Active vibration and noise reduction device

Abstract

To update an adaptive filter coefficient with the optimal value to easily set the filter coefficient to a control filter that generates a signal for cancelling a noise.SOLUTION: An active vibration noise reduction device 100 comprises: a speaker 20 that outputs a cancellation sound y for cancelling a noise d; a microphone 30 that generates an error signal e from the noise d and the cancellation sound y; a control filter W that generates, from a reference signal r, a control signal u for controlling the cancellation sound y; and a secondary path filter C^ indicating an estimated value of a transmission function from the speaker 20 to the microphone 30. The control filter W is adaptively updated by an update amount ΔW determined by multiplying the error signal e, a step size parameter μW(t) calculated on the basis of the error signal e, and a result of convolution operation of the reference signal r and the secondary path filter C^.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an active vibration noise reduction device. [Background technology]

[0002] Active noise reduction devices have been studied for some time now to reduce noise (e.g., road noise) generated inside a vehicle cabin by generating a canceling sound that is in the opposite phase to the noise and causing the generated canceling sound to interfere with the noise.

[0003] For example, in the active noise control device disclosed in Patent Document 1, paragraph 0008 states that "...a target value for the noise level inside a vehicle or the like is expressed as a function of the frequency characteristics of a noise source such as an engine, for example, its rotation speed, and the sound pressure error between the target value at the current rotation speed and the residual noise level is calculated. A convergence coefficient is determined based on this sound pressure error, and this convergence coefficient is used to update the filter coefficient of adaptive digital filter processing using the steepest descent method, thereby controlling the control sound output from the control sound source." [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent No. 2751685 Summary of the Invention [Problem to be solved by the invention]

[0005] The active noise control system described in Patent Document 1 aims to create a comfortable space that does not expose occupants to unpleasant noise, regardless of changes in engine speed. Specifically, the system calculates a target value for the noise level inside the vehicle cabin based on the current engine speed, with reference to the target value storage table in FIG. 6 of Patent Document 1. The active noise control system also calculates the residual noise level and the sound pressure error between the target value and the residual noise level. The active noise control system then determines a convergence coefficient by referring to the convergence coefficient storage table (map) in FIG. 7 of Patent Document 1.

[0006] As shown in FIG. 7 of Patent Document 1, the convergence coefficient is constant when the sound pressure error is equal to or greater than a predetermined value. Therefore, when the sound pressure error is large, such as at the beginning of control or when the microphone position has changed, there is room for improvement in order to improve the convergence. In particular, to flatten the residual noise characteristics and improve stability, it is necessary to adjust the convergence coefficient storage table in FIG. 7, and setting the convergence coefficient storage table is complicated.

[0007] The present invention has been made in consideration of the above circumstances, and an object of the present invention is to provide an active noise reduction device that can easily update and set adaptive filter coefficients with optimal values ​​for a control filter that generates a signal that cancels noise. [Means for solving the problem]

[0008] That is, in order to solve the above-mentioned problems of the present invention, an active vibration noise reduction device of the present invention includes a speaker that outputs a canceling sound to cancel out a noise, a microphone that generates an error signal from the noise and the canceling sound, a control filter that generates a control signal for controlling the canceling sound from a reference signal, and a secondary path filter that indicates an estimate of a transfer function from the speaker to the microphone, and is characterized in that the control filter is adaptively updated by an update amount determined by multiplying the error signal, a step-size parameter calculated based on the error signal, and the result of a convolution operation of the reference signal and the secondary path filter. [Effects of the Invention]

[0009] According to the present invention, adaptive filter coefficients for a control filter that generates a signal that cancels noise can be updated with optimal values ​​and easily set. In particular, in the present invention, the active noise reduction system automatically adjusts the update amount for adaptively updating the control filter according to the magnitude of the error signal using a predetermined update formula. Therefore, when the error signal is large, for example, at the beginning of control, the active noise reduction system increases the update amount, thereby improving the convergence speed. On the other hand, when the error signal is small, for example, after control has converged, the active noise reduction system also decreases the update amount, thereby improving the accuracy of adaptive updating. [Brief explanation of the drawings]

[0010] [Figure 1] 1 is a block diagram showing a schematic configuration of an active vibration noise reduction device according to an embodiment of the present invention. [Figure 2] FIG. 10 is an explanatory diagram showing the concept of adaptively updating a control signal generating unit of a noise control unit. [Figure 3] FIG. 1 is an explanatory diagram showing an LMS algorithm for calculating filter coefficients that minimize an evaluation function. DETAILED DESCRIPTION OF THE INVENTION

[0011] The following describes in detail embodiments of the present invention. Note that the embodiments described below are examples for realizing the present invention, and should be appropriately modified or changed depending on the configuration of the device to which the present invention is applied and various conditions. The present invention is not limited to the following embodiments. In addition, in each drawing, the same components are given the same reference numerals, and their description will be omitted as appropriate.

