American Institute of Aeronautics and Astronautics

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Experimental Training and Validation of a System for

Aircraft Acoustic Signature Identification

V. Quaranta* and I. Dimino

†

CIRA, the Italian Aerospace Research Center, Capua (CE), Italy, 81043

This paper deals with the experimental validation of an innovative system for the aircraft

acoustic signature identification which has been developed by the VIAC (Vibration &

Acoustics) Laboratory of CIRA (The Italian Aerospace Research Centre). The system is

composed by an algorithm for the acoustic signature identification and a dedicated neural

network classifier, trained with a set of experimental aircraft noise data. The algorithm test

and validation has been performed for different airplanes during take-off and landing

manoeuvres. The experimental activity of ground noise measurements has been carried out

@ Naples airport of Capodichino. More than 200 aircraft noise events of five aircraft types

(Airbus A320, Boeing B737, Mc Donnel Douglas MD80, Fokker F100, Aerospatiale/Alenia

ATR72), during both take off and landing manoeuvre, have been measured. The paper

demonstrates the feasibility of developing a suitable ANN to establish if a time signal,

elaborated through a wavelet process, is or isn't similar to other, having been recognized as

originated by a defined type of aircraft. The ANN was trained by the use of a subset of

experimental data and then validated through the comparison with another subset of data,

anyhow originated by the same experimental campaign. Developed SW demonstrated to give

more than satisfactory results for each of the acquired spectra, being the max error always

being under (10)%.

Nomenclature

STFT = Short Time Fourier Transform

GMM = Gaussian mixture models

HMM = hidden Markov models

FIR = Finite Impulsive Response

ANN = Artificial Neural Network

WMRA = Wavelet Multi-Resolution Analysis

* Test Engineer, Vibration & Acoustics Laboratory, CIRA, Via Maiorise 81043 Capua (CE), ITALY,

v.quaranta@cira.it. † Test Engineer, Vibration & Acoustics Laboratory, CIRA, Via Maiorise 81043 Capua (CE), ITALY,

i.dimino@cira.it.

12th AIAA/CEAS Aeroacoustics Conference (27th AIAA Aeroacoustics Conference)8 - 10 May 2006, Cambridge, Massachusetts

AIAA 2006-2696

Copyright © 2006 by CIRA, the Italian Aerospace Research Centre. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.

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I. Introduction

he acoustic signature identification techniques are commonly used for the automatic speaker recognition, i.e. the

labelling of an unknown voice as one of a set of known voices. Furthermore, great interest is growing on the

monitoring and prediction of ship signatures in order to minimize a ship’s susceptibility to being detected, tracked,

identified or targeted. The acoustic signature of a naval vessel is, in fact, one of its greatest vulnerabilities.

For aeronautical applications, this branch of research possesses interesting potentiality. Identification of noise

source and the association between the cause and noise admissible levels is fundamental for airport authorities. It

can be applied as support for the radar monitoring of the airports and for the verification of compliance with

limitations of annoyance in the area around them. It can allow the surveillance of isolated or dangerous areas and the

verification of compliance with peace agreements (“no-fly zone”). The acoustic identification of military airplanes

with small radar detection (stealth) and the acoustic surveillance of strategic objectives can play a crucial role in the

success of military operations.

The aim of the aircraft acoustic signature identification is to recognize the type and manoeuvre of an aircraft,

during different flight conditions, by processing only the emitted acoustic signal. The central task of the signal

classification process is the feature extraction: the assignment of noise time histories to classes which have strictly

defined properties.

The most common acoustic signature recognition techniques derive from the speech recognition methods,

founded on time-varying spectral signal representations in terms of time and frequency coordinates. Typical time-

frequency distributions such as Spectograms, Wavelet transforms, Multispectra, Wigner distributions, are used not

only for the speech recognition, but also for mechanical failure detection, the analysis of seismic data processing and

biomedical engineering.

Several systems have been proposed to recognize and classify different noise sources. A common focus of these

systems is represented by the capability of spectral details investigation from the acoustic signatures.

The Fourier Transform, widely used for the signal analysis and processing, is not suitable to analyze time-variant

spectra of non-stationary and fast transient signals. It does not provide any information regarding the time evolution

of the spectral characteristics of the signal. An intuitive way to analyze a non-stationary signal is to divide it into a

sequence of time segments where the signal can be reasonably treated as stationary. After that, it is reasonable to

perform a Fourier Transform to each of the local segments of the signal (Short-Time Fourier Transform).

