JPH03278620A - Noise reduction circuit - Google Patents

Abstract

PURPOSE:To expand a noise control range and to apply noise control over a wide reception input range by using fuzzy control so as to apply feedback control. CONSTITUTION:A fuzzy control means 14 applies fuzzy deduction processing according to a fuzzy production rule given by a membership function as to each signal according to the input condition of a reception electric field strength from an input level detection circuit 12 and a noise level detection value from an output noise level detection circuit 13. That is, a fuzzy control signal generating circuit 15 generates a fuzzy control signal based on a noise level detection detected by the output noise level detection circuit 13 and a final deduction result generated from the fuzzy deduction means 14, controls a controlled variable of the noise reduction means 11 to control the noise reduction. Thus, the fuzzy control is used for a feedback loop of the noise reduction circuit 11 to reproduce a voice signal with high quality.

Description

DETAILED DESCRIPTION OF THE INVENTION [Field of Industrial Application] The present invention relates to a noise reduction circuit that reduces various noises contained in a received input using fuzzy control.

[Prior Art] There are various types of noise reduction circuits that reduce various types of noise contained in received input, and as shown in FIG. 6, there is a noise reduction circuit that uses feedback control.

In FIG. 6, 20 is an overall noise reduction circuit that performs processing to reduce various noises included in reception inputs such as AM and FM broadcasting, and internally includes a noise reduction means 21, an output noise detection circuit 22, and a noise control signal. A generating circuit 23 is provided.

The noise reduction means 21 includes, for example, a frequency characteristic control circuit, a separation control circuit, a muting control circuit, etc., or a combination thereof, and operates each of these circuits to reduce the noise contained in the receiving input. Performs processing to reduce various types of noise.

The output noise detection circuit 22 detects the noise level included in the output signal after the noise reduction processing by the noise reduction means 21, and generates a noise level detection signal.

The noise control signal generation circuit 23 compares the noise level detection signal generated by the output noise detection generation circuit 22 with a fixed reference voltage Vs, generates a noise control signal proportional to the difference, and outputs the noise control signal to the noise reduction means 21. By performing such feedback control, noise generated at the output side of the receiver can be effectively removed and a high quality audio signal can be reproduced.

[Problem to be solved by the invention]

As described above, a conventional receiver noise reduction circuit using feedback control generates an operation control signal for feedback control by comparing the noise level detection value on the output side with a fixed reference voltage.

When noise is controlled by such feedback control, the noise level is maintained at the level of the reference voltage Vs as shown in FIG. 7 within the range of the control limit value of the noise reduction circuit, so the noise is effectively reduced. Is possible.

However, when the control limit range of this noise reduction circuit is exceeded, noise control becomes impossible and the noise level rapidly increases as shown in the figure. For this reason, the conventional noise reduction circuit using feedback control has a problem in that when the range of the control limit value of the noise reduction circuit is narrow, the noise control range seen from the received input level becomes narrow.

Particularly in FM receivers, noise reduction circuits such as frequency characteristic control circuits, separation control circuits, muting control circuits, etc. have narrow control limit ranges (for example, approximately 20 dB for separation control circuits, and approximately 20 dB for frequency characteristic control circuits). (several dB), the noise control range seen from the reception input level becomes narrower, and the noise control characteristics become steeper.

When the noise control range becomes narrow and the noise control characteristics become steep,
In a reception environment where there are large fluctuations in the number of receivers, such as a receiver installed in a car, the noise reduction circuit does not work well, causing large fluctuations in the noise level and making it impossible to receive good reception. .

[Object of the Invention] The present invention has been made to solve the above-mentioned problems, and it is possible to expand the noise control range by performing feedback control using fuzzy control, and to perform noise control over a wide reception input range. The purpose of the present invention is to provide an improved noise reduction circuit that can perform the following steps.

