Active noise reduction system

Abstract

The present invention relates to an active noise reduction system including a reference signal generator configured to generate a reference signal, a counter sound generator configured to generate a counter sound, an error detector configured to detect an error between a noise and the counter sound and generate an error signal corresponding to the error, and a controller configured to control the counter sound generator based on the reference signal and the error signal, wherein the controller is configured to update an estimated value of an acoustic characteristic in an internal space of a moving body based on the reference signal and the error signal, estimate a head position of an occupant in the internal space based on the updated estimated value of the acoustic characteristic, and update a control filter based on the estimated head position of the occupant, the control filter being a filter for controlling the counter sound generator.

Description

Technical Field

[0001] The present invention relates to an active noise reduction system that reduces noise by canceling out sound interference noise that is out of phase with the noise. Background Technology

[0002] Typically, active noise reduction systems reduce noise by canceling out acoustic interference noise that is out of phase with the noise. For example, WO2019 / 106748A1 discloses an active noise reduction system configured to estimate the occupant's head position based on camera images and reduce noise in the estimated head position of the occupant.

[0003] However, WO2019 / 106748A1 requires specialized equipment such as a camera to estimate the occupant's head position. Therefore, the configuration of an active noise cancellation system can become complex, and such systems can become expensive. Summary of the Invention

[0004] In view of the above background, the object of the present invention is to provide a simple and inexpensive active noise cancellation system that can estimate the head position of the occupant and effectively reduce the noise at the estimated head position of the occupant.

[0005] To achieve this objective, one aspect of the present invention provides an active noise cancellation system 11 for reducing noise in the interior space (carriage 5) of a moving body (vehicle 1), the active noise cancellation system comprising: a reference signal generator (vibration sensor 12) configured to generate a reference signal corresponding to the noise; a cancelling sound generator (speaker 13) configured to generate a cancelling sound for cancelling the noise; an error detector (error microphone 14) configured to detect an error between the noise and the cancelling sound and generate an error signal corresponding to the error; and a controller 15 configured to control the cancelling sound generator based on the reference signal and the error signal, wherein the controller is configured to: update an estimate of acoustic characteristics in the interior space based on the reference signal and the error signal; estimate the head position of an occupant in the interior space based on the updated estimate of the acoustic characteristics in the interior space; and update a control filter W based on the estimated head position of the occupant, the control filter being a filter for controlling the cancelling sound generator.

[0006] Based on this, the occupant's head position can be estimated using acoustic characteristic estimates. Therefore, when the occupant's head position changes, the characteristics of the control filter can be modified to follow the change in head position. This effectively reduces noise at the occupant's head position. Furthermore, since no dedicated equipment (e.g., a camera) is required for estimating the occupant's head position, a simple and inexpensive active noise cancellation system can be provided.

[0007] In the foregoing, preferably, the controller is further configured to: generate an estimated signal of the noise at the head position of the occupant and an estimated signal of the canceling sound at the head position of the occupant based on the estimated head position of the occupant; and update the control filter such that the error between the estimated signal of the noise at the head position of the occupant and the estimated signal of the canceling sound at the head position of the occupant is minimized.

[0008] Based on this, the control filter can be updated appropriately based on the estimated head position of the occupant. Therefore, it is possible to more effectively reduce noise at the occupant's head position.

[0009] In the foregoing, preferably, the controller is further configured to: update an estimate of the transmission characteristics of the noise from the noise source to the error detector and an estimate of the transmission characteristics of the canceled sound from the canceled sound generator to the error detector based on the reference signal and the error signal; generate an estimated signal of the noise at the location of the error detector based on the updated estimate of the transmission characteristics of the noise from the noise source to the error detector; and use a predetermined noise correction filter H^ me The estimated signal of the noise at the location of the error detector is corrected to generate an estimated signal of the noise at the head position of the occupant; based on an updated estimate of the transmission characteristics of the canceled sound from the canceled sound generator to the error detector, an estimated signal of the canceled sound at the location of the error detector is generated; and a predetermined cancellation correction filter C^ is used. me The estimated signal of the canceling sound at the location of the error detector is corrected to generate the estimated signal of the canceling sound at the head position of the occupant.

[0010] Based on this aspect, it is possible to appropriately generate estimated signals of noise and cancellation sound at the occupant's head position based on estimated signals of noise and cancellation sound at the error detector's location. That is, noise and cancellation sound at the occupant's head position can be appropriately estimated based on estimated signals of noise and cancellation sound at the error detector's location. Therefore, noise at the occupant's head position can be reduced more effectively.

[0011] In the foregoing, preferably, the cancellation correction filter is defined as the ratio of the transmission characteristics of the cancellation sound from the cancellation sound generator to the head position of the occupant to the transmission characteristics of the cancellation sound from the cancellation sound generator to the error detector, and the noise correction filter is defined as the ratio of the transmission characteristics of the noise from the noise source to the head position of the occupant to the transmission characteristics of the noise from the noise source to the error detector.

[0012] Based on this aspect, the influence of factors other than the transmission characteristics of the canceled sound (e.g., changes in the internal spatial state) on the cancellation correction filter can be minimized. Therefore, the cancellation correction filter can appropriately correct the estimated signal of the canceled sound. Similarly, the influence of factors other than the transmission characteristics of noise (e.g., changes in the internal spatial state) on the noise correction filter can be minimized. Therefore, the noise correction filter can appropriately correct the estimated signal of the noise.

[0013] In the foregoing, preferably, the controller is further configured to store each of a first relationship and a second relationship in a table, the first relationship being the relationship between the occupant's head position and the coefficients of the cancellation correction filter, and the second relationship being the relationship between the occupant's head position and the coefficients of the noise correction filter.

[0014] Based on this, the coefficients of the cancellation correction filter and the noise correction filter can be easily determined according to the occupant's head position.

[0015] In the foregoing, preferably, the controller includes a neural network that has learned the relationship between estimates of the acoustic characteristics in the interior space and the head position of the occupant, and the controller is configured to estimate the head position of the occupant by inputting the estimates of the acoustic characteristics in the interior space into the neural network.

[0016] Based on this, the estimated value based on acoustic characteristics can accurately estimate the occupant's head position.

[0017] Therefore, based on the above aspects, it is possible to provide a simple and inexpensive active noise cancellation system that can estimate the occupant's head position and effectively reduce noise at the estimated head position of the occupant. Attached Figure Description

[0018] Figure 1 This is a schematic diagram showing a vehicle to which the active noise cancellation system according to the first embodiment is applied;

[0019] Figure 2This is a functional block diagram showing the active noise cancellation system according to the first embodiment;

[0020] Figure 3 This is an explanatory diagram illustrating the process by which the head position estimation unit according to the first embodiment estimates the head position of the occupant.

