Selected Papers: Recent Trends in Electromagnetic

Formant Analysis System for Realtime
Electromagnetic Interference Source Discrimination
Fumihiko Ishiyama†, Kazuo Murakawa,and Hiroshi Yamane
Abstract
We studied a formant analysis system that extracts characteristic frequencies of acoustic noise generated by telecommunication equipment as a result of electromagnetic interference. Our realtime formant
analysis system is based on linear predictive coding and digital signal processors. We checked the system’s technical conditions for formant analysis and found that characteristic frequencies could beobtained even when the signal-to-noise ratio of the waveform was –5 dB.

1. Introduction
Malfunctions of telecommunication equipment
caused by electromagnetic interference (EMI) areincreasing along with the trend of saving power andoperating telecommunications equipment at low voltages. In the countermeasures against equipment malfunction, it takes time to identify the EMI source
causing the malfunction. Methodologies for characterizing EMI sources have not been established.
Therefore, engineers need intuition and experience todiscriminate EMI sources. Thus, there is a need toestablish a quantitative method of discriminating
EMI sources.
EMI waves invade equipment through its power
supply wire or the communications line, or directly aselectromagnetic waves. As a result, there are equipment malfunctions or problems such as audible
acoustic noise being emitted from the equipment. For
instance, the envelope of the EMI wave is detected at
the rectification circuit of the power unit, andacoustic noise is generated.
We applied formant analysis based on linear predictive coding (LPC) [1]-[5] to the time series signalof acoustic noise to discriminate EMI sources
because the EMI wave is thought to contain charac† NTT Energy and Environment Systems Laboratories
Musashino-shi, 180-8585 Japan
E-mail: ishiyama.fumihiko@lab.ntt.co.jp

12teristic information. We clarified that the EMI wave ischaracterized by the distribution diagram of the formant frequencies [6].
The analysis system should preferably work in realtime considering practical use of the system in realapplications. Therefore, we developed an EMI source
discrimination system that uses LPC-based formant
analysis and examined its realtime operation.
2. Formant analysis system
2.1 Development environment
In developing a system based on digital signal
processors (DSPs), we can describe the system in
C/C++ language or describe it using a development
environment. The main development environments
are shown in Table 1. We chose Hypersignal RIDE
[7] because it was the most suitable for developing
our realtime LPC-based formant analysis system
based on DSPs.
2.2 System configuration
The system’s processing blocks are shown in Fig. 1
and its display blocks in Fig. 2.
In Fig. 1, the upper left is the signal generation part.
The signal u(t) given here is:(1)u(t) = u1(t)u2(t) + u3(t),u1(t) = sin(2π f1(t)t) + sin(2π f2(t)t), (2)(3)f1(t) = fa1 + fb1|v(c1t)|,(4)
f2(t) = fa2 + fb2|v(c2t)|,
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Compatibility Technologies

Table 1. Development environments.
Manufacturers/products

Main purpose

Main benefits

Assessment

The MathWorks, Inc.
MATLAB

GeneralWidely usedGood

National Instruments Corp.
LabVIEW

Measurement

Supports GP-IB, CAMAC, etc.

Fair

Elanix, Inc.
SystemView

System analysis of wirelesscommunication systems

W-CDMA, Bluetooth emulation

Fair

Hyperception, Inc.
Hypersignal RIDEDSP system development

Realtime audio/visual analysis

Excellent

Formant analysis vs. FFT analysis
Signal generator

Sweep (Chirp)
Generator 2

Square Wave
Generator 1

LinearPredictive
Coding 1

FFT 1

Magnitude 1

Multiply 1

Sweep (Chirp)
Generator 1

dB Amplitude 1

Add 1

Add 3

Noise Generator 1

Gain=–1

Formant analysis
Sets LPC Order

Sets Amplitude

FFT 2

Magnitude 2

Parameter input

Exponential 1

Scale 1dB Amplitude 2

FFT analysis
Add 2Fig. 1. Processing blocks of the system.

u2(t) = sgn(v(c3t)),
u3(t) = d · rand,(5)(6)

xv(x) = x – 2   – 1.
2

(7)

Here, u1(t) consists of two chirp frequencies, where
the frequencies change from fa1 to fa1 + fb1 and from
fa2 to fa2 + fb2. The periods of these frequency chirps
are 2/c1 and 2/c2. The u2(t) is the modulation term ofa rectangular wave with period 2/c3 on u1(t) to consider a pseudo-random number pattern of the spread
Vol. 2 No. 11 Nov. 2004

spectrum. The u3(t) is the random noise term. The
parameters are set as follows: fa1 = 300 Hz, fb1 = 3300
Hz, c1 = 1/60 Hz, fa2 = 500 Hz, fb2 = 2500 Hz, and c2= 1/7 Hz. The c3 is obtained from the “Wave form”
bar in Fig. 2. The d is the amplitude of the random
noise obtained from the “Noise intensity” bar inFig. 2.
The lower left of Fig. 1 is the parameter control partfor the signal generation and formant analysis parts.
It contains blocks giving the strength of the random
noise imposed on the signal in the signal generation
13Selected Papers

