JPH07160279A - Noise cancel system - Google Patents

Abstract

PURPOSE:To prevent a system from being enlarged in size and increased in manufacturing cost by silencing noise generated in a noise source by a noise canceling processing sections being fewer than the number of sensors. CONSTITUTION:Noise detecting sensors 101a-101c of n pieces (n>m) are provided to detect noise of m pieces of independent noise sources, n pieces of sensor output signals are inputted to conversion filters 102a-102b, m pieces of signals of non-correlation are generated from the conversion filters, m pieces of signals outputted from the conversion filters are respectively processed in adaptive signals as reference signals by m pieces of adaptive signal processing sections 103a-103b, a noise canceling signal in which output signals of m pieces of the adaptive signal processing sections are synthesized is inputted to a loudspeaker 107, and a canceling sound is outputted so that noise at a observing point is canceled.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a noise canceling system, and more particularly to a noise canceling system for canceling noises at observation points generated from a plurality of noise sources.

[0002]

2. Description of the Related Art As a noise countermeasure, a method using a sound absorbing material (passive control) has been conventionally known. However, in the method using the sound absorbing material, it is troublesome to form a quiet area where noise is small, and there is a problem that the bass cannot be effectively eliminated. In particular, in order to prevent noise in the passenger compartment of an automobile, there is a problem that the weight of the automobile increases and the noise cannot be effectively eliminated. For this reason, a method (active control) of radiating a noise canceling sound having a phase opposite to that of the noise from the speaker to reduce the noise has been in the spotlight and is being put to practical use in a part of indoor space such as a factory or an office. Also, a method has been proposed in which noise is reduced by active control even in the passenger compartment of an automobile.

FIG. 4 is a block diagram of a conventional noise canceling device for canceling engine noise. Reference numeral 11 is an engine that is a noise source, 12 is a rotation speed sensor that detects the engine rotation speed R, and 13 is a reference signal generation unit that generates a sine wave signal of a constant amplitude having a frequency according to the engine rotation speed R as a reference signal xn Is. When the noise source is an engine, noise generated by engine rotation has a periodicity, and its frequency depends on the engine speed. For example, in the case of a 4-cylinder engine, the periodic noise generated in the vehicle compartment is dominated by the second harmonic of the engine speed, and the engine speed is 600r.
At pm (10 rps), the frequency of noise generated in the passenger compartment is 20 Hz, and the rotation speed is 6000 rpm (100 rp).
In the case of s), the frequency of the noise generated in the passenger compartment is 200H.
z. The reference signal generation unit 13 stores the sine wave data of the second harmonic in the ROM, reads the data as needed, and outputs the data to generate the reference signal xn. The timing of reading / outputting this data is controlled according to the engine speed R, whereby a reference signal having the frequency of periodic noise generated according to the engine speed R is output.

Reference numeral 14 denotes a noise canceling controller, which receives the reference signal xn generated from the reference signal generating section 13 and outputs noise Sn at a noise canceling position in the passenger compartment (observation point, for example, near the driver's ear). The synthesized sound signal of the cancel sound Sc is input as an error signal en, and adaptive signal processing is performed so that the error signal is minimized, and a noise cancel signal yn is output. The noise cancellation controller 14 filters the adaptive signal processing unit 14a, the adaptive filter 14b having a digital filter configuration, and the reference signal xn by convolving the propagation characteristics (transfer function) of the cancel sound propagation system from the speaker to the noise cancellation point. It has a filtered X signal producing filter 14c for producing an X signal (reference signal for signal processing) rn. 1
5 is a DA converter for converting the output of the adaptive filter (noise cancel signal yn) into an analog noise cancel signal, 16 is a power amplifier for amplifying the noise cancel signal, 17 is a cancel speaker for radiating the noise cancel sound Sc, 18 Is placed at the noise cancellation point, and noise S
An error microphone that detects a synthesized sound of n and the cancel sound Sc and outputs the synthesized sound signal as an error signal en, 19 is an amplifier that amplifies the error signal en, 20 is a low-pass filter that removes aliases, and 21 is a low-pass filter. It is an AD converter that converts the output to digital.