Unfortunately, this approach can be critically depending on the choice of the window function. Once that it is

chosen, the width along time and frequency axes is fixed in the entire time-frequency plane. This causes constant

time and frequency resolutions.

The wavelet transform is particularly indicated for the analysis of non-stationary and fast transient signal. Unlike

the STFT analysis, the window function is a compressed or dilated version of the same function (mother wavelet)

when it is shifted through the signal. It allows a time-frequency representation of the signal, contributing to

overcome the resolution problems (T=1/df) of the STFT. In a multi-resolution wavelet analysis, the signal is

decomposed with different resolutions corresponding to different scale factors (levels) of the wavelets. This

procedure can be employed in order to represent the original acoustic noise signal with a series of detail and

approximation functions. The resolution, which is a measure of the detailed information, is changed by an iterative

filtering process, and a variable scale allows the up-sampling or the sub-sampling of the signal.

Wu, Siegel and Khosla [1] proposed the Short-Time Fourier Transform of sampled noise signals and Principal

Component Analysis for the feature vectors extraction. Classification has then been performed by projecting these

vectors into the principal component subspace and verifying the distance from the locus of the different training

sample set.

Liu [8] described a recognition system based on a biological hearing model to extract multi-resolution feature

vectors: cochlear filter and A1-cortical wavelet transform. Different classification systems, like learning vector

quantization (LVQ), tree-structured vector quantization (TSVQ) and parallel TSVQ (PTSVQ) have been

implemented and compared in terms of classification error and search and training time.

Sampan [7] presented a microphone array technique to detect the presence and classify the type of the sound

source. The feature vectors included the energy features extracted in a specific frequency band. A multi-layer

perceptron and an adaptive fuzzy logic system were used for the successive classification.

Undoubtedly, the classifier accuracy depends on the feature extraction process. The state-of-the-art of the speech

recognition techniques, like Mel-frequency cepstral coefficients, and statistical classifier as Gaussian mixture

T

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models (GMM) or hidden Markov models (HMM) [4], seems to be inappropriate for aeronautical applications

because of the poor accuracy of the spectral information.

The feature vectors obtained from the log-magnitude of the short term Fourier transform (STFT) of acoustic

signals provide, instead, good practical results. They are often followed by a Principal Component analysis or by a

third-octave bins data partition, in order to compress the data into feature vectors of reasonable size [4].

As a time-frequency approach, Wavelet transform for extracting distinctive information from the acoustic

signatures has been investigated. It is the most promising method, used by Choe et al. [5], Maciejewski et al., Dress

and Kercel [6,9], that provides time-frequency multi-resolution analysis useful for a compact signal representations.

II. Aircraft Acoustic Signature identification

The selected method for the aircraft acoustic signature identification employs a wavelet multi-resolution analysis

of noise signals and a statistical analysis of the noise events for each aircraft class [3]. This investigation plays a

crucial role to learn the system classifier, a dedicated neural network, with features parameters of reasonable size

and condense, at the same time, all the peculiar characteristics of each aircraft noise.

System procedure can be summarized into two different phases:

1) Training phase;

2) Aircraft identification phase.

The training phase consists on the definition of the acoustic signal properties and their collection into

representative feature vectors of each aircraft acoustic signature. It contributes to define an aircraft feature database

on the basis of which the system classifier is designed. In the second phase, the identification phase, any new

aircraft noise signal is processed by means of a neural network classifier that gives, as output, an estimation of the

grade of similarity with the included classes.

In Figure 1 the training phase is described. Each

different airplane noise time history is time-

frequency analyzed on the basis of a wavelet-based

decomposition. This process aims at investigating

the frequency content of the original noise signals

and offers, at the same time, optimum Time-

Frequency resolution, only limited by the

Heisenberg’s Uncertainty Principle [2]. The second

step, the Feature Extraction process, is based on a

statistical analysis of the wavelet coefficients and on

the evaluation of the energetic content of each

wavelet decomposition level. This approach intends

to include, in a statistical point of view, the great

variability of the measured data that could affect the

signature evaluation of each aircraft class. The

wavelet transform coefficients for the FIR filter

bank are taken from Daubechies. Twelve-level

wavelet transform is computed for each of the

events. This phase is the basis for a correct aircraft

noise classification and means to carry out an

optimum estimation of the acoustic information.