Formant analysis vs. FFT analysisLPC coefficients
Power spectrum(FFT)BA

LPC coefficients
Time series of formant frequencies

BSpectrum envelope(formant analysis)

ASpectrum peaks calculated from LPC coefficients
LPC order

Power spectrum vs. power envelope
524

off

even
Wave form

Noise intensity

Noise switch

Fig. 2. Display blocks of the system.

part and blocks giving the analytical degree of formant analysis. We obtained the strength of the random noise in units of dB. The index is converted in
the “Exponential 1” block and fed to the “Noise Generator 1” block. The “Scale 1” block is used as aswitch that imposes the random noise. The analyticaldegree of LPC-based formant analysis, which is usually an even number, is obtained from the output of
the “Add 2” block. Then, the output of the dial block,which is showing an even number (8) in Fig. 2, isadded to the output of the block where either 0 and 1
is taken (also shown in Fig. 2), and the combined
value is passed to the “Linear Predictive Coding 1”
block, where the formant is analyzed.
The upper right of Fig. 1 is the formant analysispart. Here, the spectrum envelope is calculated using
the LPC coefficients obtained from the time series
signal [1]-[5]. The procedure is as follows. The LPC
coefficients are obtained in the “Linear Predictive
Coding 1” block. The result is passed to the “FFT(fast Fourier transform) 1” block and Fourier transformation is performed. The result is passed to the14“Magnitude 1” block, which is usually used to calculate the power spectrum from the result of the “FFT”
block. The spectrum envelope is obtained by taking
the logarithm of the output of the “Magnitude 1”
block in the “dB Amplitude 1” block and taking thereciprocal in the “Gain = –1” block. It is passed to thedisplay panel in Fig. 2.
The lower right in Fig. 1 is the Fourier analysis part.
It is provided for comparison with formant analysis.
Here, the power spectrum is given by Fourier transform in the “FFT 2” block, and the result is passed tothe “Magnitude 2” block. The logarithm of the power
spectrum is taken in the “dB Amplitude 2” block. Theresult is passed to the display panel in Fig. 2.
2.3 LPC-based formant analysis and Fourieranalysis
The lower left of Fig. 2 is the operation panel forgiving the LPC order of the LPC-based formant
analysis; in this example, the eighth degree of theorder is set here [6]. The lower right is the operation
panel to control the parameters for the signal generaNTT Technical Review

Selected Papers

Formant analysis vs. FFT analysis

LPC coefficients

Spectrum peaks calculated from LPC coefficients
LPC order

Power spectrum vs. power envelope
245on

Wave form

Noise intensity

Noise switch

evenFig. 3. Displays for SNR = –5 dB.

tion part. The value of twenty-four is given to the“Wave form” bar, which corresponds to c3 in Eq. (5).
The noise intensity is set to 5 dB, but it is disabled by
the “Noise switch” (indicated by “off” on the displaypanel).
There are three display windows in the upper partof Fig. 2. The upper-left window displays the LPC
coefficients obtained by the LPC method. The rightwindow shows the spectrum envelope obtained byformant analysis and the power spectrum obtained by
Fourier transform. The change in the temporal position of the peak frequency of the spectrum envelope
is shown in the lower window on the left, where thehorizontal axis is time and the vertical axis is frequency.
Two spectra are displayed in the window on theright of Fig. 2. The spectrum with lots of fine peakstructure is the power spectrum obtained by Fourier
analysis, and the spectrum with two peaks shown in asmooth line is the spectrum envelope obtained by
LPC-based formant analysis.
As a result, we found that the outline of the peakVol. 2 No. 11 Nov. 2004

structure of the power spectrum (peaks A and B in thewindow) was detected by LPC-based formant analysis, and the signal could be characterized by the frequencies. The lines correspond to two frequencies
f1(t) and f2(t), which are functions of the frequency
chirp of the sinusoidal signals given in the signal generation part, appearing in the lower left window. The
A and B in this window correspond to A and B in theright window.
We found that LPC-based formant analysis could
extract the main frequency information of the timeseries data of the signal, and the input signal could becharacterized by that frequency information.
Figure 3 shows the case when random noise wasintroduced.
The signal-to-noise ratio (SNR) was chosen to be–5 dB, so the signal was completely buried in the random noise. In this case, the characteristic frequenciesof the original signal could not be read from thepower spectrum by Fourier analysis. On the otherhand, the spectrum envelope obtained from LPCbased formant analysis was not affected, and the15Selected Papers