The adaptive signal processing unit 14a receives the error signal en at the noise canceling point and the signal processing reference signal (filtered X signal) rn input through the filter 14c, and uses these signals to cancel the noise canceling point. Adaptive signal processing is performed so as to cancel noise, and the coefficient of the adaptive filter 14b is determined. For example, the adaptive signal processing unit 14a is a well-known filtered X LMS (Least Mean).
Square) According to the adaptive algorithm, the error microphone 18
The coefficient of the adaptive filter 14b is determined so that the error signal en input from (1) is minimized. Adaptive filter 14b
Cancels noise by performing digital filter processing on the reference signal xn according to the coefficient determined by the adaptive signal processing unit 14a and outputting a noise cancel signal yn. still,
The reference signal xn must be a signal having a high correlation with the noise Sn that is desired to be deleted, and a sound that is not correlated with the reference signal is not deleted.

The adaptive filter 14b, as shown in FIG.
For example, delay elements DL and D configured by an FIR digital filter and sequentially delaying an input signal by one sampling time
L ... and the coefficient w ₁ (n), w ₂ (n), w for each delay element output
₃ (n) ... W _N (n) multiplication units ML, ML, ...
And adder units AD, AD ...
・ It is realized by. That is, the reference signal at the current time n · Ts is xn, and the coefficient of each multiplier at that time is w ₁ (n), w
₂ (n), w ₃ (n) ・ ・ ・ w _N (n), output (noise cancellation signal)
Is set to yn, the adaptive filter 14b executes the calculation of the following equation yn = Σwi (n) · x (n-i + 1) (i = 1 to N) ··· (1) to obtain the noise cancellation signal yn. Output.

Filtered X signal producing filter 14c
6 is composed of FIR type digital filters, for example, delay elements DL, DL ... Which delay the input signal sequentially by one sampling time, and coefficients c ₁ , c ₂ , c for each delay element output. ₃ ... Multipliers ML, ML, which multiply by c _M
.. and addition units AD and AD that sequentially add the outputs of the respective multiplication units
It is realized by. Coefficients _{c 1, c 2, c 3} ··· c M is determined so as to simulate the propagation characteristics of the secondary sound propagation system (system from the speaker to the observation point). If the reference signal at time n · Ts is xn and the output (filtered X signal) is r (n), the filter 14c has the following equation r (n) = Σci · x (n-i + 1) (i = 1 ~ M) ··· (2) is executed and the filtered X signal r (n) is output.

The adaptive signal processing unit 14a has the coefficients w ₁ (n + 1), w ₂ (n + 1) and w ₃ of the adaptive filter 14b at the next time (n + 1) · Ts after one sampling time Ts. (n + 1) ・ ・ ・ w
_N (n + 1) is the coefficient at the current time n · T and the error signal en
And the filtered X signal rn are used to determine by the following equation (coefficient update equation) w _j (n + 1) = w _j (n) + μr (n-j + 1) en (3) (however, j = 1, 2, ... N). (3)
In the formula, (n) is the value at the current sampling time and (n + 1) is 1
The value after the sampling time, (n-1) means the value before one sampling time, (n-2) means the value before two sampling times, .... Further, μ is a constant (step size parameter) of 1 or less that determines the step of updating the coefficient of the adaptive filter, and is set to an appropriate value according to the noise cancellation system.