The classification of each aircraft noise signal

uses the feature vectors obtained from the previous

analysis to design a dedicated neural network. This

process is a function fitting process between the

feature vectors and the output vector corresponding

to specific aircraft types. It is based on the classes

definition and allows the identification of the degree

of similarity between any unknown acoustic signal

and the classes of recognition.

In the training phase, the extracted feature vectors of each aircraft noise signal, called training data set, are used

to train a neural net in which the aircraft types and manoeuvres constitute the output vector. This process allows

tuning the weight functions and biases between the net neuron connections.

Figure 1. Aircraft Acoustic Signature:

Training phase flow-chart

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The recognition classes, correspondent to the number

of different aircraft training data, are identified with the

output nodes. The back propagation training algorithm is

used in the training phase.

The system is trained with the signatures obtained

from different noise time histories for each aircraft and

manoeuvre. The final result is a trained neural network

which needs a sufficient number of events (patterns) to

perform the correct classification of the noise signals to

be identified.

The Aircraft Identification phase is described in

Figure 2. The process of acoustic signature extraction,

described above, is again repeated on the “unknown”

aircraft noise emission. The feature vector is then

processed by the trained neural network classifier in

order to recognize the airplane class with the relative

percentage of successful identification. The

classification process, performed by the network,

consists on the evaluation of the acoustic similarity

between the processed noise event, and those evaluated

during the training phase.

III. Experimental Training and Validation

As described in the previous paragraph, the system, developed for the aircraft acoustic signature identification,

processes noise time histories of airplanes and gives, as output, the identified airplane and manoeuvre, together with

an index of the percentage of successful identification. On the whole, the system performs on-line processing of the

noise events but requires a previous collection of aircraft noise measurements in order to learn the system classifier.

The algorithm input is the noise time history of the airplane to be identified; identification task is possible only for

airplane types and manoeuvres for which the classifier has been trained.

A preliminary evaluation of the developed algorithm for acoustic signature recognition has been numerically

performed by simulating different airplane noise sources [3]. Subsequently, the algorithm test and validation have

been experimentally carried out for different airplanes. An experimental campaign, focused on the collection of

noise measurements produced by several airplanes during take-off and landing manoeuvres, has been performed in

order to refine and validate the system. In this paper, the experimental results, in terms of airplane classes analyzed

and then recognized, are presented.

The experimental activity of ground noise measurements has been carried out at Naples airport of Capodichino.

Test activities have been executed in 2 weeks (May, 16th – 27th 2005). More than 200 aircraft noise events of five

aircraft types (Airbus A320, Boeing B737, Mc Donnel Douglas MD80, Fokker F100, Aerospatiale/Alenia ATR72),

during both take off and landing manoeuvre, have been measured.

A. Noise data Collection

The experimental activity has been performed at one of the four GESAC (Naples airport Management Company)

noise monitoring stations. The test site was located near the Naples airport (about 2 km), positioned along the

clearway zone. Final landing phase or initial take-off phase of several airplanes have been evaluated.

Figure 2. Aircraft Acoustic Signature:

Aircraft Identification phase flow-chart

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Omni-directional microphones have been placed, with dedicated tripod, at about 1 meter from the paved edge,

and pointed upwards. The acoustic events have been recorded with a digital tape recorder. Recorder configuration

and operation have been performed with a laptop PC through a SCSI connection.

Figure 5. Test Setup: noise measurement

stations

Figure 6. Data acquisition system

Figure 3. View of Naples airport and test site

Figure 4. Test site distance from Capodichino

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Noise signals have been measured with a sample frequency of 40 kHz. In figure 9, the number of the total

measurements for each class (aircraft and manoeuvres) is reported. Data sets were divided in training and

identification data. The first have been used to train the neural network, whereas the second have been employed to

test the classifier performance. Noise data measurements were grouped into 9 airplane classes:

1) Airbus A320 Take off

2) Airbus A320 Landing

3) Boeing B737 Take off

4) Boeing B737 Landing

5) Fokker F100 Take off

6) Fokker F100 Landing

7) MD82 Take off

8) MD82 Landing

9) ATR72 Landing

They correspond to 3

engine configurations:

