J. Biomedical Science and Engineering, 2010, 3, 187-192
doi:10.4236/jbise.2010.32024 Published Online February 2010 (http://www.SciRP.org/journal/jbise/
JBiSE
).
Published Online February 2010 in SciRes. http://www.scirp.org/journal/jbise
Iterative spectral subtraction method for millimeter-wave
conducted speech enhancement
Sheng Li1,2, Jian-Qi Wang1*, Ming Niu1, Xi-Jing Jing1, Tian Liu1
1Department of Biomedical Engineering, the Fourth Military Medical University, Xi’an, China;
2The Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Department of Biomedical Engineering,
School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, China.
Received 4 November 2008; revised 4 December 2009; accepted 7 December 2009.
ABSTRACT
A non-air conducted speech detecting method has
been developed in our laboratory by using millimeter
wave radar technology. Because of the special attrib-
utes of the millimeter wave, this method may consid-
erably extend the capabilities of traditional speech
detecting methods. However, radar speech is substan-
tially degraded by additive combined noises that in-
clude radar harmonic noise, electrocircuit noise, and
ambient noise. This study, therefore, proposed an
iterative spectral subtraction method which can be
adaptively estimate noise spectrum at every iteration,
and reduce the musical noise remained in the previ-
ous spectral subtraction process. Results from simu-
lations as well as evaluations confirm that the pro-
posed method satisfactorily reduces whole-frequency
and musical noises and produces good speech quality.
Keywords: Millimeter Wave; Speech Detecting; Speech
Enhancement
1. INTRODUCTION
The speech, which is produced by speech organ of hu-
man beings [1,2], is well known that it can be spread and
perception by means of air, and can be detected and re-
corded by acoustic sensors. However, air is not the only
medium which can spread and be used to detect speech.
For example, voice content can be transmitted by way of
bone vibrations. This vibration, therefore, can be picked
up using the bone-conduction sensors at special location
[3]. Other medium, such as infrared ray, ultrasound wave,
laser light also can be used to detecting the non-air
spread speech or noise for some special applications [4].
Li Zong Wen (1996) [4] reported another medium,
millimeter wave (MMW) radar, as well as light radar
and laser radar, can detect and identify out exactly the
existential speech signals in free space from a person
speaking through the electromagnetic wave fields by
principle and experiment. Since the microwave radar has
low range attenuation, and has attribute of noninvasive,
safe, fast, portable, low cost fashion [5], it may extend
traditional speech detecting method to a large extent, and
provide some exciting possibility of wide applications:
the speech and acoustic signal directional detection in
complex and rumbustious acoustic environment, due to
its better sense of direction; the tiny acoustic or vibrant
signal detection which cannot be detected by traditional
microphone; the microwave radar also can be used in the
clinic to assist diagnosis or to measure speech articulator
motions [6].
However, there has been little previous research work
concentrated on the research of speech which is pro-
duced by MMW radar, some studies with respect to the
MMW radar speech concentrated on the MMW non
acoustic sensors [5,7], in order to measure speech ar-
ticulator motions, such as vocal tract measurements and
glottal excitation [5].
Although MMW radar provides another method to
detect speech, the MMW radar speech itself has several
serious shortcomings including artificial quality, reduced
intelligibility, and poor audibility. This is not only be-
cause some harmonic of the EMW and electrocircuit
noise are combined in the detected speech due to the
different detecting methods from traditional air conduct
speech, but also the channel noise, as well as ambient
noise combined in the MMW radar speech. These com-
bined noise components are quite larger and more com-
plex than traditional air conduct speech, and are the big-
gest problem which must be resolved for the application
of the MMW radar speech. Therefore, speech enhance-
ment is a challenging topic of MMW radar speech re-
search.
The spectral subtraction method is the most widely
used, and has been shown to be an effective approach for
noise canceling, in order to improve the intelligibility
and quality of digitally compressed speech. Due to the