〔overview〕

Regarding a noise reduction circuit that uses fuzzy control to reduce various noises included in a reception input, there is provided a noise reduction means for controlling the reduction of noise components in the reception input, and an input level detection unit for detecting a received electric field strength value of the reception input. an output noise level detection circuit that detects a noise level included in the output signal after noise reduction processing by the noise reduction means;
A fuzzy inference method uses the received electric field strength value detected by the input level detection circuit and the noise level detection value detected by the output noise level detection circuit as input conditions, and performs fuzzy inference using a fuzzy production rule given by a membership function for each detection value. The present invention is characterized by providing an inference means and a fuzzy control signal generation circuit that controls the noise reduction operation performed by the noise reduction means based on the inference result of the fuzzy inference means, thereby expanding the noise control range of the noise reduction circuit. However, noise control is performed over a wide reception input range, allowing for high-quality reception.

[Means to solve the problem]

The means adopted by the present invention to solve the above-mentioned problems are as follows:
This will be explained with reference to FIG. FIG. 1 is a block diagram showing the basic configuration of the present invention.

In FIG. 1, 10 is the entire noise reduction circuit. Reference numeral 11 denotes a noise reduction means, which is composed of, for example, a frequency characteristic control circuit, a separation control circuit, a muting control circuit, etc., or a combination thereof. Perform processing to reduce noise.

Reference numeral 12 denotes an input level detection circuit, which performs a process of detecting a received field strength value E of received input.

Reference numeral 13 denotes an output noise level detection circuit, which detects the noise level included in the output signal after the noise reduction processing by the noise reduction means 11 and outputs a noise level detection signal.

14 is a fuzzy control means, and input level detection circuit 1
Using the received field strength value E from 2 and the noise signal from the output noise level detection circuit as input conditions, fuzzy inference is performed using fuzzy production rules given by membership functions for each of the signals.

Reference numeral 15 denotes a fuzzy control signal generation circuit, which generates a fuzzy control signal for controlling the operation of the noise reduction circuit 10 based on the noise level detection value detected by the output noise level detection circuit 13 and the inference result of the fuzzy inference means 14. I do.

[Effect]

The operation of the present invention will be explained with reference to FIG.

FIG. 2 illustrates an example of fuzzy inference processing used in the present invention.

The input level detection circuit 12 detects the received field strength value E of the received input and sends it to the fuzzy inference means 14.

The output noise level detection circuit 13 includes the noise reduction means 11
The noise level included in the output signal after the noise reduction processing is detected and sent to the fuzzy inference means 14.

The fuzzy control means 14 uses the received electric field strength value E from the input level detection circuit I2 and the noise level detection value N from the output noise level detection circuit as input conditions, and uses a fuzzy production rule given by a membership function for each of the signals. Accordingly, fuzzy inference processing as shown in FIG. 2 is performed.

As fuzzy production rules in this case, for example, the following fuzzy production rules R1 and R2 are used.

(1) Fuzzy production rule R1 ■ Preconditions (
IF): aI: Received electric field level is high.

bl: Low noise level.

■Result (consequent part, THEN): Decrease the control amount (for example, control time constant).

(2) Production Rule R2 ■Prerequisites (
IF): B2: Received electric field level is low.

b2: The noise level is high.

■Result (consequent part, THEN): Increase the control amount (for example, control time constant).

In FIG. 2, m A + and mB are respective membership functions regarding the antecedent part of the fuzzy production rule R1 for the received electric field strength value E and the noise level detection value N, which are input hectoral elements. mAz and m
B2 is each membership function regarding the antecedent part of the fuzzy production rule R2 regarding the received field strength value E and the detected noise level value N, which are input conditions.

Furthermore, mP and mPz are membership functions for the results (consequent parts) of the fuzzy production rules R1 and R2.

Each of these membership functions is set in advance theoretically or experimentally for each fuzzy production rule for the received electric field strength value E and noise level detection value N, which are input conditions, but the contents are not determined by actual implementation. This is explained in the example section.

The fuzzy inference means 14 calculates the received electric field strength value E and the noise level detection value N, which are each observed input condition.
By performing fuzzy inference based on each membership function of the antecedent part of the corresponding fuzzy production rules R1 and R2, first, as shown in FIGS. 2(c) and (f), each membership function mP and Inference results for m P z (each membership function mP+ and m
(the shaded area in Pz) is determined.

Next, the fuzzy inference means 14 calculates a membership function mPo as shown in FIG. The final inference result (for example, control time constant T) is obtained by (the details of the above-mentioned membership functions and each inference processing method based thereon will be explained in the embodiment section).