Formant analysis vs. FFT analysis

LPC coefficients

Spectrum peaks calculated from LPC coefficients
LPC order

Power spectrum vs. power envelope
249on

Wave form

Noise intensity

Noise switch

evenFig. 4. Displays for SNR = –9 dB.

characteristic frequencies of the original signal wereextracted. The peak frequency of the spectrum envelope displayed in the lower left window was almost
the same as that obtained when random noise was notintroduced (Fig. 2). Thus, we think that LPC-basedformant analysis can cope with severe measurement
conditions because it is more robust than Fourier
analysis.
Figure 4 shows the case where the intensity of therandom noise was chosen to be 9 dB.
In this case, even when LPC-based formant analysis was used, the target signal was buried in the random noise. However, we could recognize signs of thetarget signal from the time series of the peak frequency of the spectrum envelope displayed in the lowerleft window.

tions equipment by EMI waves. We chose to use
Hypersignal RIDE to develop the system.
We found that LPC-based formant analysis wasmore robust than Fourier analysis for random noiseand gave an analytical result equal to the case withoutthe noise even in a situation that is hard to analyze by
Fourier analysis. We could find characteristic frequencies even when the SNR was –5 dB. Therefore,
we believe that LPC-based formant analysis is more
effective than Fourier analysis.
Further studies are needed to improve the formant
analysis system, construct a formant database systemthat presents candidate EMI sources in cooperation
with the formant analysis system, and expand theapplication to the high frequency region.
References

3. Conclusion[1]

We developed an LPC-based formant analysis system to investigate how to discriminate EMI sourcesfrom the acoustic noise generated in telecommunica16

[2]
[3]

J. Makhoul, “Linear prediction, a tutorial review,” Proc. IEEE, Vol.
63, pp. 561-580, 1975.
A. Acero, “Acoustical and Environmental Robustness in AutomaticSpeech Recognition,” Kluwer Academic Publishers, Boston, 1992.
S. Furui, “Digital Speech Processing, Synthesis, and Recognition,”

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Selected Papers[4][5][6][7]

Marcel Dekker, Inc., New York, 1989.
J. D. Markel and A. H. Gray Jr., “Linear Prediction of Speech,
Springer-Verlag,” New York, 1976.
R. W. Schafer and J. D. Markel, “Speech Analysis,” IEEE press, NewYork, 1979.
F. Ishiyama and H. Yamane, “Identification of the source of acoustic
noise caused by electromagnetic disturbances using formant analysis,” IEEE Int. Sym. on EMC, Vol. 2, pp. 696-700, Aug. 2002.
http://www.hyperception.com/RIDE/

Fumihiko Ishiyama
Research Engineer, Energy Systems Project,
NTT Energy and Environment Systems Laboratories.
He received the B.S. and M.S. degrees inphysics from Tohoku University, Sendai, Miyagi
in 1988 and 1990, respectively. In 1990, hejoined NTT Telecommunication Networks Laboratories, Kanagawa. He has been researching
techniques for analyzing audible acoustic noisein telecommunication equipment caused by electromagnetic interference. He is a member of theInstitute of Electronics, Information and Communication Engineers (IEICE).

Kazuo Murakawa
Senior Manager, Intellectual Property Strategy
Planning Group, Intellectual Property Center.
He received the B.E., M.E., and D.E. degrees
in electrical engineering from Kumamoto University, Kumamoto in 1983, 1985, and 2001,
respectively. In 1985, he joined the ElectricalCommunication Laboratories, NTT, Ibaraki. He
has been researching and developing electricfield measurement and estimation systems forelectromagnetic interference. In 2004, he moved
to Intellectual Property Center. He is the rapporteur of ITU-T SG5 question 10. He is a memberof IEICE and the Institute of Electrical Engineersof Japan.

Hiroshi Yamane
Senior Research Engineer, Supervisor, Energy
Systems Project, NTT Energy and Environment
Systems Laboratories.
He received the B.E., M.E., and D.E. degrees
in electronic engineering from Ibaraki University, Hitachi, Ibaraki in 1980, 1982, and 1997,
respectively. In 1982, he joined the ElectricalCommunication Laboratories, Nippon Telegraphand Telephone Public Corporation (now NTT),
Ibaraki. He has been researching lightning surge
and overvoltage protection for telecommunications systems. He is a member of IEICE.

Vol. 2 No. 11 Nov. 200417