the reverberation may impair speech intelligibility. Speech dereverberation can be achieved
by equalizing the channels from the source to microphones. Equalization systems can
be computed using estimates of multichannel acoustic impulse responses. However,
the estimates obtained from system identification always include errors; the fact that
an equalization system is able to equalize the estimated multichannel acoustic system does not mean that it is able to equalize the true system. The objective of this
thesis is to propose and investigate robust equalization methods for multichannel
acoustic systems in the presence of system identification errors.
Equalization systems can be computed using the multiple-input/output inverse theorem or multichannel least-squares method. However, equalization systems
obtained from these methods are very sensitive to system identification errors. A
study of the multichannel least-squares method with respect to two classes of characteristic channel zeros is conducted. Accordingly, a relaxed multichannel least-
squares method is proposed. Channel shortening in connection with the multiple-
input/output inverse theorem and the relaxed multichannel least-squares method is
discussed.
Two algorithms taking into account the system identification errors are developed. Firstly, an optimally-stopped weighted conjugate gradient algorithm is
proposed. A conjugate gradient iterative method is employed to compute the equalization system. The iteration process is stopped optimally with respect to system identification errors. Secondly, a system-identification-error-robust equalization
method exploring the use of error models is presented, which incorporates system
identification error models in the weighted multichannel least-squares formulation
Convolutive Blind Source Separation Methods
In this chapter, we provide an overview of existing algorithms for blind source separation of convolutive audio mixtures. We provide a taxonomy, wherein many of the existing algorithms can be organized, and we present published results from those algorithms that have been applied to real-world audio separation tasks
Sparseness-controlled adaptive algorithms for supervised and unsupervised system identification
In single-channel hands-free telephony, the acoustic coupling between the loudspeaker and
the microphone can be strong and this generates echoes that can degrade user experience.
Therefore, effective acoustic echo cancellation (AEC) is necessary to maintain a stable
system and hence improve the perceived voice quality of a call. Traditionally, adaptive
filters have been deployed in acoustic echo cancellers to estimate the acoustic impulse
responses (AIRs) using adaptive algorithms. The performances of a range of well-known
algorithms are studied in the context of both AEC and network echo cancellation (NEC).
It presents insights into their tracking performances under both time-invariant and time-varying
system conditions.
In the context of AEC, the level of sparseness in AIRs can vary greatly in a mobile
environment. When the response is strongly sparse, convergence of conventional
approaches is poor. Drawing on techniques originally developed for NEC, a class of time-domain
and a frequency-domain AEC algorithms are proposed that can not only work
well in both sparse and dispersive circumstances, but also adapt dynamically to the level
of sparseness using a new sparseness-controlled approach.
As it will be shown later that the early part of the acoustic echo path is sparse
while the late reverberant part of the acoustic path is dispersive, a novel approach to
an adaptive filter structure that consists of two time-domain partition blocks is proposed
such that different adaptive algorithms can be used for each part. By properly controlling
the mixing parameter for the partitioned blocks separately, where the block lengths are
controlled adaptively, the proposed partitioned block algorithm works well in both sparse
and dispersive time-varying circumstances.
A new insight into an analysis on the tracking performance of improved proportionate
NLMS (IPNLMS) is presented by deriving the expression for the mean-square error.
By employing the framework for both sparse and dispersive time-varying echo paths, this
work validates the analytic results in practical simulations for AEC.
The time-domain second-order statistic based blind SIMO identification algorithms,
which exploit the cross relation method, are investigated and then a technique with proportionate
step-size control for both sparse and dispersive system identification is also
developed
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