Through each of the above inference processes, the received electric field strength value E is large,
The smaller the noise level detection value N becomes, that is, the better the reception condition becomes, the smaller the value of the final inference result (time constant T) becomes.Conversely, the received electric field strength value E becomes smaller and the noise level detection value N becomes larger. That is, the worse the reception condition is, the larger the value of the final inference result (time constant T) becomes.

The fuzzy control signal generation circuit 15 generates a fuzzy control signal based on the noise level detection value detected by the output noise level detection circuit 13 and the final inference result generated by the fuzzy inference means 14, and quantity (e.g. time constant, control action starting point, control action sensitivity, etc.)
to control its noise reduction operation.

As a result, the larger the received electric field strength (17E) is and the smaller the noise level detection value N is, that is, the better the reception condition is, the lower the fuzzy control signal level is, and the noise reduction means 11 operates to reduce noise at a low level. to reproduce high-quality audio signals.

Conversely, the smaller the received field strength value E and the larger the detected noise level value N, that is, the worse the reception condition, the more the fuzzy control signal level increases, and the noise reduction means 11 performs a noise reduction operation when the level is high. This will allow you to play audio signals with less noise.

FIG. 3(a) shows the feeder of the noise reduction circuit 11. 7
This figure shows the noise characteristics of the present invention using fuzzy control for the croup. In addition, FIG. 7 illustrates the noise characteristics of the conventional noise reduction circuit shown in FIG. 7 for reference.

In FIG. 3(a), the horizontal axis shows the amount of decrease in the received electric field strength value E, and the vertical axis shows the noise level N. 2 shows the noise characteristics without fuzzy control, and 2 shows the noise characteristics with fuzzy control.

When performing fuzzy control as in the present invention, the larger the received electric field strength value E and the smaller the detected noise level value N, that is, the better the reception condition, the more fuzzy inference based on the fuzzy production rule R1 becomes the main focus. The level of the final inference result (denoted as F) is lowered, and low-level noise is also effectively removed.

On the other hand, as the received electric field strength value E becomes smaller and the detected noise level value N becomes larger, that is, as the reception condition becomes worse, the main body of the fuzzy inference process changes the inference result (F2) from the fuzzy production rule R1 to the inference based on the fuzzy production rule R2 )'s center of gravity moves. Along with this, the inference result F changes from the inference result F1 to the inference result F2.

As a result, as shown in the figure, the noise characteristics become gentler than those without fuzzy control, and the noise control range of the present invention is, as is clear from the comparison with the figure (g)).
This is significantly increased compared to conventional noise reduction circuits.

By using fuzzy control in the feedback loop of the noise reduction means 11 in this way, it is possible to achieve both a noise reduction effect and reproduction of a good quality audio signal.

That is, since noise reduction is performed by deleting or processing a part of the received signal, there is an antinomic relationship between the noise reduction effect and the quality of the reproduced audio signal.

However, in the present invention, the received electric field strength value E is large;
The smaller the noise level detection value N is, that is, the better the reception condition is, the lower the fuzzy control signal level is, so the less deletion or processing of the received signal by the noise reduction means 11 is performed, and a high quality audio signal is reproduced. be able to.

On the other hand, the smaller the received field strength value E and the larger the detected noise level value N, the worse the reception condition (I see, the fuzzy control signal level increases and the noise reduction operation of the noise reduction means 11 increases, When the reception condition is poor, the noise reduction means 11 effectively removes even high-level noise, making it possible to prevent quality deterioration of the reproduced audio signal and malfunctions due to noise.

As described above, the present invention performs fuzzy control on the noise reduction operation of the noise reduction circuit in the receiver, so it is possible to increase the noise control range and perform good noise reduction over a wide receiving input range. It became.

Furthermore, it is possible to skillfully achieve both improvement in the noise reduction effect and reproduction of audio signals of good quality.

Next, the stability of the noise reduction circuit using fuzzy control according to the present invention will be explained.

In FIG. 1, the noise reduction means 11, the output noise level detection circuit 13, the fuzzy inference means 14, and the fuzzy control signal generation circuit 15 form a feedback loop.

