Electrical audio signal processing systems and devices – Acoustical noise or sound cancellation – Counterwave generation control path
Reexamination Certificate
1998-05-21
2003-11-18
Mei, Xu (Department: 2644)
Electrical audio signal processing systems and devices
Acoustical noise or sound cancellation
Counterwave generation control path
C381S071110, C381S071400, C708S322000
Reexamination Certificate
active
06650756
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates to a method and an apparatus for characterizing an audio transmitting system and also to a method and an apparatus for setting the characteristics of an audio filter. More particularly, the invention relates to a method and an apparatus for characterizing an audio transmitting system and also to a method and an apparatus for setting the characteristics of an audio filter, both of which are required for an equalization system that simulates an ideal acoustic space in a vehicle cabin.
2. Description of the Related Art
Generally, the propagation characteristics of sound which is emitted from a speaker installed inside a vehicle and heard are very complicated. This is one of the major factors that deteriorate the reproduced sound of an audio system. In order to overcome the above drawback, an audio system which controls a sound field by using a digital filter has been proposed.
An audio system shown in
FIG. 7
is formed of an audio source
100
having a radio tuner and a CD player, a control filter
102
for controlling the frequency characteristics of audio signals output from the audio source
100
, and a speaker
104
for radiating audio output from the control filter
102
into a vehicle cabin. Meanwhile, a target-response setting unit
106
, in which target response characteristic (impulse response) H is set, receives an audio signal from the audio source
100
and outputs the corresponding target response signal. As used hereinafter, in the specification, the uppercase C, H, W, X, and U each indicate a vector quantity, and the lowercase w, x, and u each represent a scalar quantity. A computation unit
108
calculates an error (difference) between a sound signal output from a microphone
110
installed at an audio-detecting position (listening position) of a vehicle acoustic space and the target response signal output from the target-response setting unit
106
. Thus, a determination of the characteristics of the control filter
102
so as to minimize the error calculated by the computation unit
108
enables the simulation of an ideal acoustic space.
The characteristics of the control filter
102
incorporated in the above-described audio system are determined by the following two steps.
Step 1: Adaptive Characterization of Audio Transmitting System
In the system illustrated in
FIG. 8
, the characteristics of a finite impulse response (FIR) adaptive filter
112
are first determined so as to be comparable to a vehicle cabin acoustic system C, thereby characterizing the audio transmitting system. Generally, as the adaptive algorithm for determining the characteristics of the FIR adaptive filter
112
, a least mean squares (LMS) algorithm is used. According to the LMS algorithm, a vector W of each tap coefficient of the adaptive filter
112
is updated each sampling time based on the following equation:
W
(
n
+1)=
W
(
n
)+&mgr;
1
X
(
n
)&egr;(
n
) (1)
where W(n) indicates a vector of a tap coefficient with respect to a time n; and X(n) represents a vector of a reference signal input into the system shown in FIG.
8
. The factors W(n) and X(n) are further expressed by the following equations:
W
(
n
)=[
W
(
n
,0),
w
(
n
,1), . . . ,
w
(
n,L
−1)]
X
(
n
)=[
x
(
n
),
x
(
n
−1), . . . ,
x
(
n−L
+1)]
where L indicates the number of taps of the adaptive filter
112
. Moreover, in equation (1), &mgr;
1
indicates a step size parameter, and &egr;(n) designates an error signal. The error signal represents the difference between a detection signal d′ output from the microphone and an output of the adaptive filter
112
. The above-described updating operation is repeated until the factor &egr;(n) becomes equal to or smaller than a predetermined value.
Step 2: Adaptive Design of Control Filter
Subsequently, the system illustrated in
FIG. 9
is constructed by using the characteristics of the audio transmitting system determined by the foregoing step 1. The characteristics of a FIR adaptive filter
114
are then determined. Design of the control filter shown in
FIG. 7
is thus performed. Referring to
FIG. 9
, a filter
116
is formed by fixing each tap coefficient of the adaptive filter
112
determined by the foregoing step 1 and is provided with characteristics corresponding to the vehicle cabin acoustics. The target-response setting unit
106
illustrated in
FIG. 9
is the same as the one used in the audio system shown in
FIG. 7
, and ideal target response characteristics are set in the target-response setting unit
106
.
A filtered-x LMS algorithm is generally employed, as the adaptive algorithm that determines the characteristics of the adaptive filter
114
. According to the filtered-x LMS algorithm, a vector W of each tap coefficient of the adaptive filter
114
is updated each sampling time based on the following equation:
W
(
n
+1)=
W
(
n
)+&mgr;
2
U
(
n
)
e
(
n
) (2)
where W(n) represents a vector of a tap coefficient with respect to a time n; &mgr;
2
indicates a step size parameter; and U(n) designates a vector of a reference signal output from the filter
116
with respect to a time n. U(n) is further expressed by the following equation:
U
(
n
)=[
u
(
n
),
u
(
n
−1), . . . ,
u
(
n−L
+1)]
where L indicates the number of taps of the adaptive filter
114
. Moreover, e(n) indicates an error signal representing the difference between a detection signal d′(n) output from the microphone and a target response signal d(n) output from the target-response setting unit
106
. The above-described updating operation is repeated until the factor e(n) is equal to or smaller than a predetermined value.
The control filter
102
shown in
FIG. 7
is designed by fixing the individual tap coefficients of the adaptive filter
114
which are determined by the foregoing two steps.
In this manner, the systems illustrated in
FIGS. 8 and 9
including the adaptive filters
112
and
114
, respectively, are constructed, and the individual tap coefficients are set by using predetermined algorithms, thereby enabling the design of the control filter
102
. It is necessary, however, that the adaptive filters
112
and
114
be constantly and stably operated in order to design the control filter
102
as described above.
For stably operating the adaptive filter
112
in the system illustrated in
FIG. 8
, the step size parameter &mgr;
1
contained in equation (1) should satisfy the condition indicated by the following expression:
0<&mgr;
1
,<(2/(
LE[x
(
n
)
2
])) (3)
wherein L indicates the number of taps of the adaptive filter
112
; E[ ] represents an expectation operator; and x(n) indicates a momentary value of the reference signal with respect to a time n.
Similarly, for stably operating the adaptive filter
114
in the system illustrated in
FIG. 9
, the step size parameter &mgr;
2
included in equation (2) should satisfy the condition represented by the following expression:
0<&mgr;
2
<(2
/LE[u
(
n
)
2
]) (4)
where L indicates the number of taps of the adaptive filter
114
; and u(n) indicates a momentary value of the reference signal with respect to a time n.
In a typical audio system, such as the one shown in
FIG. 7
, it is vital that the control filter
102
be stably operated. Accordingly, the adaptive filters
112
and
114
used for designing the control filter
102
should be operated stably. Thus, the step size parameters &mgr;
1
and &mgr;
2
are required to satisfy the conditions represented by expressions (3) and (4). The factors E[x(n)
2
] and E[u(n)
2
] calculated by using expectation operators are contained in expressions (3) and (4), respectively. Thus, in order to reliably satisfy the above conditions (expressions (3) and (4)), the step size parameters &mgr;
1
and &mgr;
2
should be set by sequentially measuring the power of the reference signals x(n) and u(n)
Ise Tomohiko
Saito Nozomu
Alpine Electronics Inc.
Brinks Hofer Gilson & Lione
Mei Xu
LandOfFree
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