A pattern recognition system was developed that successfully recognizes simulated spectra of five different types of transportation noise sources. The system generates hyperplanes during a training stage to separate the classes and correctly classify unknown patterns in classification mode. A feature selector in the system reduces a large number of features to a smaller optimal set, maximizing performance and minimizing computation

Cabell, Randolph H.

Fuller, Chris R.

Obrien, Walter F.

English

NASA Technical Reports Server

- c
APPLICATION OF PATTERN RECOGNITION TECHNIQUES TO THE
IDENTIFICATION OF AEROSPACE ACOUSTIC SOURCES
Annual Report : Year one
Submitted to
Applied Acoustics Branch
NASA Langley Research Center
Hampton, VA 23665
from
Chris R. Fuller
Walter F. O'Brien
Randolph H. Cabell
VPI &
June 1988
i (NASA-CR-183116) APPLICATION OF P A T T E B N N88-27877
! RECOGNITION TECHNIQUES TO THE IDENTIFICATION
! OF AEROSPACE ACOUSTIC SOUBCES Annual Report
I No. 1 {Virginia Polytechnic Inst. ,and State Unclas
I Univ.) 11 p CSCL 20A G3/71 OV5U655
https://ntrs.nasa.gov/search.jsp?R=19880018493 2020-03-20T05:41:47+00:00Z
INTRODUCTION and THEORY
A pattern recognition system has been developed to recognize simulated aerospace
acoustic sources. This paper describes the theory and operation of the system and the
results obtained. A more complete description of pattern recognition techniques and their
applications can be found in ref 1.
Fig 1 shows a general pattern recognition system. Recognition of a pattern consists
of data acquisition, pre-processing, transformation of the pattern, then training or clas-
sifying, depending on the mode of operation. In the training mode the system learns to
discriminate between the different classes of patterns, while in the classification mode the
learned information is used to classify unknown patterns.
The pattern recognition system uses pattern descriptors or features to represent the
patterns and these features make up each pattern's feature vector. The feature vector's
dimensionality is equal to the number of features, so the greater the dimensionality the
more complete the description but more features add to the complexity and expense of
data acquisition and processing. A two dimensional example is shown in figure 2, where
the x and y axes represent values of feature 1 and feature 2, respectively. A pattern's
feature vector locates it in feature space and patterns of similar classes will cluster about
the same area if the features are well chosen.
An example of such a feature space is the space containing the heights and weights
of basketball player and jockeys. The x axis could be height values and the y axis weight
values, making the class of basketball players cluster toward the upper right due to their
higher weights and heights while the jockeys would cluster at the lower left.
Features useful in the recognition of acoustic emission signals can be extracted from
both the time and frequency domains. Examples of such features are the standard deviation
of the signal, the rise time for the biggest time domain pulse, partial power in various
frequency bands, and the number of peaks exceeding a preset threshold [2].
If the features are well chosen for a particular problem the different classes will separate
into clusters and a surface can be put between the classes. Figure 3 illustrates a two
DATA
ACQUISITION
! \
- 1PROCESSING
.
TRANSFORM
SIGNAL
Fig 1. General Classification System
CLASSIFY
SIGNAL
1
TRAINING
FEATURE 2
FEATURE 1
Fig 2. 2-D Feature Space
d(S)
Fig 3. Linear Decision Function
dimensional case where a line separates classes 1 and 2. The separating surface could
be more complicated than a simple line and in higher dimensional spaces a simple line
becomes a hyperplane.
The classification system we have developed uses a hyperplane as a decision function
given by the equation
d(x) = w'x '
where w, the weight vector, and x, the pattern vector, are n dimensional. There is one
such decision function for each class and a particular pattern is classified by evaluating
each decision function and assigning the pattern to the class of the highest valued decision
function.
The location of these decision function is not always intuitively obvious, especially in
higher dimensional spaces, so the surfaces must be constructed in a training mode. In the
training mode known patterns are presented to the classifier and the decision functions
evaluated and adjusted. The algorithm we used is based on a reward punishment concept
described below.
At the fcth iteration a pattern x(fc) is presented to the classifier, where x(fc) is a