[0021] Figure 4A and Figure 4B Each of these is an explanatory diagram showing the offset correction table according to the first embodiment;

[0022] Figure 5 This is an explanatory diagram showing the definitions of the cancellation correction filter and the noise correction filter according to the first embodiment;

[0023] Figure 6 This is a functional block diagram showing the offset correction unit according to the first embodiment;

[0024] Figure 7 It is a graph showing the effect of reducing road noise;

[0025] Figure 8 This is an explanatory diagram illustrating a method for constructing a neural network according to a first embodiment;

[0026] Figures 9A to 9C This is an explanatory diagram illustrating a first example of a method for constructing an offset correction table according to a first embodiment;

[0027] Figure 10 This is an explanatory diagram illustrating a second example of the method for constructing an offset correction table according to the first embodiment;

[0028] Figure 11 This is a schematic diagram showing a vehicle to which an active noise cancellation system according to a modified example of the first embodiment is applied.

[0029] Figure 12 This is a schematic diagram showing a vehicle to which the active noise cancellation system according to the second embodiment is applied; and

[0030] Figure 13 This is a functional block diagram illustrating the active noise cancellation system according to the second embodiment. Detailed Implementation

[0031] In the following description, embodiments of the invention will be described with reference to the accompanying drawings. In this specification, the “^” (swirl) shown together with the symbols each represents an identified value or an estimated value. The “^” is shown above the symbols in the drawings and formulas, but after the symbols in the text of the specification.

[0032] (First Implementation)

[0033] First, refer to Figures 1 to 11The first embodiment of the present invention is described.

[0034] <Active Noise Cancellation System 11>

[0035] Figure 1 This is a schematic diagram showing a vehicle 1 (an example of a moving body) applying the active noise reduction system 11 (hereinafter referred to as "noise reduction system 11") according to the first embodiment. When the wheels 2 vibrate due to the force received from the road surface S and the vibration of the wheels 2 is transmitted to the vehicle body 4 via the suspension 3, road noise d is generated in the passenger compartment 5 (an example of the interior space of the moving body). The noise reduction system 11 according to the first embodiment is a feedback-controllable active noise control device (ANC device) for reducing such road noise d. More specifically, the noise reduction system 11 reduces road noise d by generating a canceling sound y that is out of phase with the road noise d and causing the generated canceling sound y to interfere with the road noise d.

[0036] See Figure 1 and Figure 2 The noise reduction system 11 includes: a vibration sensor 12 (an example of a reference signal generator) configured to generate a reference signal x corresponding to road noise d; a plurality of loudspeakers 13 (an example of a canceling sound generator) configured to generate a canceling sound y for canceling road noise d; a plurality of error microphones 14 (an example of an error detector) configured to detect an error (synthesized sound) between road noise d and canceling sound y and generate an error signal e corresponding to the detected error; and a controller 15 configured to control the plurality of loudspeakers 13 based on the reference signal x and the error signal e.

[0037] Figure 2 The symbol H in m This represents the transmission characteristics of road noise d from the noise source (in this embodiment, the road surface S) to each error microphone 14 (transmission characteristics of the primary path: an example of acoustic characteristics in the carriage 5). Figure 2 The symbol C in m This represents the transmission characteristics of the canceled sound y from speaker 13 to each error microphone 14 (transmission characteristics of the secondary path: an example of the acoustic characteristics in carriage 5).

[0038] <Vibration Sensor 12>

[0039] Reference Figure 1 The vibration sensor 12 of the noise reduction system 11 is mounted, for example, in at least one suspension 3. The vibration sensor 12 detects the acceleration of the suspension 3 based on the road noise d and generates a reference signal x based on the acceleration of the suspension 3. In another embodiment, the vibration sensor 12 may be mounted in a location outside the suspension 3 of the vehicle 1.

[0040] <Speaker 13>

[0041] See Figure 1 Each speaker 13 of the noise reduction system 11 forms part of, for example, the audio system of the vehicle 1 and is installed in the door of the vehicle 1. In another embodiment, the speaker 13 may be separately located from the audio system of the vehicle 1, or may be installed in a location outside the door of the vehicle 1 (e.g., the speaker 13 may be installed in the headrest 6a of the passenger seat 6 or on the floor below the passenger seat 6).

[0042] <Error Microphone 14>

[0043] Each error microphone 14 of the noise reduction system 11 is installed, for example, in the headrest 6a of the passenger seat 6. In another embodiment, the error microphone 14 may be installed in a location outside the passenger seat 6 of the vehicle 1 (e.g., the error microphone 14 may be installed on the ceiling above the passenger seat 6).

[0044] <Controller 15>

[0045] The controller 15 of the noise reduction system 11 is composed of an electronic control unit (ECU) that includes a processing unit (CPU, MPU, etc.) and a storage device (ROM, RAM, etc.). The controller 15 can be composed of a single piece of hardware or a unit composed of multiple pieces of hardware.

[0046] Reference Figure 2 The controller 15 includes a first A / D conversion unit 21, a control signal output unit 22, a D / A conversion unit 23, a second A / D conversion unit 24, an acoustic characteristic update unit 25, a reference signal correction unit 26, a head position estimation unit 27, an estimation signal correction unit 28, and a control filter update unit 29 as functional components. Figure 2 The symbol "ADA" in the text stands for "Adaptive".

[0047] <First A / D Conversion Unit 21>

[0048] The first A / D conversion unit 21 of the controller 15 converts the analog reference signal x output from the vibration sensor 12 into a digital reference signal x, and outputs the digital reference signal x to the control signal output unit 22, the acoustic characteristic update unit 25, and the reference signal correction unit 26. In the following text, "reference signal x" unless otherwise stated refers to the reference signal x that has passed through the first A / D conversion unit 21.

[0049] <Control Signal Output Unit 22>

[0050] The control signal output unit 22 of the controller 15 includes a control filter W. A finite impulse response filter (FIR filter) or a single-frequency adaptive notch filter (SAN filter) can be used for the control filter W. The control signal output unit 22 generates a control signal u by filtering a reference signal x, and outputs the generated control signal u to the D / A conversion unit 23 and the acoustic characteristic update unit 25.

[0051] <D / A conversion unit 23>

[0052] The D / A conversion unit 23 of the controller 15 converts the digital control signal u output from the control signal output unit 22 into an analog control signal u, and outputs the analog control signal u to the speaker 13. Accordingly, the speaker 13 generates a cancellation sound y according to the control signal u.

[0053] <Second A / D conversion unit 24>

[0054] The second A / D conversion unit 24 of the controller 15 converts the error signal e output from the error microphone 14 from an analog signal into a digital signal, and outputs the converted error signal e to the acoustic characteristic update unit 25. Hereinafter, the "error signal e" not described represents the error signal e that has passed through the second A / D conversion unit 24.

[0055] <Acoustic characteristic update unit 25>

[0056] The acoustic characteristic update unit 25 of the controller 15 updates an estimated value of the acoustic characteristics in the vehicle compartment 5 based on the reference signal x, the control signal u, and the error signal e. The acoustic characteristic update unit 25 includes a cancellation estimation signal generation unit 31, a noise estimation signal generation unit 32, and an adder 33.

[0057] The cancellation estimation signal generation unit 31 includes a secondary path filter unit 35 and a secondary path update unit 36.

[0058] The secondary path filter unit 35 includes a secondary path filter Ĉ. The secondary path filter Ĉ is a filter corresponding to an estimated value of the transfer characteristic C m (Example of an estimated value of acoustic characteristics) of the cancellation sound y from the speaker 13 to the error microphone 14. An FIR filter or a SAN filter can be used for the secondary path filter Ĉ.