The above is the case of canceling the higher harmonic component of the engine speed, for example, the second higher harmonic component, but road noise is generated in addition to the engine sound in the automobile.
Road noise is generated when the entire vehicle body vibrates via the route of tire → suspension → suspension support part due to the unevenness of the road surface and propagates into the vehicle interior. In order to cancel such road noise by adaptive signal processing, it is necessary to obtain a reference signal that is coherent with the road noise in the vehicle interior. By the way, road noise does not have periodicity like harmonic noise of engine speed,
Moreover, it is difficult to specify only one noise source.
Therefore, a plurality of acceleration sensors are attached to the suspension supporting portion, and the outputs of the acceleration sensors are respectively used as reference signals for canceling road noise. A noise canceling controller is provided for each reference signal, and each noise canceling controller performs adaptive signal processing using a predetermined reference signal and a synthesized sound signal to output a noise canceling signal, and synthesizes each noise canceling signal to produce a speaker. The noise cancellation sound is generated by inputting into.

FIG. 7 is a block diagram of a conventional noise canceling system for road noise. In the figure, 31a, 31b, 3
1c is a signal x corresponding to road noise by detecting vehicle body vibration
It is an acceleration sensor that generates ₁ n, x ₂ n, and x ₃ n. Road noise causes the entire vehicle body to vibrate via the route of tire → suspension → suspension support due to the unevenness of the road surface,
It is generated by propagating in the passenger compartment. The noise source of such road noise cannot be uniquely identified. For this reason, a plurality of acceleration sensors 31a to 31c are attached to each part of the suspension support part so that the vehicle body vibration is detected as a whole.

Numerals 32a, 32b and 32c are input with the output signals of the acceleration sensor as reference signals x ₁ n, x ₂ n and x ₃ n, respectively, and are adapted to perform noise cancellation processing (adaptive signal processing) according to the LMS adaptation algorithm. It is a signal processing unit (noise canceling controller). Each adaptive signal processing unit 3
2a to 32c have the same configuration and are provided with an adaptive signal processing unit LMS, an adaptive filter ADF, and a filter FXF for producing a filtered X signal similarly to the conventional noise canceling controller (see FIG. 4). Each adaptive signal processing unit LMS has an error signal at the noise canceling point (a synthesized sound signal of noise canceling sound and noise) en and a filter FX.
Reference signal for signal processing output from F (filtered X
(Signals) r ₁ n, r ₂ n, r ₃ n are input, and adaptive signal processing is performed using these signals so as to cancel the noise at the noise canceling point, and the coefficient of the adaptive filter ADF is determined. Each adaptive filter ADF is an adaptive signal processing unit LMS.
The reference signals x ₁ n, x ₂ n, x according to the coefficient determined by
Digital filtering is applied to ₃ n and noise canceling signals y ₁ n, y ₂ n and y ₃ n are output.

Reference numeral 33 is each of the noise cancellation processing units 32a-3.
2c noise cancel signals y ₁ n, y ₂ n, y
It is an adder that synthesizes ₃ n and outputs a noise cancellation signal yn for canceling road noise. Reference numeral 34 is a DA converter (DAC) that converts the noise cancel signal yn into an analog noise cancel signal, 35 is a power amplifier that amplifies the noise cancel signal, and 36 is a cancel speaker that emits the noise cancel sound Sc from the speaker. The output cancellation sound reaches the noise cancellation point via the secondary sound propagation system (cancellation sound propagation system) 37. Three
8 is located at the noise cancellation point (observation point) and the noise Sn
An error microphone for detecting a synthesized sound of the cancel sound Sc and outputting the synthesized sound signal as an error signal en, a microphone amplifier 38 for amplifying the error signal en, and an AD converter 39 for converting the microphone amplifier output into a digital signal ( ADC)
Is.

[0013]

In the adaptive signal processing, the noise cannot be effectively canceled unless the signal so-called coherent reference signal xn correlated with the noise to be reduced (for example, road noise) is generated. In other words, the muffling performance of road noise depends on the output of each acceleration sensor (reference signal).
And noise (road noise) depend on the magnitude of multiple coherence. Therefore, conventionally, the number of acceleration sensors is increased in order to secure the magnitude of multiple coherence. The multiple coherence is a multiple relevance function calculated from the coherence (correlation) between each sensor output and noise. However, as the number of sensors increases, the noise cancellation processing unit is required accordingly, and thus there is a problem that the conventional system causes an increase in size and cost. From the above, it is an object of the present invention to provide a noise canceling system that can effectively muffle road noise and the like by using a noise canceling processing unit that is smaller in number than sensors. Another object of the present invention is to provide a noise canceling system capable of preventing an increase in size and cost of the system by reducing the number of noise canceling processing units than the number of sensors.

