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Title

Study on Blind Method of Estimating Speech
Transmission Index from Noisy Reverberant
Amplitude‑Modulated‑Signals

Author(s)Miyazaki, Akikazu; Morita, Shota; Unoki, Masashi

Citation

Journal of Signal Processing, 18(4): 201‑204

Issue Date

2014

Type

Journal Article

Text version

publisher

URL

http://hdl.handle.net/10119/12891

RightsCopyright (C) 2014 信号処理学会. Akikazu
Miyazaki, Shota Morita, and Masashi Unoki,
Journal of Signal Processing, 18(4), 2014, 201‑204. http://dx.doi.org/10.2299/jsp.18.201

Description

Japan Advanced Institute of Science and Technology

Journal of Signal Processing, Vol.18, No.4, pp.201-204, July 2014

SELECTED PAPER AT NCSP'14

Study on Blind Method of Estimating Speech Transmission Index from Noisy Reverberant
Amplitude-Modulated-Signals
Akikazu Miyazaki, Shota Morita and Masashi Unoki
School of Information Science, Japan Advanced Institute of Science and Technology
1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan
E-mail: {miyazaki.aki, s-morita, unoki}@jaist.ac.jp
Abstract
The authors previously proposed a method for blindly estimating the speech transmission index (STI) based on the concept of the modulation transfer function (MTF). This method,
however, over- or under-estimates STIs from observed signals
in real environments due to the effect of background noise. In
this paper, the proposed method was developed from the previous method to resolve the problem by simultaneously estimating the effects of inverse MTFs in noisy and reverberant
environments. Simulations were carried out to verify that the
proposed method can correctly estimate STIs in noisy reverberant environments by using noisy reverberant AM signals.
The results revealed that the proposed approach could be used
to effectively estimate STIs from noisy reverberant signals invarious room acoustics.
1. Introduction
The speech transmission index (STI) is an important objective measure that is used to assess the quality of speech transmission in room acoustics. The quality of speech transmission in room acoustics corresponds to “bad,” ranging from 0.0
to 0.3, “poor,” ranging from 0.3 to 0.45, “fair,” ranging from
0.45 to 0.6, “good,” ranging from 0.6 to 0.75, and “excellent,”
ranging from 0.75 to 1.0. Recently, it is also well known
that STI has a correlation with listening difficulty higher thanspeech intelligibility.
The method for calculating STI was proposed by Houtgast
et al. [1] and was standardized in IEC 60268-16 [2]. STI
is based on the concept of the modulation transfer function(MTF). This concept is aimed at accounting for the relationship between the transfer characteristics of the enclosure andtemporal envelopes of input and output signals, as shown in
Fig. 1. STI calculation needs direct measurements of room
impulse responses (RIRs) (or MTFs). However, people must
be excluded from measurements of RIR for the protection ofhearing because these measurements use an input signal witha high sound pressure level. Therefore, it is difficult to calculate STI by measuring RIRs in sound environments where
people cannot be excluded, e.g., in public spaces such as stations, airports, and concourses.
The authors previously proposed a specified method for

Journal of Signal Processing, Vol. 18, No. 4, July 2014

blindly estimating the STI in room acoustics based on theconcept of the MTF [3]. Previous results revealed that (1)the previous method could estimate STIs even if RIRs could
not be approximated as with Schroeder’s RIR model, (2)the previous method effectively estimated STIs from reverberant amplitude modulated (AM) and speech signals, and(3) the previous method could estimate STIs in real environments where people were present in the room [3]. However,there is a problem in that the previous method over- or underestimates the STI from the observed signals in real environments, due to the effect of background noise because thismethod assumes that room acoustics can be regarded as reverberant environments without noise.
In this paper, we propose a method for blindly estimating
STI from observed noisy reverberant signals. The proposed
method involves estimating inverse MTF from the observed
signals with the same approach we previously used [3]. An
advantage in our approach enables us to estimate STI in room
acoustics where people cannot be excluded, without having
to measure RIRs and the signal-to-noise ratio (SNR).
2. Calculation of Speech Transmission Index
Schroeder’s RIR model was used in the STI calculation
method. This model was defined ash(t) = eh (t)ch (t) = aexp(−6.9t/TR )ch (t)(1)

where a is a gain factor of RIR, TR is a parameter of reverberation time, and ch (t) is a carrier such as a random variable ofGaussian white noise. The MTF of Schroeder’s RIR model
can be represented as2 (− 12 )TRmR (fm , TR ) = 1 + 2πfm
(2)13.8
The MTF mR (fm , TR ) has the characteristics of low-pass filtering as a function of the modulation frequency fm and TR .
The process of calculating STI can be summarized into five
steps.
(i) Calculating MTFs in seven octave-bands: MTFs mk (fi )are measured in seven octave-bands (center frequencies (CFs)range from 125 Hz to 8 kHz, and k = 1, 2, 3, · · · , 7). In ad201

