lecture_01.ppt

ECE 8443 – Pattern Recognition
ECE 8423 – Adaptive Signal Processing
• Objectives:
Definitions
Random Signal Analysis (Review)
Discrete Random Signals
Random Signal Models
Power Spectrum and Moments
• Resources:
WIKI: Adaptive Systems
BW: Adaptive Filtering
ISIP: Pattern Recognition
RWS: Java Applet
• URL: .../publications/courses/ece_8423/lectures/current/lecture_01.ppt
• MP3: .../publications/courses/ece_8423/lectures/current/lecture_01.mp3
LECTURE 01: RANDOM SIGNAL ANALYSIS

ECE 8423: Lecture 01, Slide 1
• Optimal Signal Processing: the design, analysis, and implementation of
processing systems that extract information from sampled data in a manner
that is “best” or optimal in some sense.
• In a statistical sense, this means we maximize some criterion function,
normally a posterior probability, by adjusting the parameters of the model(s).
• Popular methods include Minimum Mean-Squared Error (MMSE), Maximum
Likelihood (ML), Maximum A Posteriori (MAP) and Bayesian methods.
• Adaptive Signal Processing: Adjusting the parameters of a model as new data
is encountered to maximize a criterion function. This is often done in
conjunction with a pattern recognition system. Key constraints are:
 Supervised vs. Unsupervised: Is the new data truth-marked so that you
know the answer that your system should produce for this new data?
 Online or Off-line: Do you adapt as the new data arrives, or do you
accumulate a set of new data, and adapt based on this data and all
previously encountered data?
 Fast vs. Slow: Does the adaptation occur over seconds or hours or days?
How much can you rely on the new data?
As you can see, there is significant overlap between this course and our
Pattern Recognition course.
Introduction

Adaptive Signal Processing Examples
• Exploiting Information: Adaptive
systems can exploit all forms of
information at all levels of an ontology
or hierarchy that describes the
problem space.
• Systems can adapt to low-level
changes such as channel or noise, or
higher-level changes in language,
user, or application.
• Speech Recognition: Most
statistical modeling systems
suffer from over training on known
training data.
• Adaptation is employed to allow
systems to deliver high
performance in previously unseen
environments or for new speakers
or microphones.

Discrete Random Signals
• Discrete random signal:
• Probability density function:
• Cumulative density function:
• Deterministic signal: a signal that is repeatable (e.g., a sinewave).
• Random signal: the signal can only be characterized through statistics. Often
we model signals as the sum of a deterministic component and a random
component (e.g., noise).
• Measures of random signals:
 Mean value:
 The expectation can also be computed
as an ensemble average:
 The expectation operator is linear:
 The expectation of a function of a
random variable is given by:
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Correlation, Covariance and Linear Systems
• The correlation of a random signal, x(n), is defined by:
• This can also be computed via an ensemble average:
• The autocorrelation is defined as:
• The covariance of a random signal is defined as:
• The variance is defined as:
• We can write the covariance and autocorrelation
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• We can generalize the covariance and autocorrelation
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• Samples x(m) and x(n) are said to
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• Samples x(m) and x(n) are said to be orthogonal if:
• A discrete random variable is said to be strictly stationary or strongly
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• White noise is a zero-mean, stationary
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• First order Markov signals:
• Gaussian Random Signals:
• Complex Random Signals:
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• We can obtain the power spectrum using :
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• For two random variables, we can define the
cross-correlation and the cross-spectrum:
• We can show:
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• Consider a zero-mean, stationary sequence, x(n), as the input to a linear shift-
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• Note that the expected value of the output is zero:
• We can use the z-transform to write an expression for the output:
• We can show that the output power spectrum is related to the input power
spectrum by the important relation:
• We can also express this in the time domain:
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• Wold’s Theorem: An stationary sequence, x(n), which has no deterministic
components can be represented as the output of a stable, causal, shift-
invariant linear filter, h(n), with a white noise input, w(n):
• The z-transform of the linear time-invariant system can be represented as a
rational of polynomials:
• Specials cases are autoregressive (AR) models (all poles), moving average
(MA) models (all zeroes), and autoregressive moving average (ARMA) models
(poles and zeros).
• The discrete-time output can be written as a linear, constant coefficient
difference equation:
• Stability is determined by the location of poles with respect to the unit circle.
• Minimum phase is determined by the location of the zeroes in the z-plane.
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Estimation of Moments
• In Pattern Recognition, we deal with the estimation of parameters in a
rigorous manner and derive properties of well-known estimators such as
means and variances.
• The error in an estimation procedure can be written as:
where  represents the true value of the parameter (often unknown).
If , the estimator is said to be unbiased.
• If , the estimator is said to be asymptotically unbiased.
• The efficiency of two estimators can be defined in terms of the variance:
• A measure which embraces both the bias and
the variance is the mean-squared error:
• An estimator is said to be consistent if:
• If contains all the information about , is said to be a sufficient
statistic. See Pattern Recognition for more information on this.
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• An unbiased estimator of the autocorrelation is:
because . An alternative estimator that is asymptotically
unbiased is:
• The latter is more useful for large values of m and small values of M.
• Later in this course we will explore many powerful ways to estimate
parameters of a model with limited amounts of data. In general, adaptive
signal processing involves trading the complexity of your model (e.g., the
number of parameters) and the amount of data needed for adaptation.
Estimation Using Time Averages
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• Adaptive signal processing involves adjusting the parameters of a model to
better fit the current operating environment using a small number of data
samples from the new environment.
• We will employ well-known techniques from digital signal processing and
statistics to develop parameter estimation techniques and derive properties
of these estimators.
• In this lecture, we quickly reviewed key topics from random signals and
systems and digital signal processing.
• We discussed how to analyze and model linear time-invariant discrete-time
systems.
• We also discussed the properties of some basic methods of estimating
moments (e.g., ensemble averages).
Summary

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