By the way, in general, a feedback loop is stable when it is a negative feedback loop, and becomes unstable when it becomes a positive feedback loop, causing oscillation. Therefore, the control range of the noise reduction operation is determined from the conditions in which the positive feedback loop does not exist.

In FIG. 1, among the components of the feedback loop, all circuits other than the fuzzy inference means 14 have a directionality of output relative to the input of the circuit operation that is uniquely defined, and the stability conditions thereof are well known. There is.

Therefore, in general terms, when the feedback loop by each other circuit is stable, the fuzzy inference means 14
If the fuzzy inference process does not change the direction between the input conditions and the final inference result, the entire feedback loop can be said to be stable.

Now, the fuzzy production rule R in Figure 2 mentioned above
Consider the polarity of control in the case of 1.

Now, if we set C・-controlled amount E...-received electric field strength value N-・-noise level, then C=f (E, N) f・-controlled amount function θC/θE
;θf(S)/θS〈0 θC/θN=θf(N)/θN>O-----1・
・-1---(1).

Similarly, considering the fuzzy production rule R2 mentioned above, θC/θE=〈0 θC/θN;〉0・−・・−・(2
).

From these equations (1) and (2), if each rule is like the fuzzy production rules R1 and R2, the fuzzy inference means 14 determines the control polarity between the input condition and the final inference result by its fuzzy inference processing. It can be seen that there is no change in . Therefore, it is concluded that the entire feedback loop is stable.

〔Example〕

An embodiment of the present invention will be described with reference to FIGS. 2 to 4. FIG. 4 is an explanatory diagram of the configuration of an embodiment of the present invention, and FIG. 2 is also used as an explanatory diagram of fuzzy inference processing of the embodiment. The noise characteristics in FIG. 3 are as described above.

In one embodiment of the invention to be described next, the receiver has F
Assume that it is an M stereo receiver. In addition, the input vector has two elements, the received electric field strength value E and the noise level detection value N, and the fuzzy production rules are two rules, R1 and R2, and the controlled target of the fuzzy control signal is the noise low frequency band. It is assumed that the control time constant that defines the start timing of the control operation of the means 11 is satisfied.

(A) Configuration of Embodiment In FIG. 4, the noise reduction means 11, input level detection circuit 12, output noise level detection circuit 13, fuzzy inference means 14, and fuzzy control signal generation circuit 15 are the same as those explained in FIG. That's right.

16 is an FM demodulation circuit, which demodulates a composite signal of FM stereo signals.

F is an MPX demodulation circuit which demodulates left and right stereo signals and R from a composite signal subjected to noise reduction processing.

In this embodiment, the noise reduction means 11 includes a stereo noise control circuit (hereinafter referred to as an SNC circuit) 111 and a high cut control circuit (hereinafter referred to as an HCC circuit) 112.

The SNC circuit 111 controls the level of the sub-signal component in the composite signal demodulated by the FM demodulation circuit 16.

The HCC circuit 112 extracts the main signal (L + R) components from the composite signal demodulated by the FM demodulation circuit 16 and controls its high frequency characteristics.

The input level detection circuit 12 outputs a received field strength value E obtained by detecting the IF multiplied signal AM of the FM demodulation circuit 16 as a received input level.

In the output noise level detection circuit 13, 131 is a high-pass filter, which extracts high-frequency components (for example, 20 kHz or more) included in the left and right stereo signals output from the MPX demodulation circuit 17 as noise components.

A level detector 132 performs AM detection on the noise component from the high-pass filter 131 and outputs it as a detected noise level value N.

In this embodiment, the fuzzy control signal generation circuit 15 generates a fuzzy control signal, that is, an SNC control signal and an HCC control signal, based on the final inference result generated by the fuzzy inference means 14.
Generates a control signal and connects the SNC circuit 111 and HCC circuit 1
12 respectively. The configuration and operation of this fuzzy control signal generation circuit 15 will be explained in section (c) below.

(B) Fuzzy inference processing The fuzzy control means 14 performs fuzzy inference using the received electric field strength value E from the input level detection circuit 12 and the noise signal from the output noise level detection circuit as input conditions. The fuzzy inference processing performed in this embodiment will be explained with reference to FIG.