[0059] The secondary path filter unit 35 generates a cancellation estimation signal ŷ by filtering the control signal u using the secondary path filter Ĉ m1 . The cancellation estimation signal ŷ m1This is the estimated signal of the canceled sound y at the position of the error microphone 14 (hereinafter referred to as the "microphone position"). The secondary path filter unit 35 converts the generated canceled estimated signal y^ m1 Output to adder 33.

[0060] The secondary path update unit 36 ​​uses an adaptive algorithm, such as the Least Mean Square (LMS) algorithm, to update the coefficients of the secondary path filter C^. More specifically, the secondary path update unit 36 ​​updates the coefficients of the secondary path filter C^ such that the virtual error signal e1 (described later) output from the adder 33 is minimized.

[0061] The noise estimation signal generation unit 32 includes a primary path filter unit 38 and a primary path update unit 39.

[0062] The primary path filter unit 38 includes a primary path filter H^. The primary path filter H^ is related to the transmission characteristics H of the path noise d from the noise source to the error microphone 14. m (Example of acoustic characteristic estimates) The corresponding filter. An FIR filter or SAN filter can be used for the primary path filter H^.

[0063] Primary path filter unit 38 generates noise estimation signal d^ by filtering the reference signal x using primary path filter H^. m Noise estimation signal d^ m This is the estimated noise signal at the microphone location. The primary path filter unit 38 converts the generated noise estimation signal d^ m The output is sent to adder 33 and estimation signal correction unit 28.

[0064] The primary path update unit 39 uses an adaptive algorithm, such as the LMS algorithm, to update the coefficients of the primary path filter H^. More specifically, the primary path update unit 39 updates the coefficients of the primary path filter H^ such that the virtual error signal e1 (described later) output from the adder 33 is minimized.

[0065] Adder 33 converts the error signal e and the cancellation estimation signal y^ m1 And noise estimation signal d^ m The signals are added together to generate a virtual error signal e1. Adder 33 outputs the generated virtual error signal e1 to cancellation estimation signal generation unit 31 and noise estimation signal generation unit 32.

[0066] <Reference Signal Correction Unit 26>

[0067] Similar to the cancellation estimation signal generation unit 31, the reference signal correction unit 26 of the controller 15 includes a secondary path filter C^. When the coefficients of the secondary path filter C^ are updated in the cancellation estimation signal generation unit 31, the updated coefficients of the secondary path filter C^ are output to the reference signal correction unit 26, and the coefficients of the secondary path filter C^ are updated in the reference signal correction unit 26. That is, the coefficients of the secondary path filter C^ set in the reference signal correction unit 26 are not fixed values, but are continuously updated values ​​based on the signal from the cancellation estimation signal generation unit 31.

[0068] Reference signal correction unit 26 generates cancellation estimation signal y^ by filtering the reference signal x. m2 Cancel the estimated signal y^ m2 It cancels out the estimated signal of sound y at the microphone position, similar to canceling out the estimated signal y^. m1 Cancel the estimated signal y^ m2 This includes information about the secondary path filter C^. The reference signal correction unit 26 will use the generated cancellation estimate signal y^ m2 The output is sent to the head position estimation unit 27 and the estimation signal correction unit 28.

[0069] <Head Position Estimation Unit 27>

[0070] The head position estimation unit 27 of controller 15 estimates the head position of the occupants in carriage 5 based on the secondary path filter C^. More specifically, the head position estimation unit 27 estimates the head position of the occupants in carriage 5 based on the coefficients of the secondary path filter C^ copied from the reference signal correction unit 26. The head position estimation unit 27 outputs the estimated head position of the occupants to the estimation signal correction unit 28.

[0071] Reference Figure 3 The head position estimation unit 27 includes a neural network that has learned the relationship between the secondary path filter C^ and the occupant's head position. The neural network consists of n layers (n: an integer). Each layer of the neural network has corresponding coefficients (w1, w2, ..., wn). The method for constructing the neural network will be described later.

[0072] When the FIR filter is used for the secondary path filter C^, the head position estimation unit 27 copies the coefficient sequence (C1, C2, ..., C1) of the secondary path filter C^ corresponding to the impulse response from the reference signal correction unit 26. LThe coefficient sequence of the secondary path filter C^ corresponding to the impulse response can be copied as a graph. The head position estimation unit 27 inputs the copied coefficient sequence of the secondary path filter C^ into the neural network, and then obtains the occupant's head position (position 1, position 2, position 3, ...) from the output of the neural network in response to the input.

[0073] When the SAN filter is used for the secondary path filter C^, the head position estimation unit 27 copies the coefficients C^0, C^1 and the corresponding frequency f of the secondary path filter C^ from the reference signal correction unit 26. The head position estimation unit 27 inputs the copied coefficients C^0, C^1 and the corresponding frequency f of the secondary path filter C^ into the neural network, and then obtains the occupant's head position (position 1, position 2, position 3, ...) from the output of the neural network in response to the input.

[0074] <Estimation Signal Correction Unit 28>

[0075] Reference Figure 2 The estimation signal correction unit 28 of the controller 15 corrects and cancels the estimation signal y^ based on the occupant's head position estimated by the head position estimation unit 27. m2 And noise estimation signal d^ m The estimated signal correction unit 28 includes a database unit 41, a cancellation correction unit 42, and a noise correction unit 43.

[0076] Reference Figure 4A and Figure 4B Database unit 41 stores the offset correction table Tc. The method for constructing the offset correction table Tc will be described later.

[0077] Reference Figure 4A When an FIR filter is used as a secondary path filter C^, the cancellation correction table Tc stores the cancellation correction filter C^ for each head position of the occupant. me The coefficient sequence (described later). Database unit 41 reads the cancellation correction filter C^ by referring to the cancellation correction table Tc based on the occupant's head position estimated by head position estimation unit 27. me The coefficient sequence. Database unit 41 will cancel the correction filter C^ me The coefficient reading sequence is output to the cancellation correction unit 42.

[0078] Reference Figure 4B When the SAN filter is used for the secondary path filter C^, the cancellation correction table Tc stores the cancellation correction filter C^ for each head position of the occupant. me The coefficient table TN. The cancellation correction filter C^ me The coefficient table TN stores the cancellation correction filter C^ for each frequency f.me The coefficients C^0 and C^1 are specified. Database unit 41 determines the coefficient table TN to be used by referring to the cancellation correction table Tc based on the occupant's head position estimated by head position estimation unit 27. Furthermore, database unit 41 reads the cancellation correction filter C^ corresponding to the current control target frequency by referring to the coefficient table TN used based on the current control target frequency. me The coefficients C^0 and C^1. Database unit 41 will cancel the correction filter C^ me The reading coefficients C^0 and C^1 are output to the cancellation correction unit 42.

[0079] Although not shown, database unit 41 stores a noise correction table similar to the aforementioned cancellation correction table Tc. In a similar method, database unit 41 reads the noise correction filter H^ corresponding to the current control target frequency. me The coefficient sequence (described later) or noise correction filter H^ me The coefficients H^0 and H^1. Database unit 41 will read the noise correction filter H^ corresponding to the current control target frequency. me coefficient sequence or noise correction filter H^ me The coefficients H^0 and H^1 are output to the noise correction unit 43.