[0014]

FIG. 1 is a diagram for explaining the principle of the present invention. Reference numerals 101a to 101c are n (n> m) noise detection sensors, 102a to 102b are conversion filters for inputting n sensor detection signals to generate m uncorrelated signals, and 103a to 103b are conversion filters. M adaptive signal processing units that perform adaptive signal processing using the output m signals as reference signals, 104 is an adder that combines the output signals of the adaptive signal processing units and outputs a noise cancellation signal, and 107 is A noise canceling unit (speaker) that receives a noise canceling signal and outputs a noise canceling sound, 109 is a noise detecting unit that detects a synthesized sound of the noise Sn and the noise canceling sound Sc at the observation point and feeds it back to the adaptive signal processing unit. It is a department.

[0015]

A system having n measured inputs but only m incoherent noise sources can be linearly transformed into a collection of m mutually incoherent Virtual inputs. Therefore, in order to detect noise from m independent noise sources, n (n> m) noise detection sensors 101a to 101c are provided, and the n sensor output signals are input to the conversion filters 102a to 102b, Generating m uncorrelated signals from the conversion filter,
M adaptive signal processing units 103a to 103b using m signals output from the conversion filter as reference signals, respectively.
The adaptive signal processing is performed in step S3, the noise cancel signal obtained by combining the output signals of the m adaptive signal processing units is input to the speaker 107, and a cancel sound is output to cancel the noise at the observation point. In this way, the noise (eg, road noise) generated from the noise sources can be effectively silenced by the noise canceling processing units whose number is smaller than the number of sensors, that is, the number of independent noise sources is equal to that of the noise sources. And cost increase can be prevented.

[0016]

【Example】

(a) Principle of the Present Invention First, the power spectrum of a signal will be briefly described. xi (t) is a time series input signal (the number of inputs is n), hi (t)
Is a time-series weighting function, x (f) = [X ₁ (t), x ₂ (t), ..., x _n (t)] (4) h (t) = [h ₁ (t) , h ₂ (t), ..., H _n (t)] (5). Fourier transform of equations (4) and (5) gives X (f) = [X ₁ (f), X ₂ (f), ..., X _n (f)] (4) ′ H (f) = It is given as [H ₁ (f), H ₂ (f), ..., H _n (f)] (5) ′. Here, the mutual spectral density function Sxx (f) between the input signals is expressed by the following equation.

[0017]

[Equation 1] However, S₁₁, S_{twenty two}・ ・ ・ S_nnIs the power spectrum
And the others represent the cross spectrum. Between each input signal
Without any relevance, the mutual spect
The rams are all zero, and Sxx (f) is a diagonal matrix.

As an actual system, considering the road noise generated in the passenger compartment when the automobile is running, for example, the road noise is generated by vibrating the suspension depending on the condition of the road surface, and the vibration is a noise source. Is transmitted to the passenger compartment. Here, in order to realize a noise cancellation system using an adaptive algorithm such as LMS, it is necessary to obtain a reference signal that is coherent with the noise in the vehicle interior. The noise source of road noise is propagated through the suspension and body into the passenger compartment, but it is difficult to uniquely identify this noise source, and multiple acceleration sensors are attached to each part of the suspension. By evaluating the multiple coherence with noise, the optimum reference signal for the noise cancellation system can be obtained. At this time, the mutual spectrum between the sensor outputs can be expressed by the n × n matrix of the equation (6). Note that n is the number of sensors.