Intensity

1/fm

#1
Time
#2

Input <x 2(t)>Reverberation<h2(t)>
2

Output <y (t)>Intensity

Reverberant
signalOctave-band
filterbank

RIR
h(t)

125 Hz250 Hz#3 500 Hz#4 1 kHz#5 2 kHz#6 4 kHz#7MTF
h1(t) calc. m1(fm)MTF
h2(t) calc. m2(fm)y(t)

Estimated
RIR

MTF
RIR
estimation
estimation(Eq. (9)) TR,n (Eq. (8))^h(t)^h(t)=at^ (b-1) exp(-6.9t/T^R)ch(t)Estimated
STI

STI
calculation(Eq. (7))

^

STI
calc.

STI

Figure 2: Block diagram of previous method

MTF
calc. m7(fm)where b is an order of the RIR. The generalized RIR model
has higher flexibility than Schroeder’s RIR model. The MTFof the generalized RIR model is shown asTime��2 �− 2b−1
�2
Figure 1: General scheme for STI calculations based on MTF
TRmR (fm , TR , b) = 1 + 2πfm
(9)concept
13.8
1/fm

8 kHz

h7(t)

The difference between the MTFs of Schroeder’s RIR model
dition, these have fourteen modulation frequencies (fi ranges
and the generalized RIR model is the exponent part −(2b −from 0.63 to 12.5 Hz and i = 1, 2, 3, · · · , 14).
1)/2.
�Figure 2 shows a block diagram of the previous method.
2
mk (fi ) = 1/ 1 + (2πfi TR /13.8)(3)This method consists of three blocks: (I) estimating the in(ii) Calculating SNRs from MTFs: SNRs N (k, i) are calcu- verse MTF from the observed signals by using the generalized RIR model, (II) estimating RIR from the estimated MTF,
lated from mk (fi ) asand (III) calculating STI by using the above method.
(4)
N (k, i) = 10 log10 mk (fi )/(1 − mk (fi ))For estimating the MTF, the previous method had threeusefulcharacteristics: (1) the MTF at 0 Hz was 0 dB, (2) the(iii) Calculating transmission indices (TIs): TIs T (k, i) are
original
modulation spectrum at the dominant modulation frecalculated by normalizing the N (k, i) as
quency of fm was the same as that at 0 Hz, (3) and the entire⎧(15 < N (k, i))
modulation spectrum of the reverberant signal was reduced as⎨ 1,
N (k,i)+15
reverberation time increased, in accordance with MTF. These
T (k, i) =(5)
, (−15 ≤ N (k, i) ≤ 15)
30⎩
useful characteristics enabled us to model a strategy to blindly0,(N (k, i) < −15)
estimate the TR and b of the inverse MTF m−1
R (fm ) that re(iv) Calculating modulation transmission indices (MTIs): stores the original modulation spectrum from the entire modMTIs M (k) are calculated by averaging T (k, i) as
ulation spectrum. Specifically, the optimal TR and b are obtained with the minimum root mean square (RMS). These are
14
1 �
M (k) =T (k, i)(6) defined as
14 i=1{T̂R , b̂} = arg min RMS(TR , b)
(10)
TR ,b
(v) Calculating STI: STI is calculated by M (k) as
�
�
L
�1 �
7
�
�
,
b)
=
[|Ey (fm� )| − mR (fm� , TR , b)]2
RMS(TR
W (k)M (k)
(7)
STI =
L
k=1

�=1

(11)
Here, the contribution rates W (k) are determined to be
W (1) = 0.129, W (2) = 0.143, W (3) = W (4) = 0.114, where Ey (fm� ) is the reduced modulation spectrum at a specific fm� and mR (fm� , TR , b) is the derived MTF of the genW (5) = 0.186, W (6) = 0.171, and W (7) = 0.143.
eralized RIR at a specific fm� as a function of TR and b, and
L is 2. Then, an RIR h(t) is estimated on the basis of the
3. Previous Method
generalized RIR model with TR and b. Finally, the algorithm
The previous method assumed that room acoustics can be described in Section 2 is used to calculate the STI from theregarded as reverberant environments without noise and a dif- estimated MTF.
The previous method studied a method of blindly estimatfuse sound field. In addition, Schroeder’s RIR model wasing
STI in reverberant environments. Therefore, the previous
modified as the generalized RIR model to account for the temmethod
could be used to estimate the STI without having toporal envelope of the real RIR as follows.
measure RIR in reverberant environments. However, there ish(t) = at(b−1) exp(−6.9t/TR )ch (t)(8) a problem in that the accuracy of the estimated STI is reduced202