[0080] As described above, database unit 41 stores the first relation (occupant's head position and offset correction filter C^) in the table. me The relationship between the coefficients) and the second relationship (occupant's head position and noise correction filter H^) me (The relationship between the coefficients of each).

[0081] refer to Figure 2 The cancellation correction unit 42 includes a cancellation correction filter C^ me FIR filters or SAN filters can be used to offset correction filters C^ me The cancellation correction filter C^ output from database unit 41 me The coefficients of the cancellation correction filter C^ are continuously updated. me The coefficient.

[0082] refer to Figure 5 , cancellation correction filter C^ me Defined by the following equation (1). Incidentally, in the following equation (1), "C" e "C" represents the transmission characteristics of the canceled sound y from speaker 13 to the occupant's head position (more specifically, the occupant's ear position), and "C" in equation (1) m "" indicates the transmission characteristics of the canceled sound y from speaker 13 to error microphone 14.

[0083]

[0084] As is clear from equation (1) above, the cancellation correction filter C^ me The transmission characteristic C of the canceled sound y from speaker 13 to the occupant's head position is defined. e The transmission characteristics C of the canceled sound y from speaker 13 to error microphone 14 m The ratio.

[0085] Reference Figure 2 The cancellation correction unit 42 uses a cancellation correction filter C^ me Correction cancellation estimated signal y^ m2 To generate the cancellation estimation signal y^ e Cancel the estimated signal y^ e It is the estimated signal of the canceled sound y at the occupant's head position (more specifically, the occupant's ear position).

[0086] Incidentally, see Figure 6 The occupant's head position estimated by head position estimation unit 27 can exist between position N (N: an integer) and position N+1. In this case, the cancellation correction unit 42 can use a cancellation correction filter C^ based on position N. me (N) and the cancellation correction filter C^ at position N+1 me (N+1) for the cancellation estimation signal y^ m2 Perform linear interpolation to generate the cancellation estimate signal y^ e .

[0087] refer to Figure 2 The noise correction unit 43 includes a noise correction filter H^ me FIR filters or SAN filters can be used as noise correction filters. me The noise correction filter H^ output from database unit 41 me The coefficients of the noise correction filter H^ are continuously updated. me The coefficient.

[0088] Reference Figure 5 Noise correction filter H^ me Defined by the following equation (2). Incidentally, H in equation (2) e The transmission characteristics of road noise d from the noise source to the occupant's head position (more specifically, the occupant's ear position) are represented by the following equation (2): H m This represents the transmission characteristics of road noise d from the noise source to the error microphone 14.

[0089]

[0090] As is clear from equation (2) above, the noise correction filter H^ me The transmission characteristics H of road noise d, defined as the noise source from the occupant's head position. e The transmission characteristics H of road noise d from the noise source to the error microphone 14 m The ratio.

[0091] Reference Figure 2 The noise correction unit 43 uses a noise correction filter H^ me Corrected noise estimation signal d^ m To generate the noise estimation signal d^ e Noise estimation signal d^ e This is an estimated signal of the road noise d at the occupant's head position (more specifically, the occupant's ear position). Similar to the cancellation correction unit 42, the noise correction unit 43 can adjust the noise estimation signal d^ m Perform linear interpolation to generate the noise estimation signal d^ e .

[0092] <Control Filter Update Unit 29>

[0093] Similar to the control signal output unit 22, the control filter update unit 29 includes a control filter W. The control filter update unit 29 updates the control filter W based on the occupant's head position estimated by the head position estimation unit 27. The control filter update unit 29 includes a control filter unit 45, an adder 46, and a control update unit 47.

[0094] Control filter unit 45 uses control filter W to cancel the estimated signal y^ e Filtering is performed. In the following text, "cancel the estimated signal y^" e "This indicates that the estimated signal y^ has been canceled by the control filter unit 45" e .

[0095] Adder 46 cancels the estimated signal y^ e And noise estimation signal d^ e Add them together to generate the virtual error signal e e Adder 46 will generate the virtual error signal e e Output to control update unit 47.

[0096] The control update unit 47 uses an adaptive algorithm, such as the LMS algorithm, to update the coefficients of the control filter W. More specifically, the control update unit 47 updates the coefficients of the control filter W such that the virtual error signal e output from the adder 46... e minimize.

[0097] When the coefficients of the control filter W are updated in the control filter update unit 29 in this manner, the updated coefficients of the control filter W are output to the control signal output unit 22, and the coefficients of the control filter W are updated in the control signal output unit 22. That is, the coefficients of the control filter W set in the control signal output unit 22 are not fixed values, but values ​​that are updated sequentially based on the signals from the control filter update unit 29.

[0098] Mechanisms for reducing road noise

[0099] The mechanism for reducing road noise d by noise reduction system 11 will then be described.

[0100] According to the configuration of the noise reduction system 11 and the cancellation correction filter C^ me The definition of canceling the estimated signal y^ e It can be represented by the following equation (3). Similarly, based on the configuration of the noise reduction system 11 and the noise correction filter H^ me Definition of noise estimation signal d^ e It can be represented by the following formula (4).

[0101]

[0102]

[0103] As is clear from equations (3) and (4) above, the virtual error signal e e (=d^ e +y^ e This corresponds to the controlled (after noise reduction) sound pressure level at the occupant's head position. Therefore, as described above, updating the coefficients of the control filter W makes the virtual error signal e... e Minimizing this can effectively suppress road noise d at the occupant's head position.

[0104] <Effects of the First Implementation Method>

[0105] According to the first embodiment, the controller 15 updates the primary path filter H^ and the secondary path filter C^ based on the reference signal x and the error signal e. In other words, the controller 15 updates the estimated acoustic characteristics of the interior space based on the reference signal x and the error signal e. Therefore, even if the acoustic characteristics of the interior space change according to the displacement of each error microphone 14, the characteristics of the control filter W can be changed to follow the change in acoustic characteristics. Therefore, the error microphones 14 can be arranged on a movable part such as the headrest 6a, and thus closer to the occupant's head position.

[0106] Additionally, the area with high control effect (sound reduction effect) of the noise reduction system 11 is limited to the area around each error microphone 14 (see Figure 1 (Circle A in the diagram). Therefore, when the occupant's head moves away from the error microphone 14 due to the occupant's driving posture, the control effect of the noise reduction system 11 that the occupant can perceive may be reduced.

[0107] In this way, controller 15 estimates the head position of the occupants in carriage 5 based on the updated secondary path filter C^, and updates the control filter W used to control speaker 13 based on the estimated head position of the occupants. Therefore, when the head position of the occupants changes, the characteristics of control filter W can also be changed to follow the change in the head position of the occupants. Therefore, road noise d at the head position of the occupants can be effectively reduced. Furthermore, no dedicated equipment (e.g., a camera) is required for estimating the head position of the occupants. Therefore, a simple and inexpensive noise reduction system 11 can be provided.

[0108] Figure 7 This is a graph showing the effect of reducing road noise d at the occupant's head position (more specifically, the occupant's ear position). For example... Figure 7 As shown, when the noise reduction of this embodiment (i.e., the noise reduction system 11 based on the occupant's head position update control filter W) is ON, it can reduce road noise d in a wide frequency band compared to the case where conventional noise reduction (i.e., the noise reduction system without considering the occupant's head position update control filter W) is ON and the case where noise reduction is OFF.