1) 2 turbofan with

engine on wing

(A320, B737)

2) 2 turbofan with

engine on fuselage

(MD82, F100)

3) 1 turboprop (ATR72)

Figure 7. MD-80 landing noise measurement

Figure 8. A320 landing noise measurement

Aircraft Manoeuvre Number of runs Training data set Identification data set

MD 82 Take off 25 13 12

MD 82 Landing 31 13 18

A320 Take off 20 13 7

A320 Landing 28 13 15

B737 Take off 20 13 7

B737 Landing 24 13 11

F100 Take off 17 12 5

F100 Landing 23 13 10

ATR72 Landing 4 3 1

Figure 9. Number of measured runs for each class (aircraft types and

manoeuvres)

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Figure 14. ATR 72

Figure 12. MD 82

Figure 13. Fokker 100

Figure 10. A320

Figure 11. B737

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B. Measured data analysis

Noise database implementation has been performed in 3 steps:

1) Class identification (airplane and manoeuvre) of the measured noise data:

the association of each measured noise signal with a class (airplane and manoeuvre) has been achieved with the

help of:

1) Flight schedule booklet of the airport;

2) GESAC real time flight timetable (@ www.gesac.it);

3) airplanes schematic body shape drawing;

4) digital photos (with date and time);

5) video camera recording (with date and time).

2) Data transfer from recorder tape to PC hard disk (HEIM FTRANS SW).

3) Event identification (time segment between the first two points with a Sound Pressure Level 20 dB lower than

the peak. It approximately happens when the aircraft flies over the microphone location).

Meaningful differences in the measured noise data for the events of the same class (airplane and manoeuvre)

have been observed. They were due to:

1) different airplanes, airlines, weight, trust, number of passengers, airplane aging;

2) very different flight altitude;

3) very different weather conditions (temp., humidity, wind,…) and consequently different sound wave

propagation and attenuation;

4) not stationary background noise (industrial activity, birds, airport traffic…);

5) not dead acoustic environment (terrace floor and building reflection).

Not-uniformity of the different noise data for each class (airplane and manoeuvre) has represented the key

problem for the identification of complex events. The real problem, in fact, has been the extraction of a set of

features, relative to each class, despite the not uniformity of the events included in that class. In other words, data

variability of each class has been verified small enough to make the features as representative of that class of

acoustic signature.

C. Results

The proposed system for the aircraft acoustic signature identification suggests combining WMRA with ANN to

provide detailed signal processing. The acoustic signature classifier (ANN) permitted to evaluate the degree of

similarity between the input (the unknown airplane sound pressure time history) and the classes (airplane and

manoeuvre) for which it has been trained. The identification of the noise signals has been performed through an

index giving the estimation of the grade of success obtained.

The results of an identification phase are reported in figure 15. It provides different types of information: the

identified Aircraft and Manoeuvre, the index of relative percentage of successful identification, the file to be

identified (input), a plot of the noise time history (sound pressure versus time) of the processed file. Furthermore, the

button “Play” may allow to listen the processed noise emitted by the aircraft during the identified manoeuvre.

The degree of similarity, between the current file (input) and that ones employed for the database training, is

expressed in a percentage scale. The value 0% means no similarity with any class; the value 100% means full

similarity with one specific class. Thus, a rank of 100% identifies the class having the highest similarity with the

input.

In Figure 16 the experimental results of the classification test are reported. The analysis of the system

performance has been carried out in terms of correct and false identifications. This process was feasible because for

each sound pressure time history to be identified, the relative airplane and the manoeuvre were already known (see

par. III B - step 1).

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In the case of correct identification,

the 3rd column of figure 16 describes, for

each airplane and manoeuvre, the number

of correct identifications for which the

second highest classification rank is <

80%; whereas, the 4th column describes

the number of correct identifications for

which the second highest classification

rank is > 80%.

The number of wrong identifications

is reported in the 5th and 6

th column of

figure 16. In this case, the true airplane

and manoeuvre have not obtained the

maximum rank (100%) in the

classification process. Thus, for each

class, the number of occurrences for

which the true airplane has been

classified with a rank > 80% (≠ 100%) is

reported in the 5th column; whereas the

number of occurrences for which the

correct airplane and manoeuvre have

been classified with a rank < 80% is

reported in the 6th column.