In the fuzzy inference processing of this embodiment, the following two fuzzy production rules R1 and R2 and membership functions giving each fuzzy production rule are set.

These fuzzy production rules and membership functions are set theoretically or experimentally so that a good reception condition can be obtained corresponding to the received electric field strength (iE) and the detected noise level value N.

(1) Fuzzy production rule R1 ■ Preconditions (
IF): aI: Received electric field level is high.

bl: Low noise level.

■Result (consequent part, THEN): The level of the time constant T, which is the final inference result, is reduced.

(2) Each membership function of fuzzy production rule R1: ■Membership function mAI of precondition aI: Second
Figure (a) shows the precondition a, that is, the membership function mA that gives the likelihood of the received electric field level.

In the membership function mA, the horizontal axis shows the received electric field strength E, and the vertical axis shows the degree to which the precondition a is applied. Prerequisite a.

The degree value is 1 when it is completely true, and the degree value is O when the precondition a1 is not completely true.
It is. The figure shows that as the received electric field strength E increases,
This shows that the degree to which precondition a is applied increases.

(2) The membership function mB of the precondition b1 (Fig. 2, etc.) shows the membership function mB that gives the precondition b1, that is, the smallness of the detected noise level value N.

In the membership function mB, the horizontal axis indicates the noise level detection value N, and the vertical axis indicates the degree to which the precondition . The degree value is 1 when the prerequisite (i) is completely met, and the degree value is 0 when the prerequisite (i) is not completely met. The figure shows that as the detected noise level value N increases, the degree to which the precondition b1 is satisfied decreases.

■ Membership function mP that gives the result + Figure 2 (c)
represents the membership function mP that provides the inference result, that is, the control time constant T.

In the membership function mP, the horizontal axis is the reference voltage V
s, and the vertical axis indicates the degree to which the results apply. The degree value is 1 when the result is completely applicable, and the degree value is O when the result is not completely applicable.

The figure shows that as the reference voltage Vs becomes larger, the degree to which the results apply becomes smaller.

(3) Fuzzy production rule R2 ■ Preconditions (
IF): a2: Received electric field level is low.

b2: The noise level is high.

■Result (consequent part, THEN): The level of the time constant T, which is the final inference result, is increased.

(4) Each membership function of fuzzy production rule R2: ■Membership function mAz of precondition a2: Second
Figure (d) shows the prerequisite a2, that is, the membership function mA that provides the smallness of the received electric field level.

The content of the membership function mAz is the same as the membership function mA, or the membership function mAz
On the contrary, as the received electric field strength E increases, the degree to which the precondition a2 is satisfied decreases.

■Membership function mBz of precondition b2: 2nd
Figure (e) shows the prerequisite b2, that is, the membership function mB2 that gives the likelihood of the noise level detection value N being large.

The content of the membership function mBz is the same as the membership function mB+, or the membership function mB,
On the contrary, it shows that as the detected noise level value N increases, the degree to which the precondition b2 is satisfied increases.

■Membership function mP that gives the result.

FIG. 4(f) shows the inference result, that is, the membership function mPz that provides the control time constant T.

The content of the membership function mP is the same as the membership function mP, but contrary to the membership function mP, as the control time constant T increases, the degree to which it applies to the results increases ( It shows.

(5) Fuzzy inference processing The fuzzy inference processing performed by the fuzzy inference means 14 is
The explanation will be given according to the inference procedure.

■ The values of the membership functions mA and mAz for the received electric field strength values under the input conditions are determined, and are shown in Figure 2 (a
) and (d), the values of U and u2 are obtained, respectively.

(2) The values of the membership functions mB and mBz for the noise level detection value N in the input conditions are determined, and the values of (2) and v2 are obtained from FIG. 2 0:I) and (e), respectively.

■ First, inference processing is performed using the fuzzy production rule R1.

Observed received electric field strength value E and noise level detection value N
There are various methods for determining the degree to which membership functions mA and mB match fuzzy production rule R1 (soft matching) from the values of each membership function corresponding to . is calculated by the minimum method.

In the minimum method, the minimum value of the degree values that match each observed value of the input condition of each membership function giving the fuzzy production rule is selected as the value of the degree that matches the fuzzy production rule R1 of each membership function.