[0109] Methods for building neural networks

[0110] Next, we will describe the method for constructing the neural network that constitutes the head position estimation unit 27.

[0111] refer to Figure 8 When constructing the neural network, data is collected for learning. More specifically, the coefficients of the secondary path filter C^ are measured for each head position of the occupant. This measurement can be performed using a mannequin positioned on the occupant seat 6, or by having the occupant actually sit in the occupant seat 6. When an FIR filter is used for the secondary path filter C^, the measured coefficients of the secondary path filter C^ are output as an impulse response waveform. When a SAN filter is used for the secondary path filter C^, the measured coefficients of the secondary path filter C^ are output for each frequency.

[0112] The occupant's head position is determined by the relative positional relationship between the error microphone 14 and the occupant's head position. Therefore, regardless of whether the backrest of the occupant seat 6 is reclined, the occupant's head position is the same if the relative positional relationship between the error microphone 14 and the occupant's head position is constant. (See also...) Figure 8 (Position 1 in the text).

[0113] Next, the coefficients of the secondary path filter C^, measured as described above, are set as input values, the occupant's head position is set as the output value, and the neural network learns the coefficients (w1, w2, ..., wn) of each layer. For this learning, machine learning methods such as deep learning can be used, for example.

[0114] Next, the neural network, having learned the coefficients w of each layer, is stored as system parameters in the memory of the controller 15. Therefore, the occupant's head position can be estimated using the neural network through the coefficients of the secondary path filter C^.

[0115] <Method for constructing the offset correction table Tc>

[0116] Next, we will refer to Figures 9A to 9C and Figure 10 A method for constructing a cancellation correction table Tc stored in a database unit 41 of the estimation signal correction unit 28 is described. Figures 9A to 9C This is an illustrative diagram showing a first example of a method for constructing the offset correction table Tc, while Figure 10 This is an illustrative diagram showing a second example of a method for constructing the offset correction table Tc.

[0117] See Figure 9A and Figure 9B In the first example of the method for constructing the cancellation correction table Tc, the sound pressure signal S at the microphone position is measured at a microphone 14m. m Simultaneously, the speaker 13 outputs the identified sound yi, and the measuring microphone 14e measures the sound pressure signal S at the occupant's head position. e And record the measured sound pressure signal S m and S e (Step ST1: Signal recording).

[0118] Next, the recorded sound pressure signal S m and S e Identify the cancellation correction filter C^ me The coefficient (step ST2: C^ me (Identification). More specifically, for the cancellation correction filter C^ me The coefficients are updated to make the sound pressure signal S e and by using the cancellation correction filter C^ me For sound pressure signal S m The error signal between the signals obtained by filtering is minimized. At this time, the cancellation correction filter C^ me The coefficients are output as the coefficient sequence of the FIR filter. Therefore, when the FIR filter is used to cancel the correction filter C^ me At that time, the cancellation correction filter C^ output from the recognition system can be used. meThe coefficients are directly input into the offset correction table Tc to construct the offset correction table Tc.

[0119] On the other hand, when the SAN filter is used to cancel the correction filter C^ me At that time, the cancellation correction filter C^ output from the recognition system me The calculated coefficients (the coefficient sequence of the FIR filter) are subjected to a Fast Fourier Transform (FFT), such as... Figure 9C As shown. Therefore, the coefficients for each frequency of the SAN filter can be obtained. The cancellation correction table Tc can be constructed by inputting the coefficients obtained for each frequency of the SAN filter into the cancellation correction table Tc.

[0120] Reference Figure 10 In the second example of the method for constructing the cancellation correction table Tc, the sound pressure signal S is measured in the same manner as in the first example of the method for constructing the cancellation correction table Tc. e S m And from the measured sound pressure signal S e S m Extract the transmission characteristics C of the canceling sound y. e C m The coefficient. At this time, the transmission characteristics C of the sound y are canceled. e C m The coefficients are calculated as the coefficient sequence of the FIR filter.

[0121] Next, by examining the transmission characteristics C of the canceling sound y... e C m Perform an FFT on the coefficients (the coefficient sequence of the FIR filter) to calculate the transmission characteristics C of the canceled sound y. e C m The frequency characteristics of the sound y. Next, by canceling the transmission characteristics C of the sound y. e The frequency response (the transmission characteristics of the canceled sound y from speaker 13 to the occupant's head position) divided by the transmission characteristics of the canceled sound y, C. m The frequency characteristics (transmission characteristics of the canceled sound y from speaker 13 to error microphone 14) are used to calculate the cancellation correction filter C^. me The coefficients. At this point, the compensation filter C^ will be canceled. me The coefficients are calculated for each frequency of the SAN filter. Therefore, when the SAN filter is used to cancel the correction filter C^ me In this case, the cancellation correction table Tc can be constructed by inputting the coefficients obtained for each frequency of the SAN filter into the cancellation correction table Tc.

[0122] On the other hand, when an FIR filter is used to cancel the correction filter C^ meAt that time, for the calculated cancellation correction filter C^ me The coefficients (coefficients at each frequency of the SAN filter) are subjected to an inverse fast Fourier transform (IFFT). Therefore, the coefficient sequence of the FIR filter can be obtained. The cancellation correction table Tc can be constructed by inputting the obtained FIR filter coefficient sequence into the cancellation correction table Tc.

[0123] The noise correction table (not shown) stored in the database unit 41 of the estimated signal correction unit 28 can also be constructed using a method similar to that used to construct the cancellation correction table Tc. However, when constructing the noise correction table, the sound pressure signal S can be measured while the vehicle 1 is traveling and road noise d is actually generated. e S m .

[0124] <Modified Example of the First Embodiment>

[0125] In the first embodiment described above, the vibration sensor 12 installed in the suspension 3 is used as an example of a reference signal generator. In another embodiment, such as Figure 11 As shown, a reference microphone 16 arranged near a noise source in carriage 5 can be used as an example of a reference signal generator. In this case, the reference microphone 16 can detect the sound generated by the noise source and generate a reference signal x based on the detected sound.

[0126] (Second Implementation)

[0127] Next, we will refer to Figure 12 and Figure 13 The second embodiment of the present invention will be described. Furthermore, descriptions overlapping with the description of the first embodiment of the present invention will be appropriately omitted. Figure 13 The symbol "ADA" in the text stands for "Adaptive".

[0128] Figure 12 This is a schematic diagram showing a vehicle 1 in which an active noise reduction system 51 (hereinafter referred to as "noise reduction system 51") according to the second embodiment is applied. When the vehicle 1 is in motion, the internal combustion engine 7 (hereinafter referred to as "engine 7") vibrates and generates drive noise d (e.g., the muffled sound of the engine 7 or the drive shaft) in the passenger compartment 5.