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simplicity of implementation, and low computational
load, the spectral subtraction method is the primary
choice for real time applications [8]. In general, this
method enhanced the speech spectrum by subtracting an
average noise spectrum from the noisy speech spectrum,
here the noise is assumed to be uncorrelated and additive
to the speech signal. The phase of the noisy speech is
kept unchanged, since it is assumed that the phase dis-
tortion is not perceived by human ear.
However, the serious draw back of this method is that
the enhanced speech is accompanied by unpleasant mu-
sical noise artifact which is characterized by tones with
random frequencies. Although many solutions have been
proposed to reduce the musical noise in the subtrac-
tive-type algorithms [9,10,11,12,13], results performed
with these algorithms show that there is a need for fur-
ther improvement. Furthermore, in order to prevent de-
structive subtraction of the speech while removing most
of the residual noise, it is necessary to propose a new
approach to improve the subtraction procedure.
Therefore, the purpose of this investigation is moti-
vated by the need of improving EMW radar speech, es-
pecially in the electronic environments. An iterative
spectral subtraction algorithm is proposed to adaptively
estimate noise spectrum at every iteration, and reduce
the musical noise remained in the previous spectral sub-
traction process. The results suggest that for the proper
iteration number, the proposed iteration number can sig-
nificantly remove the musical noise, and improve the
speech quality.
2. METHODS
2.1. The Description of the System
The schematic diagram of the speech-detection system is
shown in Figure 1. A phase-licked oscillator generates a
very stable MMW at 34 GHz with an output power of 50
mW. The output of the amplifier is fed through a 6 dB
directional coupler, a variable attenuator, a circulator,
and then to a flat antenna. The 6 dB directional coupler
branches out 1/4 of the amplifier output to provide a
reference signal for the mixer. The variable attenuator
controls the power level of the microwave signal to be
radiated by the antenna. The radiated power of the an-
tenna is usually kept at a level of about 10–20 mW. The
flat antenna radiates a microwave beam of about 9º beam
width aimed at the opposing human subjects standing or
sitting directly in front of the antenna. The echo signal is
received by the same antenna, which is a 34G Hz MMW
signal modulated by the speech which is produced by the
larynx of the opposing human subjects. This signal is
then mixed with reference signal in a double-balanced
mixer. The mixing of the amplified speech signal and a
reference signal in the double-balanced mixer produces
low-frequency signals and is amplified by a signal proc-
essor and then passed through a A/D converter before
reaching computer to get further processor. For More
details of description of the system, the reader is referred
to [14] and [15].
2.2. Iterative Spectral Subtraction Method
The iterative spectral subtraction algorithm is based on
the assumption that the additive noise will be stationary
and uncorrelated with the clean speech signal. If ,
the noisy speech, is composed of the clean speech signal
()yn
()
s
n and the uncorrelated additive noise signal ,
then:
()dn
() () ()yn sn dn

 (1)
The power spectrum of the corrupted speech can be
approximately estimated as:
22
() ()()YSD
2

  (2)
where 2
()Y

, 2 ()S

and 2 ()D

represent the noisy
speech short-time spectrum, the clean speech short-time
spectrum, and the noise power spectrum estimate, re-
spectively.
Most of the subtractive-type algorithms have different
variations allowing for flexibility in the variation of the
spectral subtraction. Berouti et al. (1979) [16] proposed
the generalized spectral subtraction scheme is described
as follows:
ˆ() 1
ˆ
() (),if
ˆ() ()
ˆ(), otherwise
D
YD
SY
D



















(3)
where (1)

  is the over-subtraction factor [16],
which is a function of the segmental SNR. (0 1)




is the spectral floor, and

is the exponent determining
the transition sharpness. Here we set 2

 and

=
0.002.
Figure 1. Schematic diagram of the speech-detec-
tion system.

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Figure 2. The proposed speech enhancement scheme.
In order to decrease the musical noise, which is pro-
duced by the speech enhancement procedure, an iterative
spectral subtraction algorithm is proposed in this study.
The iterative method is motivated from Wiener filtering
which is one of the speech enhancement techniques
[17,18]. In this study, the output of the enhanced speech
using traditional spectral subtraction method is used as
the input signal of the next iteration process.
Figure 2 shows the scheme of the proposed MMW
speech enhancement algorithm. It can be seen from the
figure that after the first spectral subtraction process, the
type of the additive noise is changed to the musical noise.
As the output signal is used as the input signal of the
next iteration process, the musical noise is reestimated,
this new estimated noise, furthermore, is been used to
process the next spectral subtraction (that is, subtracted
by the new noisy speech), therefore, an enhanced output
speech signal can be obtained, and the iteration process
goes on. If we regard the process of noise estimate and
the spectral subtraction as a filter, then the output signal
of the filter is used not only for designing the filter but
also as the input signal of the next iteration process.
More important, this filter can be refreshed adaptively
by reestimate the musical noise so that to improve the
speech quality effectively.
2.3. Noise Estimation
The noise in the radar speech, which included of each
order of the EMW harmonic, the channel noise, the am-
bient noise combined in the MMW radar speech, and so
on, is highly nonstationary noise, it is imperative to up-
date the estimate of the noise spectrum frequently. This
study adopted the minimum-statistics method proposed
by Cohen and Berdugo (2002) [19] for noise estimation,
since this method is computationally efficient, robust
with respect to the input signal-noise ratio (SNR), and
have an ability to quick follow the abrupt changes in the
noise spectrum. The minimum tracing is based on a re-
cursively smoothed spectrum which is estimated using
first-order recursive averaging
22
(,)( 1,)(,)
ˆˆ ˆ
()() (1)()
klDklDkl
DD Y
2