Now, assuming that the number of independent noise sources is m, it is possible to detect all the noise sources if the number of sensors is at least m, but in reality, between the noise sources and the sensor mounting points. The number of sensors is required to be n (n> m) because of having a predetermined transfer characteristic and having a zero point in the transfer characteristic. The cross spectrum of the signal obtained from the sensor having such a structure is represented by an n × n matrix as shown in the equation (6), but there are m noise sources. SMPrice and RJ Bernhard said, "A system with n measured inputs, but only m incoherent noise sources can be represented by a set of m incoherent virtual inputs by linear transformation. (SMPrice and RJ Bernhard. "Virtual
Coherence: A Digital Signal Processing Technique fo
r Incoherent Source Identification; Proceedings of4
th Inter-national Modal Analysis Conference, page
1256-1262, 1986). Therefore, the above system (a system having m independent noise sources and n noise detection sensors) is subjected to a series of processes shown in this document to obtain m virtual coherences (incoherent virtual inputs). Can be asked. In fact, n
Although the virtual coherences are calculated, it is confirmed that the nm virtual coherences become almost 0 and do not affect the output.

Further, m virtual inputs Sii 'affecting the output terminal are obtained by the following equation Sxx' = P ^H SxxP (7),

[Equation 2] It is also described in the above document. In the equation (7), P is a unitary matrix having Sxx eigenvectors as columns, and the virtual input Sii ′ is given as a diagonal element in the equation (8).

From the above, by obtaining the virtual coherence function using Sxx ', it is possible to specify n virtual inputs Sii' affecting the output end. Since (n−m) virtual inputs among the n virtual inputs Sii ′ are almost 0, m noise sources and n sensors
In the case of this system, the number of virtual inputs that affect the output end (observation point in the vehicle interior) can be m. If m virtual inputs are obtained, adaptive signal processing is performed by m adaptive signal processing units using each as a reference signal, and output signals of the respective adaptive signal processing units are combined to create a noise cancellation signal,
The noise cancel signal is input to the speaker and a noise cancel sound is output so as to cancel the noise at the observation point.

(B) Outline of the Present Invention FIG. 1 is a schematic explanatory view of the present invention. First, the input x (t) = [x ₁ (t), x ₂ (t), ... X _n (t)] shown in the equation (4) is Fourier transformed to obtain X (f) = [X ₁ ( f), X ₂ (f), ... X _n (f)] is obtained (step 1). Then, the mutual spectral density function Sxx (f) between the input signals is obtained by the equation (6) (step 2). If the mutual spectral density function Sxx (f) is obtained, (7)
The diagonal cross spectrum Svii is obtained from the equation (step 3). A virtual coherence function is calculated from this diagonalized mutual spectrum Svii to obtain the number m of independent virtual inputs (step 4). The number of independent virtual inputs is the number of virtual inputs Svii that is not zero.

Thereafter, a conversion filter h ₁ for producing m virtual inputs from the actual n inputs (sensor outputs),
Determine h ₂ , ... H _m (step 5). For example, x
As a result of obtaining the virtual coherence for the three inputs ₁ (t), x ₂ (t), and x ₃ (t), it is determined that there are two independent virtual inputs. At this time, 3 of x ₁ (t), x ₂ (t), x ₃ (t)
A conversion filter that calculates _one output y ₁ (t) from the input is h ₁
(The conversion filter may be either FIR type or IIR type, but IIR type is superior for simplifying the structure).
The output y ₁ (t) of the conversion filter h ₁ is Fourier transformed to obtain the power spectrum Y ₁ (f), which is S ₁ . If h ₁ is determined so that this S ₁ becomes equal to the virtual input Sv ₁₁ , x
_This means that the first conversion filter h ₁ for converting the three inputs of ₁ (t), x ₂ (t) and x ₃ (t) into the first virtual input is obtained.
Similarly, if h ₂ is determined so that S ₂ becomes equal to the virtual input Sv ₂₂ , the three inputs x ₁ (t), x ₂ (t), x ₃ (t) are converted into the second virtual input. This means that the second conversion filter h ₂ for doing so has been obtained. The conversion filters h ₁ and h ₂ do not have to be calculated each time in real time, and can be fixed filters determined in advance.