Journal of Signal Processing, Vol. 18, No. 4, July 2014

in noisy reverberant environments.
4. Proposed Method
The proposed method expands the previous method fornoisy reverberant environments to resolve the above problem.
The authors already proposed a method for restoring an MTF
based power envelope in noisy reverberant environments [4].
This method can be used to estimate the STI in noisy reverberant environments.
Suppose that x(t), y(t), h(t), and n(t) are the original signal, noisy reverberant signal, RIR, and background noise, respectively. The signal is also assumed to be composed of thetemporal envelope e(t) and the carrier c(t) as random variables of Gaussian white noise. By assuming linear systems
and the mutual independence between carriers, e2y (t) can be
represented as e2y (t) = e2x (t) ∗ e2h (t) + e2n (t), where “∗” indicates convolution.
The MTF in the noisy reverberant environment can be represented as [4]:on the speech power envelope. The second block is a speech
power envelope dereverberation based on the MTF concept.
The last block is a threshold processing on the dereverberated
speech power envelope for speech/non-speech decision.
The SNR is estimated from the mean power ratio of speech
sections to noise sections. However, speech sections are affected due to the additive noise effect. Therefore, the estimated SNR can be obtained by removing the additive noise
effect from speech sections.
The mean power of e2n (t), e2n is calculated from noise signals, and power envelope of reverberant signals is calculated
by subtracting e2n from e2y (t). This signal is an input signal
for MTF estimation in the previous method. Therefore, theproposed method uses MTF estimation and RIR estimationfrom the previous method.
Next, the MTF in noisy environments mN (fm ) is calculated from Eq. (13) by using the estimated SNR of the noisy
reverberant signal. Generally, calculating the STI of the proposed method can be done in the same way as with the previous method. However, MTFs in noisy reverberant environments multiply MTFs in seven octave-bands mRk (fm ), k =1, 2, · · · , 7 by mN (fm ).
Finally, the algorithm described in Section 2 is used to calculate STI from the estimated MTFs.

m(fm , TR , b, SNR) = mR (fm , TR , b) · mN (fm , SNR)
(12)
Here, the MTF in the reverberant environment,mR (fm , TR , b), is defined in Eq. (9), and the MTF in
the noisy environment, mN (fm , SNR), is defined as
5. EvaluationsmN (fm , SNR) =e2x
e2x + e2n

=

1
SNR

1 + 10− 10(13)Therefore, the MTF in the noisy reverberant environmentm(fm ) is defined asm(fm , TR , b, SNR)
2 − (2b−1)



2
TR
1
=
1 + 2πfm
SNR
13.8
1 + 10− 10(14)The previous method estimated STI by estimating the MTF
in reverberant environments (Eq. (9)). The previous method
was used in noisy reverberant environments, so estimation errors were caused by the effect of the MTF in noisy environments (Eq. (14)).
Figure 3 shows a block diagram of the proposed method.
Power envelopes of observed signals e2y (t) are calculated
from the observed signals y(t) as2ê2y (t) = LPF |y(t) + j · Hilbert(y(t))|(15)
where Hilbert(·) is the Hilbert transform and LPF[·] is a lowpass filter with a cut-off frequency of 20 Hz.
Speech sections and noise sections of the observed signals
were estimated by using the robust voice activity detection(VAD) in noisy reverberant environments [5]. The VAD algorithm consists of three blocks. The first block is an estimationof the SNR which is used to mitigate the additive noise effect