 
1 0
D


, (4)
where 2
(,)
ˆ()
kl
D

and 2
(,)
ˆ()
kl
Y

are the kth compo-
nents of noise spectrum and noisy speech spectrum at
the frame l, and
D

is a smooth parameter. Let (,)pkl

denote the conditional signal presence probability in
Cohen and Berdugo (2002) [19], then Eq. (4) implies
22
(,)( 1,)(,)
ˆˆ ˆˆ
()(,)() (1(,))()
klDk lDkl
DklD klY
2
ˆ



  (5)
where is a time-varying
smoothing parameter. Therefore, the noise spectrum can
be estimated by averaging past spectral power values.
For More details of description of this algorithm, the
reader is referred to [19,20].
ˆ(,)(1) (,)
DDD
klp kl 


3. EXPERIMENTS
Ten healthy volunteer speakers, 6 males and 4 females,
participated in the radar speech experiment. All the sub-
jects were native speakers of Mandarin Chinese. Their
ages varied from 20 to 35, with a mean age of 28.1 (SD
=12.05). All the experiments were conducted in accor-
dance with the terms of the Declaration of Helsinki
(BMJ 1991; 302, 1194), and appropriate consent forms
were signed by the volunteers. Ten sentences of Manda-
rin Chinese were used as the speech material for acoustic
analysis and acceptability evaluation. The lengths of the
sentences varied from 6 words (5.6 s) to 30 words (15 s).
The sentences were spoken by each participant in a quiet
experimental environment. The speakers were instructed
to read the speech material at normal loudness and
speaking rates.
For the perceptual experiment, eight listeners were
selected to evaluate the acceptability of each sentence
based on the criteria of mean opinion score (MOS),
which is a five-point scale (1, bad; 2, poor; 3, common;
4, good; 5, excellent). All the listeners were native
speakers of Mandarin Chinese, had no reported history
of hearing problems, and were unfamiliar with MMW
radar speech. Their ages varied from 22 to 36, with a
mean age of 26.37 (SD = 4.63).
In order to test the effectiveness of the proposed
method, two different types of background noise, namely,
white Gaussian noise and speech babble noise, were
added to the enhanced MMW radar speech; both noises
were taken from the Noisex-92 database. These two rep-
resentative noises have a greater similarity to actual
talking conditions than the other noises. Noises with
SNRs of 0 dB were added to the original MMW radar
speech signal.

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4. RESULTS AND DISCUSSIONS
JBiSE
In order to evaluate and compare the performance of the
proposed enhancement algorithm, two other algorithms
are performed in this study, they are: traditional spectral
subtraction method and noise-estimation algorithm [21].
For the purpose of analyzing the time-frequency distri-
bution of the original/enhanced speech, speech spectro-
grams were provided since they have been identified as a
well-suited tool for observing both the residual noise and
speech distortion. In addition, results are also measured
subjectively by Mean Opinion Score (MOS) in condi-
tions of additive white Gaussian noise as well as Bobble
noise (for MOS) for the algorithm evaluation.
Figure 3 shows the spectrograms of the original radar
speech (a), the enhanced speech using traditional spec-
tral subtraction algorithm (b), the enhanced speech using
noise-estimation algorithm (c), and the proposed itera-
tive spectral subtraction algorithm (d) (the iteration
number is set to 5). The speech material is a Chinese
sentence “Di si jun yi da xue” (in English, the Fourth
Military Medical University).
Because of its different speech detecting theory and
working conditions, non-air conducted speech has some
special attributes. As stated earlier, the most important is
that combined noises are introduced into the original
MMW radar speech. These noises can be clearly seen in
Figure 3(a), especially in the speech-pause region. It can
also be seen from the figure that the combined noise is
mainly concentrated in the low-frequency components,
roughly below 3 KHz. Figure 3(b,c) shows that the
spectral subtraction algorithm and the noise estimate
algorithm are effective in reducing the combined radar
noises. However, there are still too much remnant noise
in the enhanced speech, especially in the frequency sec-
tion in which the noise are concentrated, suggesting that
the noise reduction are not satisfactory. Figure 3(d)
shows that the proposed algorithm not only greatly re-
duces the low-frequency noise, in which the combined
radar noise is concentrated, but it also completely elimi-
nates the high-frequency noise. It can be seen from the
figure that in the speech-pause regions the residual noise
(a) (b)
(c) (d)
Figure 3. The Spectrogram of the sentence “Di Si Jun Yi Da Xue”. (a) The original spectrogram of the millimeter wave con-
ducted speech. (b) Enhanced radar speech obtained by the traditional spectral subtraction method. (c) Enhanced radar speech
obtained by the noise estimated algorithm. (d) Enhanced radar speech obtained by the proposed algorithm.

