a statistical model-based double-talk detection incorporating soft ...

Far−end Echo
Signal
Near−end Speech
Signal
Microphone Input
Signal
p(H 1
|Y(i))
1
0
−1
1
0
−1
1
0
−1
1.0
0.5
0.0
Noise Far−end echo Double−Talk Near−end Speech Noise
Table 1. ERLE and SA test results obtained from the proposed
DTD algorithm based on a soft decision with those
yielded by the conventional hard decision method during
double-talk.
Environments ERLE (dB) SA (dB)
Noise SNR (dB) Park-based proposed Park-based proposed
10 3.23 2.96 1.75 1.64
White 20 3.46 3.30 1.99 1.86
30 3.51 3.36 2.03 1.90
10 3.36 3.23 1.81 1.71
Babble 20 3.48 3.33 1.20 1.87
30 3.52 3.36 2.03 1.90
10 2.95 2.84 1.56 1.47
Vehicle 20 3.40 3.25 1.96 1.83
30 3.50 3.35 2.02 1.90
Clean speech ∞ 3.52 3.37 2.03 1.91
0 1 2 3 4
Time (sec)
Fig. 2. DTD results for the acoustic echo signal under the
vehicular noise condition (SNR=20 dB).
where
C(i, k) =ζ C C(i − 1,k)+(1− ζ C )|X ∗ (i, k)Y (i, k)| (14)
R(i, k) =ζ R R(i − 1,k)+(1− ζ R )|X ∗ (i, k)X(i, k)|, (15)
and ζ C (= 0.998) and ζ R (= 0.998) are smoothing parameters.
Note that this update iteration achieves the room change
tracking.
3. PROPOSED DOUBLE TALK DETECTION
BASED AES
As noted earlier, the update of the echo path response must
be frozen in the case of the double-talk. For this, we propose
the DTD technique to incorporate the newly derived GNSPP,
p(H 1 |Y(i)), with the help of the VAD results of the near-end
and far-end signal, as shown in Fig. 1. We inherently consider
the near-end speech presence in the case of far-end signal
presence, where the GNSPP substantially determine the
double-talk situation and is used to update the echo path response
based on (12). Note that the VAD has an impact on
the near-end speech presence and the far-end speech presence
only. Specifically, we derive a novel update routine of the
echo path response by utilizing the soft decision as follows:
⎧
⎪⎨
ˇĤ(i, k) =
p(H 1 |Y(i)) ˇĤ(i − 1,k)
+(1 − p(H 1 |Y(i)))Ĥopt(i, k)
, if I(Y(i)) = 1 and I(X(i)) = 1
⎪⎩
Ĥ opt (i, k), otherwise
(16)
where I(·) denotes an indicator function of the VAD result
provided by the IS-127 noise suppression algorithm since it
is known that it gives us a robust performance under various
noise environments [12]. Furthermore, we modified the
VAD algorithm to reduce the false decisions. For example,
I(Y(i)) = 1 if the near-end signal Y(i) exists at the ith frame
and I(Y(i)) = 0 otherwise. Therefore, the update of ˇĤ(i, k)
is finally addressed such that
ˇĤ(i, k) replaces ˇĤ(i − 1,k)
(i.e., no update) within the double-talk regions on each frequency
bin and (12) in the case of single-talk. In particular, in
the case of abrupt transient periods between double-talk and
single-talk, as shown in Fig. 2, the GNSPP could be a soft
value between 0 and 1. This accounts for why the soft decision
scheme is more insensitive to detection error compared
to conventional hard decision methods.
Based on this proposed DTD method, we finally apply it to
the AES algorithm proposed by Faller et al. [10] as follows:
Ŝ(i, k) =G(i, k)Y (i, k) (17)
where the Wiener filter gain G(i, k) is given by [10]
[ max(|Y (i, k)|−| Ŷ (i, k)|, 0)
]
G(i, k) =
. (18)
|Y (i, k)|
4. EXPERIMENTAL RESULTS
In order to verify the performance of the proposed DTD algorithm,
we conducted objective comparison experiments under
various noise conditions. Twenty test phrases, spoken by
seven speakers and sampled at 8 kHz, were used as the experimental
data. For assessing the performance of the proposed
method, we artificially created 20 data files, where
each file was produced by mixing the far-end signal with the
near-end signal. Each frame of the windowed signal was
transformed into its corresponding spectrum through a 128-
point DFT after zero padding. We then constructed 16 frequency
bands through combination of subbands to cover all
frequency ranges (∼4 kHz) of the narrow band speech signal,
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