Error detection/correction and fault detection/recovery – Pulse or data error handling – Digital data error correction
Reexamination Certificate
2000-11-29
2003-05-20
Decady, Albert (Department: 2133)
Error detection/correction and fault detection/recovery
Pulse or data error handling
Digital data error correction
C714S701000, C704S230000
Reexamination Certificate
active
06567949
ABSTRACT:
BACKGROUND OF THE INVENTION
Field of the Invention
The invention relates to a method and to configurations for error masking in the transmission and/or storage of digital data, in particular in the case of application of source coding (for example speech coding using the Code Excited Linear Predictive-CELP principle). Within the scope of the invention, transmission (sending and/or receiving) is also understood as the transfer of data to/from a storage medium, that is to say also the storage of data.
Source signals or source information such as speech, sound, image and video signals virtually always include statistical redundance, that is to say redundant information. This redundancy can be greatly reduced by source coding, thus permitting efficient transmission and/or storage of the source signal. This reduction in redundancy removes, before transmission, redundant signal contents which are based on the prior knowledge of, for example, statistics of the signal shape. The bit rate of the source-coded information is also called coding rate or source bit rate. After the transmission, these components are added to the signal again during the source decoding so that no loss of quality can be detected objectively and/or subjectively.
On the other hand, it is customary in signal transmission specifically to make use of channel coding to add redundancy again, in order largely to eliminate the influence of channel faults on transmission. Additional redundant bits permit the receiver and/or decoder to detect errors and possibly also to correct them. The bit rate of the channel-coded information is also called gross bit rate.
In order to be able to transmit as efficiently as possible information, in particular speech data, image data or other useful data, by means of the limited transmission capacities of a transmission medium, in particular of an air interface, this information which is to be transmitted is therefore compressed before transmission by a source coding and protected against channel errors by a channel coding. Different methods are respectively known for these purposes. Thus, for example, in the GSM (Global System for Mobile Communication) speech can be coded by means of a full rate speech codec, a half rate speech codec or an enhanced full rate speech codec.
Within the scope of this application, a method for encoding and/or for corresponding decoding, which can also comprise a source and/or channel coding, is also denoted as a speech codec.
Residual bit errors which cannot be corrected by the channel decoding, occasionally lead to a substantial impairment of the speech reproduction. An additional method for error masking is capable of substantially improving the subjectively perceived reproduction quality.
By way of example, in the case of a frame extinction in the GSM, the last correctly received speech frame is repeated instead of the instantaneous one. After five successive faulty frames muting is performed in stages. This method is controlled by binary frame reliability information, the Bad Frame Indicator (BFI).
Soft bit speech decoding can be used for error masking by systematic expansion of the reliability information passed on to the speech decoder by the channel decoder. Methods are known in this case (Tim Fingscheidt, Peter Vary, “Error Concealment by Softbit Speech Decoding”, ITG Fachbericht No. 139 “Sprachkommunikation” [“Speech communication”], pages 7-10, Frankfurt a.M., 1996), in which methods of decision and estimation theory are used and are briefly explained below.
The signal-to-noise power ratio (SNR) between transmitted and received parameter values proves to be an informative quality criterion for the majority of the speech parameters determined, for example, by an CELP speech codec. Subjectively perceived speech quality and parameter SNR are thereby well correlated as a rule.
For this reason, a parameter decoder (for example a speech coder based on CELP) is sensible; it maximizes this SNR between a transmitted parameter (which is not, however, limited to speech parameters) X and the appropriately decoded parameter {circumflex over (X)} on average, or minimizes their quadratic difference, that is to say
X
^
=
arg
min
E
x
~
{
(
X
-
X
~
)
2
}
(
1.1
)
In order to transmit the continuous-value parameter X, it is first necessary to undertake coding by means of the bit sequence {x
1
, . . . x
W
}. This is performed by quantization, as a rule. For this purpose, the entire value range of the parameter X is decomposed into 2
W
intervals (or cells in the case of vector quantization) S
i
. Each of these cells is respectively assigned a unique transmit bit sequence x
i
={x
1
(i), . . . x
W
(i)}.
A channel with a binary input x and continuous-value output z is now adopted as a transmission channel. In this case, the values z observed at the output are a function of the input values x and a random process which is not initially specified in more detail. All that is presupposed for interference which acts on successive bit sequences and/or parameters is statistical independence. This channel can be completely described by the likelihood function p
z|x
(z
1
, . . . z
W
|X
1
, . . . X
W
).
The expectation in (1.1) is therefore determined by two random processes: by the parameter-generating process X and the observed received values z, that is to say
E
X
,
{
z1
,
…
,
zW
}
{
[
X
-
X
~
(
z
1
,
,
z
W
)
]
2
}
=
∫
z
1
,
…
,
z
W
∫
X
[
X
-
X
~
(
z
1
,
…
,
z
W
)
]
2
p
x
,
z
1
,
…
,
z
W
(
X
,
z
1
,
…
,
z
W
)
ⅆ
X
ⅆ
z
1
…
ⅆ
z
W
.
(
1.2
)
Since the integrand is always positive, the expectation can be minimized by minimizing the inner integral with respect to {tilde over (X)} for each possible reception sequence {z
1
, . . . ,z
W
}. The result is the formula of the Mean Square (MS) estimator
X
~
opt
=
∫
X
X
·
p
x
|
z
1
,
…
,
z
W
(
X
|
z
1
,
…
,
z
W
)
ⅆ
X
(
1.3
)
Taking account of the quantization at the transmitting end, this further yields
X
~
opt
=
∑
1
∫
X
∈
S
i
X
·
p
z
1
,
…
,
z
W
|
x
(
z
1
,
…
,
z
W
|
x
)
·
p
x
(
X
)
p
z
1
,
…
,
z
W
(
z
1
,
…
,
z
W
)
ⅆ
X
.
(
1.4
)
Since the same bit sequence x
i
is transmitted for all X&egr;Si, the conditional probability in the numerator is a constant with respect to the integration, and it follows that
X
~
opt
=
∑
i
p
z
1
,
…
,
z
W
|
x
(
z
1
,
…
,
z
W
|
x
i
)
p
z
1
,
…
,
z
W
(
z
1
,
…
,
z
W
)
∫
X
∈
S
i
X
·
p
x
(
X
)
ⅆ
X
=
∑
i
p
z
1
,
…
,
z
W
|
x
(
z
1
,
…
,
z
W
|
x
i
)
p
z
1
,
…
,
z
W
(
z
1
,
…
,
z
W
)
·
E
(
X
|
x
i
)
·
Pr
(
x
i
)
=
∑
i
E
(
X
|
x
i
)
·
Pr
(
x
i
|
z
1
,
…
,
z
W
)
(
1.5
)
If the parameter-generating process X is not devoid of memory, there are additional statistical connections between successive parameter values and/or bit sequences x.
An analogous derivation then follows taking account of the time index n
X
~
opt
(
n
)
=
∑
i
E
(
X
|
x
i
)
·
Pr
(
x
i
|
z
1
(
n
)
,
…
,
z
W
(
n
)
,
…
,
z
1
(
0
)
,
…
,
z
W
(
0
)
)
.
(
1.6
)
It is shown below how the a posteriori probabilities can be determined in (1.5) and (1.6):
It may be assumed by way of simplification that the transmission channel located between a source coder and a source decoder and comprising a channel c
Heinen Stefan
Xu Wen
De'cady Albert
Dooley Matthew C.
Greenberg Laurence A.
Locher Ralph E.
Siemens Aktiengesellschaft
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