When the conversion filter is obtained as described above, a noise canceling system is constructed as shown in FIG. 3, and m adaptive signal processing units perform adaptive signal processing using each output of each conversion filter as a reference signal. A noise canceling signal is created by synthesizing the output signals of the respective adaptive signal processing units, the noise canceling signal is input to the speaker, and a canceling sound is output so as to cancel the noise at the observation point (step 6). There are various possible methods for obtaining the coefficient of the conversion filter h. The first method is a method in which h is configured by an adaptive filter and an adaptive operation is performed so that Si-Svii → 0, and a coefficient is obtained. The second method uses the well-known linear random search method without using the adaptive operation. This is a method used for identification of a system using a computer, in which the coefficients of the conversion filter are randomly changed to sequentially find Si-Svii, and the coefficient for Si-Svii → 0 is found.

(C) Noise Canceling System of the Present Invention-Configuration FIG. 2 is a block diagram of the noise canceling system of the present invention.
It In the figure, 101a, 101b, 101c are noise sources.
Signal x according to the output noise₁n, x₂n, x₃generate n
Sensor (for example, detecting vehicle body vibration to reduce road noise)
It is an acceleration sensor that generates a corresponding signal). 102
a and 102b are sensor detection signals x₁n, x₂n, x₃enter n
Forced, incoherent virtual entry of the first and second
Force x₁n ', x ₂a conversion filter that outputs n ′, 103a,
103b conversion filters 102a and 102b output signals x
₁n ', x₂n'is input as a reference signal, and LM
Noise cancellation processing according to the S adaptation algorithm (adaptive
It is an adaptive signal processing unit that performs signal processing). Each adaptive signal processing
The processing units 103a to 103b have the same configuration, and
Adaptive signal processing similar to conventional noise cancellation controller
Section LMS, adaptive filter ADF, filtered X signal
The filter FXF for generation is provided. Each adaptive signal processor
LMS is the error signal en at the noise cancellation point
Filter FXF signal processing reference signal (file
Lard X signal) r₁n, r₂n is input, these signals
Use to cancel noise at the noise cancellation point
The adaptive signal processing is performed so that the coefficient of the adaptive filter ADF is
To decide. Each adaptive filter ADF has an adaptive signal processing unit L.
Reference signal x according to the coefficient determined by the MS₁n ', x
₂Noise cancellation signal is obtained by applying digital filtering to n '.
No. y₁n, y₂Output n.

Reference numeral 104 denotes each noise cancellation processing unit 103a.
Noise canceling signals y ₁ n, y output from the ˜103 b
It is an adder that synthesizes ₂ n and outputs a noise cancellation signal yn for canceling noise (for example, road noise). Reference numeral 105 denotes a DA converter (DA converter for converting the noise cancel signal yn into an analog noise cancel signal.
C), 106 is a power amplifier for amplifying the noise canceling signal, 107 is a canceling speaker for radiating the noise canceling sound Sc, and the canceling sound output from the speaker is a secondary sound propagating system (canceling sound propagating system) 108. The noise cancellation point is reached via. An error microphone 109 is arranged at a noise cancellation point (observation point), detects a synthesized sound of the noise Sn and the cancel sound Sc, and outputs a synthesized sound signal as an error signal en, and a microphone 110 amplifies the error signal en. The amplifier 111 is an AD converter (ADC) that converts the output of the microphone amplifier into a digital signal.