Correct identification Wrong identification

Aircraft Manoeuvre 2nd < 80 % 2nd > 80 % true > 80 % true < 80 %

A320 Take off 5 1 0 1 (75%)

A320 Landing 8 4 1 (88%) 2 (70%)

B737 Take off 7 0 0 0

B737 Landing 8 1 1 (99%) 1 (<60%)

ATR72 Landing 1 0 0 0

F100 Take off 3 1 0 1 (78%)

F100 Landing 6 2 1 (88%) 1 (<50%)

MD 82 Take off 11 1 0 0

MD 82 Landing 14 1 2 (80%) 1 (<50%)

Figure 16. Classification Results

Figure 15. Aircraft Identification: MD82 Takeoff

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IV. Conclusion

Noise emissions are a critical problem for aircraft. Identification of noise source and the association between

the aircraft and the emitted sound pressure level is fundamental for airport authorities. This has interest for military

applications, also. Each aircraft presents its own signature by the point of view of noise emitted spectrum. Several

algorithms based on the recognition of this kind of information have been developed in literature. Classical FFT

extracts limited amount of information from the time signal. Wavelet may be selected in order to keep information

about time variations, essential for the detection of moving sources. Neural networks are useful classifying

algorithms, able to fix the belonging or not of a vector to a class of elements with some defined properties.

The developed system, unlike electromagnetic radar or ultrasonic sonar, has been thought as a passive (only

listening) modus operandi to allow the identification of airplane types and manoeuvres by processing only the sound

emissions. The paper demonstrates the feasibility of developing a suitable ANN to establish if a time signal,

elaborated through a wavelet process, is or isn't similar to other, having been recognized as originated by a defined

type of aircraft.

The ANN was trained by the use of a subset of experimental data, in this same frame of activity, acquired and

then validated through the comparison with another subset of elements, anyhow originated by the same experimental

campaign.

Experimental data were got at Napoli Capodichino Airport, and were relative to (five) different kinds of aircraft.

Developed SW demonstrated to give more than satisfactory results for each of the acquired spectra, being the

max error always under (10)%.

Acknowledgments

All the results of the experimental activity reported in this paper have been obtained within the MONSTER

(Monitoring Noise at European Airports) European Project, Task 2.7. Project has been funded by the European

Commission under the “Competitive and Sustainable Growth” Programme (1998-2002).

References

Books 1Huadong Wu, Mel Siegel, Pradeep Khosla Vehicle sound signature recognition by frequency vector principal component

analysis. IEEE Transactions on instrumentation and measurement, Vol 48, N. 5 October 1999. 2Sheng Y, Wavelet Transform. The Transforms and Applications handbook: Second Edition, Ed. A. D. Poularikas, 2000.

Proceedings 3V. Quaranta, I. Dimino, “A method for Acoustic Signature Identification of aircraft”, Proceeding ICSV Conference 11-14

Luglio 2005, Lisbon. 4Munich M, “Bayesian Subspace methods for acoustic signature recognition of vehicles”. Proc. EUSIPCO 2004, Sept 6-10,

2004 Vienna, Austria. 5H. Choe, R. Karsen, G. Gerhart, T. Meitzler, “Wavelet based ground vehicle recognition using acoustic signals”, Proc. SPIE

vol. 2762, pp. 434-445, 1996. 6Dress W. B., Kercel S. W. “Wavelet-based acoustic recognition of aircraft”. Harold H. Szu, Editor, Proc. Spie Vol 2242 pp

778-791 Wavelet Applications (1994).

Reports, Theses, and Individual Papers 7Somkia, Sampan, “Neural fuzzy techniques in vehicle acoustic signal classification”. Phd thesis, Dept. of Electr. Eng. ,

Virginia Polytechnic Institute and State University, Blacksburg VA, April 1997. 8Li Liu, “Ground vehicle acoustic signal processing based on biological hearing models”. Master’s thesis, University of

Maryland, College Park, 1999. 9Dress W. B., Kercel S. W. “Event identification by acoustic signature recognition”. 11th Annual Security Technology

Symposium Virginia Beach, Virginia, June 19-22 1995.