Journal of Signal Processing, Vol. 18, No. 4, July 2014

Simulations were carried out to confirm whether the proposed method could estimate STI in noisy reverberant environments. An AM-noise signal was used in these simulations.
This signal was designed to have periodic information in the
power envelope. The period in the power envelope was set tobe 0.2 s so that the fundamental modulation frequency was
5 Hz. We used 43 realistic RIRs in these simulations, whichwere produced in the SMILE2004 datasets [6] (these RIRs’
information was shown in [3]) and two types of white noise(SNR = 20 dB and 5 dB).
Figures 4 and 5 plot the estimated STIs from noisy reverberant AM-noise signals. The horizontal axis indicates STIs
directly calculated from RIRs, and the vertical axis indicatesestimated STIs. The symbols “·” and “◦” correspond to the
STIs estimated with the previous and proposed methods. The
numbers in Figs. 4 and 5 correspond to the results for the
43 realistic RIRs, where the numbers indicate over- or underestimates of STIs by 0.05. The dashed line in the figure indicates the optimal estimated values for STIs.
In cases where SNR = 20 dB (Fig. 4), the RMS error withthe proposed method was 0.04, while it was 0.05 with theprevious method. This means that all STIs should be on thisline if the method can be used to accurately estimate them. In
cases where SNR = 5 dB (Fig. 5), the RMS error with theproposed method was 0.05, while it was 0.11 with the previous method. The results for SNR = 5 dB indicate that the
proposed method could be used to accurately estimate STIs
from the observed noisy reverberant AM-noise signals.

203

Noisyreverberant
signaly(t)Powerenvelope
extraction#1

Estimated
RIR

Power
envelope
subtraction

MTF
estimation

RIR
estimation

#2

^h(t)

T^R, ^b

250 HzMTF^
h1(t) mR1(fi)MTF
m1(fi)MTF^h2(t) mR2(fi)MTF
m2(fi)MTF^h7(t) mR7(fi)MTF
m7(fi)STI
calc.

#3 500 Hz#4 1 kHz#5 2 kHz#6 4 kHz

Speechsections (SSs)
Non-speech
sections (NSs)
RobustVAD

125 Hz#7

8 kHz

Octave-band
filterbank

SNR
estimation

Estimated
STI

MTF
mN(fi)

Estimated SNR

Figure 3: Block diagram of proposed method
6. Conclusion

Acknowledgments

1This work was supported by the Strategic Information
and Communications R&D Promotion Programme (SCOPE;
131205001) of the Ministry of Internal Affairs and Communications (MIC), Japan.
References

33

0.45
34
34

0.3

[6] Architectural Institute of Japan: Sound library of architectureand environment, Gihodo Shuppan Co., Ltd., Tokyo, 2004.

204

4

0.45

0.6

Calculated STI

0.75

1

Figure 4: STI estimated from noisy reverberant AM signals(SNR = 20 dB)
1Previous method
Proposed method

0.75

38 8727
24
28
18116230
259
643
21
23
20
41
42
31
17
35
18
38
23
15 20
15
12 44
14
12
32
39
34 39

0.6
0.45
0.3

[4] M. Unoki, Y. Yamasaki and M. Akagi.: MTF-based powerenvelope restoration in noisy reverberant environments, Proc.
EUSIPCO2009, pp. 228–232, 2009.
[5] S. Morita, M. Unoki and M. Akagi: Study on robust voice activity detection in noisy reverberant environments, 2013 Autumn Meeting Acoustical Society of Japan, 1-P-22b, 2013.

3
14
1439

7
87

0.3

0
0

Estimated STI[3] M. Unoki, K. Sasaki, R. Miyauchi, M. Akagi and N. S. Kim:
Blind method of estimating speech transmission index fromreverberant speech signals, Proc. EUSIPCO2013, 2013.

0.6

18
40
19 38

RMS (Previous) = 0.05
RMS (Proposed) = 0.04

[1] T. Houtgast, H. J. M. Steeneken and R. Plomp: Predicting
speech intelligibility in rooms from the modulation transfer
function I. General room acoustics, Acustica, Vol. 46, pp. 60–
72, 1980.
[2] IEC 60268-16:2003. Sound system equipment - Part 16: Objective Rating of Speech Intelligibility by Speech Transmission Index.

Previous method
Proposed method

0.75

Estimated STI

In this paper, we proposed a method for blindly estimating
STIs in noisy reverberant environments based on the MTF
concept. The proposed method resolved the problem of theprevious method over- or under-estimating the STI by simultaneously estimating the inverse MTF in noisy and reverberant environments. Results from simulations revealed that the
proposed approach could be used to accurately estimate these
STIs in noisy reverberant environments.

263736

RMS (Previous) = 0.11
RMS (Proposed) = 0.05
0
0

0.3

0.45

0.6

Calculated STI

0.75

1

Figure 5: STI estimated from noisy reverberant AM signals(SNR = 5 dB)

Journal of Signal Processing, Vol. 18, No. 4, July 2014