Operation The noise of each noise source is detected by the sensors 101a to 101c, and the detection signals x ₁ n, x ₂ n and x ₃ n are input to the conversion filters 102a and 102b, respectively. Conversion filter 102
a and 102b are sensor detection signals x ₁ n, x ₂ n, x
₃ n virtual performs a predetermined filtering process on the input x ₁ n ', x
Create and output ₂ n '. Adaptive signal processing unit 103a, 1
03b performs noise canceling processing (adaptive signal processing) according to the LMS adaptive algorithm by using the output signals (virtual inputs) x ₁ n ′ and x ₂ n ′ of the conversion filters 102 a and 102 b as reference signals, and noise canceling signal y ₁ n, y
Output ₂ n. The adder 104 synthesizes the noise cancellation signals y ₁ n and y ₂ n output from the noise cancellation processing units 103a and 103b and outputs a noise cancellation signal yn for canceling noise (for example, road noise). The DA converter 105 converts the noise canceling signal yn into an analog noise canceling signal, and the power amplifier 106 amplifies the noise canceling signal and causes the speaker 107.
The noise canceling sound Sc is emitted from the speaker.

The cancellation sound output from the speaker is 2
A noise cancellation point (observation point) is reached via the next sound propagation system (cancellation sound propagation system) 108, and noise at the observation point is canceled. The error microphone 109 detects a synthesized sound of the noise Sn and the cancel sound Sc, and feeds back the synthesized sound signal as an error signal en to each adaptive signal processing unit via a microphone amplifier and an AD converter. After that, the above operation is repeated, and finally, a considerable amount of noise at the observation point is canceled. Although the present invention has been described above with reference to the embodiments, the present invention can be variously modified according to the gist of the present invention described in the claims, and the present invention does not exclude these.

[0029]

As described above, according to the present invention, n (n> m) noise detection sensors are provided to detect noise from m independent noise sources, and a conversion filter for converting the n sensor output signals. To m, and generate m uncorrelated signals from the conversion filter. The m signals output from the conversion filter are used as reference signals to perform adaptive signal processing in m adaptive signal processing units. Since the noise canceling signal that synthesizes the output signals of each adaptive signal processing unit is input to the speaker and the noise canceling sound for canceling the noise at the observation point is output, the noise canceling number less than the number of sensors is canceled. The processing unit can effectively muffle noise (for example, road noise) generated from a noise source, and prevent the system from increasing in size and cost. Further, according to the present invention, the correlation between the reference signals is eliminated, so that the convergence speed in the adaptive signal processing is improved and the performance can be improved.

[Brief description of drawings]

FIG. 1 is a diagram illustrating the principle of the present invention.

FIG. 2 is a schematic explanatory view of the present invention.

FIG. 3 is a configuration diagram of a noise canceling system of the present invention.

FIG. 4 is a configuration diagram of a conventional noise canceling device (when canceling periodic noise).

FIG. 5 is a configuration diagram of an adaptive filter.

FIG. 6 is a configuration diagram of a filter for generating a filtered signal.

FIG. 7 is a configuration diagram of a conventional noise canceling system for canceling road noise.

[Explanation of reference signs] 101a to 101c ··· Sensors 102a, 102b · · Conversion filters 103a, 103b · · Adaptive signal processing unit 104 · · Adder 107 · · Speaker 109 · · Error microphone

Continuation of the front page (51) Int.Cl. ⁶ Identification code Office reference number FI technical display location H03H 21/00 8842-5J H04R 3/00 320

Claims (1)

[Claims] 1. A noise canceling system for canceling noise at an observation point generated from m noise sources, wherein n (n> m) noise detection sensors and n sensor detection signals are input and uncorrelated. A conversion filter that creates m signals, m adaptive signal processing units that perform adaptive signal processing using the m signals output from the conversion filters as reference signals, and the output signals of each adaptive signal processing unit are combined. A noise having a canceling sound generation unit that inputs a noise canceling signal and outputs a noise canceling sound, and a noise detecting unit that detects a synthesized sound of the noise at the observation point and the noise canceling sound and feeds it back to the adaptive signal processing unit Cancellation system.