member of class Wt. If
di\x(k)] > dj\x(k)]
for all y, j not equal to t, then the pattern has been classified correctly and no adjustment
is made. However, if for some /,
di [x(k) ]>di [x(k) \
then
Wi(/c + 1) =Wi(fc) +x(k)
w j ( f c + l ) = w i ( f c ) - x ( f c )
Wy(fc + l ) = W y ( f c )
for all j, j not equal to i or I
where w, is the weight vector for the tth class.
The steps above are repeated for each pattern until either all training patterns are
correctly classified or an acceptable error rate is reached.
The performance of the algorithm is dependant on the discriminatory power of the
features because poorly chosen features won't separate the classes thereby preventing the
algorithm from reaching a satisfactory solution. It would seem that increasing the number
of features would solve this problem but this increases computation time and reduces
performance on classification data. An optimal set of features is needed, but such a set is
difficult to specify a priori without extensive system specific knowledge. Our system has
a feature selection capability to solve this problem.
Feature selection means a user can input numerous features without worrying about
their discriminatory power and the system will search the list and select the set of features
that performs best. The selection is done independent of the user, who only needs to
specify the desired number of features in the final set. The interested reader is referred to
ref 4, which outlines the feature selection algorithm.
RESULTS
The system was tested on SPL data from five different sources. The data was derived
from SPL curves for a prop plane, jet aircraft, automobile, train, and helicopter, shown
in figure 4.The percent of energy in each of 8 octave bands makes up an 8 dimensional
feature vector for each pattern. The classifier was trained with three patterns from each
class with minor variations betwen patterns of the same class. One unknown from each
class was also fed to the classifier.
The system successfully separated the five classes and correctly classified the unknown
patterns.
The feature selector was used to reduce the features from 8 to 3 or 4 best, resulting
in faster convergence to a working classifier during the training stage. When compared
visually, the features from the chosen best set of features differentiated between classes
much better than those left out as poor features.
NOISE DA TA
110
100
R
t
I
j -
]
r
97/A
LocOMfiv* Ktdtr
wrtoui lead con-
dlHvn «t 50 fwt
SPL CURVE FOR TRAIN
SPL CURVE FOR HEUCOPTER
Fig 4. SPL curves
NO1SEDATA
110
1
70
OKIGINAE PAGE IS
O?. POOR QUALITY
I I
Gwund NeiM ol 1000 FM»
00 •
IT
- Typical Operation
~ ~ '10600 t
SPL CURVE FOR JET AIRCRAFT
Intak*
Ceolb^fon
Autonobll* Nob. Spectra «t 50 •»•»
i i i
SPL CtUVE FOR AUTOMOBILE
Fig 4. SPL curves
NOISE DA TA OKJGSS7AI; IS
SPL CURVE FOR PROP AIRCRAFT
Fig 4. SPL curves
CONCLUSIONS
A pattern recognition system has been developed that successfully recognizes simu-
lated spectra of five different types of transportation noise sources. The system generates
hyperplanes during a training stage to separate the classes and correctly classifies unknown
patterns in classification mode. A feature selector in the system reduces a large number of
features to a smaller optimal set, maximizing performance and minimizing computation.
Future work will involve testing the system on more realistic signals and studying the
effect of perturbations of the signal on the performance of the classifier. Realistic signals
will complicate the preprocessing and feature extraction but will yield better results due
to increased information content. The overall performance of this initial classifier and the
potential for information in realistic signals leads us to believe there is a potential for such
a system to identify aerospace acoustic sources.
REFERENCES
1. J.T. Tou and R.C. Gonzalez, Pattern Recognition Principles, Addison-Wesley,
Reading,1974.
2. R.W.Y. Chan and R. Robert Hay, "A Case Study of Sensitivity of Pattern Classifiers
Used in Sorting Acoustic Emission Signals," Review of Progress in Quantitative
Nondestructive Evaluation, v.l, Plenum, New York, 1982, pp.413-420.
3. Transportation Noise and Noise from Equipment Powered by Internal Combustion
Engines, Wyle Laboratories, National Technical Information Service, Springfield, 1971.
4. Gregory S. Klassen,A General Purpose Reduction Intensive Feature Selector
for Pattern Classification, VPI fe SU Thesis, 1986.


Application of pattern recognition techniques to the identification of aerospace acoustic sources

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