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is almost eliminated. Moreover, it is clear that the resid-
ual noise is greatly reduced and has lost its structure.
These results suggest that the proposed algorithm
achieves a better reduction of the whole-frequency noise
than traditional spectral subtraction methods.
The perceptual score of the noisy speech and the en
hanced noisy speech are shown in Figure 4. Mean Opin-
ion Scores (MOS) were used for 100 sentences produced
by ten volunteer speakers. The noisy speech in the cases
of the additive white and babble noises had SNR inputs
of 0 dB. It can be seen from the figure that the original
noisy speech has “bad” perceptual effects, but the score
of the enhanced speech obtained by using the proposed
algorithm is much better. Comparing the two noises, it
can be seen from the figure that the MOS for white noise
is a little higher than for babble noise. This suggests that
the proposed algorithm is more “sensitive” to white
noise, however, the difference is small.
The iteration times is another important factor which
has effects on the performance of speech enhancement.
In order to explore the relationship between the per-
formance of speech enhancement and the iteration times,
the variation of the mean segmental SNR of the radar
speech with iteration times are shown in Figure 5. It can
be seen from Figure 5 that the SNR increased as the
iteration number increased, which suggest the larger
iteration number will corresponding to the better speech
enhancement performance and the less musical noise.
However, both performed waveform and the corre-
sponding spectrogram suggest that the larger iteration
number would eliminate part of the normal speech
component to some extent while it works overmuch ef-
fectively on reducing the musical noise, therefore, the
proposed iteration number for the radar speech is 3 to 5.
As a single channel subtractive-type speech enhance-
ment method, the algorithm proposed in this paper can
be applied for the enhancement of non-air conducted
speech using available electronics. For example, a mil-
limeter wave conducted speech enhancing system, into
Figure 4. Perceptual results of the noisy and enhanced
noisy speech based on the MOS criteria.
Figure 5. Variations of mean segmental SNR of the
MMW radar speech with iteration times.
which this algorithm is embedded, can be developed.
With the help of digital signal processing (DSP) tech-
nology, the speech enhancement function can be realized
with a microprocessor and implanted into a ra-
dar-telephone, radar-microphone, or other electronic
equipment. Different enhancement algorithms, suitable
for different noise conditions, can be selected by a
switch. With the development of efficient enhancement
methods, the quality of non-air conducted speech will be
vastly improved and will provide better perception.
5. CONCLUSIONS
As a non-air conducted speech, MMW radar speech may
have greater advantage and wider applications than air
conduct speech. However, the complex noises added in
the radar speech decreased the speech quality to a large
extent. Therefore, an improved spectral subtraction
method, iterative spectral subtraction algorithm are used
in this study in order to decrease the complex noise and
the musical noise. The results from both simulation and
evaluation suggest that for the proper iteration number,
this method achieves a better reduction of the whole-
frequency noise, the musical noise, and yields good
speech quality.
6. ACKNOWLEDGEMENTS
This work was supported by the National Natural Science Foundation
of China (NSFC, No. 60571046), and the National postdoctoral Sci-
ence Foundation of China (No. 20070411131). We also want to thank
the participants from the E.N.T. Department, the Xi Jing Hospital, the
Fourth Military Medical University, for helping with data acquisition
and analysis.
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