Membership function mA corresponding to each observed value of the input condition, that is, the received electric field strength value E and the detected noise level value N
The magnitude relationship between the values U and V of , and mB is that u,>v
, so by the minimum method the membership function mA,
vl is selected as the value of the degree to which and mB match the fuzzy production rule R1.

Since the value of the degree of matching with the fuzzy production rule R1 is V, the fuzzy production rule R
Membership function m P that gives an inference result according to 1
+ is obtained by Yager's head-cutting method as shown in Figure 2(c).
As shown by diagonal lines in , it is represented by the region lower than vl.

(2) Next, inference processing is performed using the fuzzy production rule R2.

As in the case of the fuzzy production rule R1, the magnitude relationship between the values u2 and v2 of the membership functions mAz and mB2 corresponding to the observed received electric field strength E and noise level detection value N is u,2<v2. , by the minimum method, u2 is selected as the value of the degree to which the membership functions mAz and mBz match the fuzzy production rule R2.

Since the value of the degree of matching with the fuzzy production rule R2 is u2, the fuzzy production rule R
The membership function mPz that gives the inference result based on U2 is expressed by Yager's truncated method in a region lower than u2, as shown by diagonal lines in FIG. 2(f).

(2) There are various methods for determining the final inference result based on the results of (1) and (2) determined by the fuzzy production rules R1 and R2, but in this embodiment, the centroid method is used.

In the centroid method, first, each membership function mP and mPz (shaded area) shown in Figure 2 (c) and 2) are superimposed using the MAX composition method, and the combined membership functions as shown in Figure 2 (2) are obtained. Synthesize the function mPo.

Then, this composite membership function mP. The horizontal axis coordinate value of the center of gravity of is output as the final inference result, that is, the control time constant T.

Through the above inference processing, the larger the received electric field strength value E and the smaller the detected noise level value N, that is, the better the reception condition, the smaller the value of the final inference result (control time constant T) becomes. The smaller the received electric field strength value E and the larger the detected noise level value N, that is, the worse the reception condition becomes, the larger the value of the final inference result (control time constant T) becomes.

(c) Fuzzy control operation The configuration of the control signal generating circuit 15 of this embodiment and its fuzzy control operation will be explained with reference to FIG.

In FIG. 5, 151 to 153 are comparison paths, which are the control time constant value T of the final inference result from the fuzzy inference means 14.
and a reference voltage.

VS, .about.s3 are reference voltages of the comparators 151 to 153, and the magnitude relationship thereof is set as vs,<Vsz<Vs3.

154 to 156 are switching transistors;
The outputs generated by the corresponding comparators 151 to 153 turn on and conduct.

One ends of capacitors C and -C3 forming a time constant circuit are connected to the collector sides of the switching transistors 154 to 156. The other ends of the capacitors 01 to C3 are commonly connected to a resistor R forming a time constant circuit, and a fuzzy control signal is output from this common connection point. Vc is
This is the power supply voltage for fuzzy control signals.

In this configuration, the comparators 151 to 153 compare the reference voltage Vs, ~Vs= of each comparator with the control time constant value T of the final inference result from the fuzzy inference means 14, and the control time constant value T is the reference voltage. When the voltage is larger than Vs, , Vs2, and Vs3, an output signal is generated, respectively.

When reference voltage Vs>reference voltage Vs2>control time constant value T>reference voltage Vs, switching transistor 1
Only 54 is turned on, and the time constants of the output fuzzy control signal are C and R.

When reference voltage Vs3>control time constant value T>reference voltage Vs2>reference voltage Vs, switching transistor 1
54 and 155 are turned on, and the time constant of the output fuzzy control signal becomes (c, 10C2)R.

When control time constant value T>reference voltage Vs,>reference voltage ■S2>reference voltage Vs, switching transistor 1
54. 155 and 156 are all turned on, and the time constant of the output fuzzy control signal is (c, +C2+C3)R
become.

The fuzzy control signal having the control time constant value obtained as described above is applied to the SNC circuit 111 and the HCC circuit 112 as an SNC control signal and an HCC control signal.