[0129] The noise reduction system 51 according to the second embodiment is a feedback-controllable ANC device for reducing drive noise d caused by vibration of the engine 7 as described above. Furthermore, in another embodiment, when an electric motor is used instead of the engine 7 as the drive source of the vehicle 1, the noise reduction system 51 can reduce drive noise d caused by vibration of the electric motor. Except for the reference signal generator 52 and controller 53, the configuration of the noise reduction system 51 according to the second embodiment is the same as that of the noise reduction system 11 according to the first embodiment, and therefore its description will be omitted.

[0130] <Reference Signal Generator 52>

[0131] refer to Figure 13 The reference signal generator 52 includes a frequency detection circuit 56, a cosine wave generation circuit 57, and a sine wave generation circuit 58.

[0132] The frequency detection circuit 56 detects the frequency (hereinafter referred to as "noise frequency f") of the drive noise d based on vehicle information (e.g., engine speed or vehicle speed) corresponding to the drive noise d. The frequency detection circuit 56 outputs the detected noise frequency f to the cosine wave generation circuit 57, the sine wave generation circuit 58, and the controller 53.

[0133] The cosine wave generating circuit 57 generates a reference cosine wave signal rc (example of a reference signal) corresponding to the driving noise d based on the noise frequency f output from the frequency detection circuit 56. The cosine wave generating circuit 57 outputs the generated reference cosine wave signal rc to the controller 53.

[0134] The sine wave generating circuit 58 generates a reference sine wave signal rs (example of a reference signal) corresponding to the driving noise d based on the noise frequency f output from the frequency detection circuit 56. The sine wave generating circuit 58 outputs the generated reference sine wave signal rs to the controller 53.

[0135] <Controller 53>

[0136] The controller 53 includes a control signal output unit 62, a D / A conversion unit 63, an A / D conversion unit 64, an acoustic characteristic update unit 65, a reference signal correction unit 66, a head position estimation unit 67, an estimation signal correction unit 68, and a control filter update unit 69 as functional components. The configuration of the D / A conversion unit 63 and the A / D conversion unit 64 is the same as that of the D / A conversion unit 23 and the second A / D conversion unit 24 of the controller 15 according to the first embodiment, and therefore their description will be omitted.

[0137] <Control signal output unit 62>

[0138] The control signal output unit 62 of the controller 53 includes a control filter W. A SAN filter is used to control the filter W. The control signal output unit 62 includes a first control filter unit 71, a second control filter unit 72, a first adder 73, a third control filter unit 74, a fourth control filter unit 75, and a second adder 76.

[0139] The first control filter unit 71 has control filter coefficients W0. The control filter coefficients W0 form the real part of the coefficients of the control filter W. The first control filter unit 71 filters the reference cosine wave signal rc output from the reference signal generator 52.

[0140] The second control filter unit 72 has control filter coefficients W1. The control filter coefficients W1 form the imaginary part of the coefficients of the control filter W. The second control filter unit 72 filters the reference sine wave signal rs output from the reference signal generator 52.

[0141] The first adder 73 generates a control signal u0 by adding the reference cosine wave signal rc, which has passed through the first control filter unit 71, and the reference sine wave signal rs, which has passed through the second control filter unit 72. The first adder 73 outputs the generated control signal u0 to the D / A conversion unit 63 and the acoustic characteristic update unit 65.

[0142] The third control filter unit 74 has coefficients obtained by reversing the polarity of the control filter coefficients W0. The third control filter unit 74 filters the reference sine wave signal rs output from the reference signal generator 52.

[0143] The fourth control filter unit 75 has control filter coefficients W1. The fourth control filter unit 75 filters the reference cosine wave signal rc output from the reference signal generator 52.

[0144] The second adder 76 generates a control signal u1 by adding the reference sine wave signal rs, which has passed through the third control filter unit 74, and the reference cosine wave signal rc, which has passed through the fourth control filter unit 75. The second adder 76 outputs the generated control signal u1 to the acoustic characteristic update unit 65.

[0145] <Acoustic Characteristics Update Unit 65>

[0146] The acoustic characteristic update unit 65 of the controller 53 includes a cancellation estimation signal generation unit 81, a noise estimation signal generation unit 82, and a virtual error signal generation unit 83.

[0147] The cancellation estimation signal generation unit 81 includes a secondary path filter C^. A SAN filter is used for the secondary path filter C^. The cancellation estimation signal generation unit 81 includes a first-stage path filter unit 91, a second-stage path filter unit 92, an adder 93, a first-stage path update unit 94, and a second-stage path update unit 95.

[0148] The first-stage path filter unit 91 has secondary path filter coefficients C^0. The secondary path filter coefficients C^0 form the real part of the coefficients of the secondary path filter C^. The first-stage path filter unit 91 filters the control signal u0 output from the control signal output unit 62.

[0149] The second-stage path filter unit 92 has secondary path filter coefficients C^1. The secondary path filter coefficients C^1 form the imaginary part of the coefficients of the secondary path filter C^. The second-stage path filter unit 92 filters the control signal u1 output from the control signal output unit 62.

[0150] Adder 93 generates the cancellation estimation signal y^ by adding the control signal u0 that has passed through the first-stage path filter unit 91 and the control signal u1 that has passed through the second-stage path filter unit 92. m1 Adder 93 will generate the cancellation estimate signal y^ m1 The signal is output to the virtual error signal generation unit 83.

[0151] The first-stage path update unit 94 uses an adaptive algorithm, such as the LMS algorithm, to update the secondary path filter coefficients C^0. More specifically, the first-stage path update unit 94 updates the secondary path filter coefficients C^0 such that the virtual error signal e1 (described later) output from the virtual error signal generation unit 83 is minimized.

[0152] The second-stage path update unit 95 uses an adaptive algorithm, such as the LMS algorithm, to update the secondary path filter coefficients C^1. More specifically, the second-stage path update unit 95 updates the secondary path filter coefficients C^1 to minimize the virtual error signal e1 output from the virtual error signal generation unit 83.

[0153] The noise estimation signal generation unit 82 includes a primary path filter H^. A SAN filter is used for the primary path filter H^. The noise estimation signal generation unit 82 includes a first primary path filter unit 101, a second primary path filter unit 102, a first adder 103, a first primary path update unit 104, a second primary path update unit 105, a third primary path filter unit 106, a fourth primary path filter unit 107, and a second adder 108.

[0154] The first primary path filter unit 101 has primary path filter coefficients H^0. The primary path filter coefficients H^0 form the real part of the coefficients of the primary path filter H^. The first primary path filter unit 101 filters the reference cosine wave signal rc output from the reference signal generator 52.

[0155] The second primary path filter unit 102 has coefficients obtained by reversing the polarity of the primary path filter coefficients H^1. The primary path filter coefficients H^1 form the imaginary part of the coefficients of the primary path filter H^. The second primary path filter unit 102 filters the reference sine wave signal rs output from the reference signal generator 52.

[0156] The first adder 103 generates a noise estimation signal d^ by adding together the reference cosine wave signal rc that has passed through the first primary path filter unit 101 and the reference sine wave signal rs that has passed through the second primary path filter unit 102. m1 The first adder 103 will generate the noise estimation signal d^ m1 The signal is output to the virtual error signal generation unit 83 and the estimation signal correction unit 68.