As a result, the larger the received field strength value E and the smaller the detected noise level value N, that is, the better the reception condition, the smaller the value of the control time constant T becomes. Also, each control circuit can respond quickly and maintain good reception conditions.

On the other hand, if the receiving input level fluctuates a lot and reception conditions are poor, if each control circuit operates in response to the fluctuations in the receiving input level, switching between stereo/monaural and frequency characteristics will occur frequently. This in turn increases the discomfort and makes it impossible to receive good reception.

However, in this embodiment, the smaller the received electric field strength value E and the larger the detected noise level value N, that is, the worse the reception condition, the larger the value of the control time constant T, which is the final inference result. , each control circuit no longer follows temporary noise or level fluctuations in the receiving input state, and can maintain a good receiving state without any discomfort. Furthermore, since the time constant circuit shown in FIG. 5 functions as a type of high-pass filter, it is also effective in improving the stability of the feedback loop.

Note that the circuits shown in FIG. 5 may be provided separately corresponding to the SNC circuit 111 and the HCC circuit 112, and their time constants may be controlled individually, but if the SNC control signal and the HCC control signal can be shared, , each control signal can be generated with one circuit.

Although one embodiment of the present invention has been described above, the present invention includes
The control amount of the noise reduction means is not limited to this embodiment, and in addition to the time constant, the control operation start point, control sensitivity, etc. can be controlled.

The noise reduction circuit may be provided at a subsequent stage instead of at the front stage of the MPX demodulation circuit, or may include a muting circuit as a noise reduction means.

Further, the characteristics of each fuzzy production rule and each membership function providing each of these rules are not limited to the characteristics shown in FIG. 2.

In addition to the centroid method explained in the embodiment, as a method for obtaining the final inference result, that is, the motion sensitivity point SE, based on the inference result by each membership function that provides the antecedent part of each fuzzy production rule, the MAX- Various known zero methods such as the MIN method can be used.

[Effects of the Invention] As explained above, according to the present invention, the following effects can be obtained.

(1) Since the noise reduction operation of the noise reduction circuit in the receiver is fuzzy controlled, it is possible to increase the noise control range and perform good noise reduction in a wide receiving input range.

(2) It is possible to both improve the noise reduction effect and reproduce audio signals of good quality.

[Brief explanation of drawings]

Fig. 1 is an explanatory diagram of the basic configuration of the present invention, Fig. 2 is an explanatory diagram of the fuzzy inference processing of the present invention and the embodiment, Fig. 3 is an explanatory diagram of the noise control characteristics of the present invention, and Fig. 4 is an explanatory diagram of the present invention. An explanatory diagram of the configuration of one embodiment, FIG. 5 is an explanatory diagram of the fuzzy control signal generation circuit of the same embodiment, FIG. 6 is an explanatory diagram of the conventional noise reduction circuit, and FIG. 7 is an explanatory diagram of the conventional noise reduction circuit. FIG. 3 is an explanatory diagram of noise control characteristics. 1 and 4, 10... Noise reduction circuit, 11... Noise reduction means, 12... Input level detection circuit, 13... Output noise level detection circuit, 14... Fuzzy inference means, 15
. . . Fuzzy control signal generation circuit, 16 . . . FM demodulation circuit, 17 . . . MPχ demodulation circuit. Fig. b- “English drawing” Fig. 1 0 Fig. Fig.

Claims (1)

[Scope of Claims] A noise reduction circuit (10) that performs processing to reduce various types of noise contained in a received input includes: (a) noise reduction means (11) that performs control to reduce noise components in the received input; (b) an input level detection circuit (12) that detects the received field strength value of the received input; (c) an output noise level that detects the noise level included in the output signal after noise reduction processing by the noise reduction means (11); The detection circuit (13) and (d) the received electric field strength value detected by the input level detection circuit (12) and the noise level detection value detected by the output noise level detection circuit (13) are used as input conditions, and for each of the above detection values. Fuzzy inference means (14) that performs fuzzy inference using fuzzy production rules given by the membership function of (e) Controls the noise reduction operation performed by the noise reduction means (11) based on the inference result of the fuzzy inference means (14). A noise reduction circuit comprising: a fuzzy control signal generation circuit (15).