[0157] The first primary path update unit 104 uses an adaptive algorithm, such as the LMS algorithm, to update the primary path filter coefficients H^0. More specifically, the first primary path update unit 104 updates the primary path filter coefficients H^0 such that the virtual error signal e1 output from the virtual error signal generation unit 83 is minimized.

[0158] The second primary path update unit 105 uses an adaptive algorithm, such as the LMS algorithm, to update the primary path filter coefficients H^1. More specifically, the second primary path update unit 105 updates the primary path filter coefficients H^1 such that the virtual error signal e1 output from the virtual error signal generation unit 83 is minimized.

[0159] The third primary path filter unit 106 has primary path filter coefficients H^0. The third primary path filter unit 106 filters the reference sine wave signal rs output from the reference signal generator 52.

[0160] The fourth primary path filter unit 107 has primary path filter coefficients H^1. The fourth primary path filter unit 107 filters the reference cosine wave signal rc output from the reference signal generator 52.

[0161] The second adder 108 generates a noise estimation signal d^ by adding the reference sine wave signal rs that has passed through the third primary path filter unit 106 and the reference cosine wave signal rc that has passed through the fourth primary path filter unit 107. m2 The second adder 108 will generate the noise estimation signal d^ m2 The output is sent to the estimated signal correction unit 68.

[0162] The virtual error signal generation unit 83 includes a first polarity inversion circuit 111, a second polarity inversion circuit 112, and an adder 113.

[0163] The first polarity inversion circuit 111 inverts the cancellation estimation signal y^ output from the cancellation estimation signal generation unit 81. m1 The polarity of the noise estimation signal d^ output from the noise estimation signal generation unit 82 is reversed by the second polarity inversion circuit 112. m1 The polarity of.

[0164] Adder 113 combines the error signal e and the cancellation estimation signal y^ that has already passed through the first polarity inversion circuit 111. m1 and the noise estimation signal d^ that has already passed through the second polarity inversion circuit 112 m1 The signals are added together to generate a virtual error signal e1. Adder 113 outputs the generated virtual error signal e1 to the cancellation estimation signal generation unit 81 and the noise estimation signal generation unit 82.

[0165] <Reference Signal Correction Unit 66>

[0166] Similar to the cancellation estimation signal generation unit 81, the reference signal correction unit 66 of the controller 53 includes a secondary path filter C^. When the coefficients C^0 and C^1 of the secondary path filter C^ are updated in the cancellation estimation signal generation unit 81, the updated coefficients C^0 and C^1 of the secondary path filter C^ are output to the reference signal correction unit 66, and the coefficients C^0 and C^1 of the secondary path filter C^ are updated in the reference signal correction unit 66. The reference signal correction unit 66 outputs the updated coefficients C^0 and C^1 of the secondary path filter C^ to the head position estimation unit 67.

[0167] The reference signal correction unit 66 includes a third-stage path filter unit 121, a fourth-stage path filter unit 122, a first adder 123, a fifth-stage path filter unit 124, a sixth-stage path filter unit 125, and a second adder 126.

[0168] The third-stage path filter unit 121 has secondary path filter coefficients C^0. The third-stage path filter unit 121 filters the reference cosine wave signal rc output from the reference signal generator 52.

[0169] The fourth secondary path filter unit 122 has coefficients obtained by reversing the polarity of the secondary path filter coefficients C^1. The fourth secondary path filter unit 122 filters the reference sine wave signal rs output from the reference signal generator 52.

[0170] The first adder 123 generates the cancellation estimation signal y^ by adding the reference cosine wave signal rc that has passed through the third-stage path filter unit 121 and the reference sine wave signal rs that has passed through the fourth-stage path filter unit 122. m2 The first adder 123 will generate the cancellation estimate signal y^ m2 The output is sent to the estimated signal correction unit 68.

[0171] The fifth secondary path filter unit 124 has secondary path filter coefficients C^0. The fifth secondary path filter unit 124 filters the reference sine wave signal rs output from the reference signal generator 52.

[0172] The sixth secondary path filter unit 125 has secondary path filter coefficients C^1. The sixth secondary path filter unit 125 filters the reference cosine wave signal rc output from the reference signal generator 52.

[0173] The second adder 126 generates the cancellation estimation signal y^ by adding the reference sine wave signal rs that has passed through the fifth secondary path filter unit 124 and the reference cosine wave signal rc that has passed through the sixth secondary path filter unit 125. m3 The second adder 126 will generate the cancellation estimate signal y^ m3 The output is sent to the estimated signal correction unit 68.

[0174] <Head Position Estimation Unit 67>

[0175] The head position estimation unit 67 of the controller 53 estimates the head position of the occupants in the carriage 5 based on the noise frequency f output from the reference signal generator 52 and the coefficients C0 and C1 of the secondary path filter C^ output from the reference signal correction unit 66. The method by which the head position estimation unit 67 estimates the head position of the occupants is the same as the method by which the head position estimation unit 27 according to the first embodiment estimates the head position of the occupants, and therefore its description will be omitted.

[0176] <Estimation Signal Correction Unit 68>

[0177] The estimated signal correction unit 68 of the controller 53 includes a database unit 131, a cancellation correction unit 132, and a noise correction unit 133. The configuration of the database unit 131 is the same as that of the database unit 41 according to the first embodiment, and therefore its description will be omitted.

[0178] The cancellation correction unit 132 includes a cancellation correction filter C^ me SAN filters are used to cancel out correction filters C^ me The cancellation correction unit 132 includes a first cancellation correction filter unit 141, a second cancellation correction filter unit 142, a first adder 143, a third cancellation correction filter unit 144, a fourth cancellation correction filter unit 145, and a second adder 146.

[0179] The first cancellation correction filter unit 141 has cancellation correction filter coefficients C^ me 0. Cancellation correction filter coefficients C^ me 0 forms a cancellation correction filter C^ me The real part of the coefficients. The first cancellation correction filter unit 141 cancels the estimated signal y^ output from the reference signal correction unit 66. m2 Perform filtering.

[0180] The second cancellation correction filter unit 142 has cancellation correction filter coefficients C^ me l. Cancellation correction filter coefficients C^ me 1. Forming a cancellation correction filter C^ me The imaginary part of the coefficients. The second cancellation correction filter unit 142 cancels the estimated cancellation signal y^ output from the reference signal correction unit 66. m3 Perform filtering.

[0181] The first adder 143 uses the canceled estimated signal y^ that has already passed through the first cancellation correction filter unit 141. m2 And the cancellation estimate signal y^ that has already passed through the second cancellation correction filter unit 142 m3 Add them together to generate the cancellation estimate signal y^ e1 The first adder 143 will generate the cancellation estimate signal y^ e1 The output is sent to the control filter update unit 69.

[0182] The third cancellation correction filter unit 144 has cancellation correction filter coefficients C^ me 0. The third cancellation correction filter unit 144 applies the cancellation estimate signal y^ output from the reference signal correction unit 66. m3 Perform filtering.

[0183] The fourth cancellation correction filter unit 145 has cancellation correction filter coefficients C^me 1. The fourth cancellation correction filter unit 145 applies the cancellation estimation signal y^ output from the reference signal correction unit 66. m2 Perform filtering.

[0184] The second adder 146 takes the canceled estimate signal y^ that has already passed through the third cancellation correction filter unit 144 and adds it. m3 And the cancellation estimation signal y^ that has already passed through the fourth cancellation correction filter unit 145 m2 Add them together to generate the cancellation estimate signal y^ e2 The second adder 146 will generate the cancellation estimate signal y^ e2 The output is sent to the control filter update unit 69.

[0185] Noise correction unit 133 includes noise correction filter H^ me SAN filters are used in noise correction filters. me The noise correction unit 133 includes a first noise correction filter unit 151, a second noise correction filter unit 152, and an adder 153.

[0186] The first noise correction filter unit 151 has noise correction filter coefficients H^ me 0. Noise correction filter coefficient H^ me 0 Forms a noise correction filter H^ me The real part of the coefficients. The first noise correction filter unit 151 processes the noise estimation signal d^ output from the noise estimation signal generation unit 82. m1 Perform filtering.

[0187] The second noise correction filter unit 152 has noise correction filter coefficients H^ me 1. Noise correction filter coefficients H^ me 1. Forming a noise correction filter H^ me The imaginary part of the coefficients. The second noise correction filter unit 152 processes the noise estimation signal d^ output from the noise estimation signal generation unit 82. m2 Perform filtering.

[0188] Adder 153 uses the noise estimation signal d^ that has already passed through the first noise correction filter unit 151 to... m1 and the noise estimation signal d^ that has already passed through the second noise correction filter unit 152 m2 Add them together to generate the noise estimation signal d^ e Adder 153 will generate the noise estimation signal d^ e The output is sent to the control filter update unit 69.

[0189] <Control Filter Update Unit 69>

[0190] The control filter update unit 69 of the controller 53 includes a control filter W, similar to the control signal output unit 62. The control filter update unit 69 includes a fifth control filter unit 161, a sixth control filter unit 162, a first adder 163, a second adder 164, a first control update unit 165, and a second control update unit 166.

[0191] The fifth control filter unit 161 has control filter coefficients W0. The fifth control filter unit 161 applies the cancellation estimation signal y^ output from the estimation signal correction unit 68. e1 Perform filtering.

[0192] The sixth control filter unit 162 has control filter coefficients W1. The sixth control filter unit 162 applies the cancellation estimation signal y^ output from the estimation signal correction unit 68. e2 Perform filtering.

[0193] The first adder 163 uses the canceled estimated signal y^ that has already passed through the fifth control filter unit 161 to... e1 And the canceled estimated signal y^ that has already passed through the sixth control filter unit 162 e2 Add them together to generate the cancellation estimate signal y^ e The first adder 163 will generate the cancellation estimate signal y^ e The output is sent to the second adder 164.

[0194] The second adder 164 cancels the estimated signal y^ e And noise estimation signal d^ e Add them together to generate the virtual error signal e e The second adder 164 will generate the virtual error signal e. e The output is sent to the first control update unit 165 and the second control update unit 166.

[0195] The first control update unit 165 uses an adaptive algorithm, such as the LMS algorithm, to update the control filter coefficients W0. More specifically, the first control update unit 165 updates the control filter coefficients W0 such that the virtual error signal e output from the second adder 164... e minimize.

[0196] The second control update unit 166 uses an adaptive algorithm, such as the LMS algorithm, to update the control filter coefficients W1. More specifically, the second control update unit 166 updates the control filter coefficients W1 such that the virtual error signal e output from the second adder 164... e minimize.

[0197] When the coefficients W0 and W1 of the control filter W are updated in the control filter update unit 69, the updated coefficients W0 and W1 of the control filter W are output to the control signal output unit 62, and the coefficients W0 and W1 of the control filter W are updated in the control signal output unit 62.

[0198] <Effects of the Second Implementation>

[0199] In the controller 53 according to the second embodiment, a SAN filter is used as the control filter W, the primary path filter H^, and the secondary path filter C^. Therefore, compared to the case where an FIR filter is used as these filters, the computational load of the controller 53 can be reduced. Therefore, the noise reduction system 51 can be implemented using an inexpensive ECU.

[0200] The specific embodiments of the present invention have been described above, but the present invention is not limited to the above embodiments, and various modifications and changes can be made within the scope of the present invention.

Claims

1. An active noise cancellation system, the active noise cancellation system being used to reduce noise in the internal space of a moving body, the active noise cancellation system comprising: A reference signal generator, the reference signal generator being configured to generate a reference signal corresponding to the noise; A cancelling sound generator, the cancelling sound generator being configured to generate cancelling sounds for canceling the noise; An error detector is configured to detect the error between the noise and the canceled sound, and to generate an error signal corresponding to the error; as well as A controller configured to control the cancelling sound generator based on the reference signal and the error signal. The controller is configured as follows: The estimated values ​​of the acoustic properties in the interior space are updated based on the reference signal and the error signal; The head position of the occupant in the interior space is estimated based on the updated estimates of the acoustic characteristics in the interior space; and The control filter is updated based on the estimated head position of the occupant; the control filter is a filter used to control the canceling sound generator.

2. The active noise cancellation system according to claim 1, wherein, The controller is also configured to: Based on the estimated head position of the occupant, an estimated signal of the noise at the head position of the occupant and an estimated signal of the canceling sound at the head position of the occupant are generated. and The control filter is updated to minimize the error between the estimated signal of the noise at the occupant's head position and the estimated signal of the canceled sound at the occupant's head position.

3. The active noise reduction system according to claim 2, wherein, The controller is also configured to: Based on the reference signal and the error signal, update the estimated value of the noise transmission characteristics from the noise source to the error detector and the estimated value of the cancellation sound transmission characteristics from the cancellation sound generator to the error detector; Based on the updated estimate of the noise's transmission characteristics from the noise source to the error detector, an estimated signal of the noise at the location of the error detector is generated; An estimated signal of the noise at the occupant's head position is generated by correcting the estimated signal of the noise at the location of the error detector using a prescribed noise correction filter. Based on the updated estimate of the transmission characteristics of the canceled sound from the canceled sound generator to the error detector, an estimated signal of the canceled sound at the location of the error detector is generated; and An estimated signal of the canceling sound at the occupant's head position is generated by correcting the estimated signal of the canceling sound at the location of the error detector using a prescribed cancellation correction filter.

4. The active noise reduction system according to claim 3, wherein, The cancellation correction filter is defined as the ratio of the transmission characteristics of the cancellation sound from the cancellation sound generator to the occupant's head position to the transmission characteristics of the cancellation sound from the cancellation sound generator to the error detector, and The noise correction filter is defined as the ratio of the noise transmission characteristics from the noise source to the head position of the occupant to the noise transmission characteristics from the noise source to the error detector.

5. The active noise reduction system according to claim 3, wherein, The controller is also configured to store each of a first relationship and a second relationship in a table, the first relationship being the relationship between the occupant's head position and the coefficients of the cancellation correction filter, and the second relationship being the relationship between the occupant's head position and the coefficients of the noise correction filter.

6. The active noise cancellation system according to any one of claims 1 to 5, wherein, The controller includes a neural network that has learned the relationship between estimates of the acoustic properties in the interior space and the head position of the occupant, and the controller is configured to estimate the head position of the occupant by inputting the estimates of the acoustic properties in the interior space into the neural network.

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