[0012] In this specification, the "^" (hat) next to various symbols indicates an identified value or an estimated value. In the figures, the "^" is placed above the various symbols, but in the text, it is placed after the various symbols.

[0013] <Present Embodiment> [Outline of active vibration noise reduction device] Fig. 1 is a block diagram showing the schematic configuration of an active vibration noise reduction device according to this embodiment. The active vibration noise reduction device 100 shown in Fig. 1 constitutes an ANC device (Active Noise Control Device) for reducing noise generated in the vehicle cabin.

[0014] Various noises occur inside the vehicle while it is moving, such as tire noise, wind noise, engine noise, etc. An ANC device is installed in the vehicle to cancel out noise d generated by the transmission of vibrations from the power unit (engine, motor, etc.) and the inflow of exhaust noise, thereby achieving a quieter vehicle and creating a comfortable, high-quality interior space.

[0015] That is, the active vibration noise reduction device 100 generates a canceling sound y that is in opposite phase to the noise d generated by the noise source, and reduces the noise d by having the generated canceling sound y interfere with the noise d. The noise d corresponds to, for example, road noise caused by wheel vibration due to force from the road surface. Note that road noise is one example of noise d, and the noise d may also be noise other than road noise, for example, drivetrain noise caused by vibration of a drive source such as an internal combustion engine or an electric motor.

[0016] As shown in Fig. 1, the active vibration noise reduction device 100 according to this embodiment is configured to include a noise control unit 10, a speaker 20, a microphone 30, and a sound field learning unit 40. Note that transfer function H in Fig. 1 indicates the noise transfer path, and represents the transfer function of the primary path from the noise source to the microphone 30. Also, transfer function C in Fig. 1 represents the transfer function of the secondary path from the speaker 20 to the microphone 30.

[0017] The speaker 20 outputs a canceling sound y to cancel out the noise d. The speaker 20 is provided, for example, in front of the driver's seat or in a door on the side of the passenger seat.

[0018] The microphone 30 generates an error signal e from the noise d and the cancellation sound y. The microphone 30 is provided, for example, on the headrest of the passenger seat of the driver's seat. The microphone 30 generates the error signal e based on the cancellation sound y output by the speaker 20 and the noise d at the position of the microphone 30.

[0019] The noise control unit 10 and the sound field learning unit 40 are configured, for example, by a computer having an arithmetic processing unit (a processor such as a CPU (Central Processing Unit) or an MPU (Micro Processing Unit)) and a storage device (a memory such as a ROM (Read Only Memory) or RAM (Random Access Memory)). In other words, the components of the active vibration noise reduction device 100 other than the speaker 20 and the microphone 30 may be configured, for example, as a single piece of hardware, or may be configured as a unit consisting of multiple pieces of hardware.

[0020] A reference signal r corresponding to noise d is input to the noise control unit 10. The reference signal r is input to the noise control unit 10, for example, from a reference microphone (not shown) that generates the reference signal r from the noise d. The noise control unit 10 is configured to include a control filter unit 11, a secondary path filter unit 12, and a control update unit 13.

[0021] The control filter unit 11 generates a control signal u for controlling the cancellation sound y from a reference signal r. The control signal u cancels the noise d by controlling the cancellation sound y. The control filter unit 11 is also composed of a control filter W. The control filter W is, for example, an FIR (Finite Impulse Response) filter.

[0022] An FIR filter is a type of digital filter, and is a filter whose impulse response has a finite duration. In other words, an FIR filter is a filter whose output signal (impulse response) converges within a finite time when an impulse signal is input. Note that the control filter unit 11 may configure the control filter W using another filter (for example, a single-frequency adaptive notch filter).

[0023] The control filter unit 11 performs filtering on the reference signal r using the control filter W, thereby generating a control signal u for controlling the speaker 20. The control filter unit 11 inputs the generated control signal u to the speaker 20. The speaker 20 generates a cancellation sound y in accordance with the control signal u generated by the control filter unit 11. The control filter unit 11 also inputs the generated control signal u to the sound field learning unit 40.

[0024] The secondary path filter unit 12 is configured by a secondary path filter C^ that indicates an estimated value of a transfer function C from the speaker 20 to the microphone 30. The secondary path filter C^ is a filter that indicates an estimated value of the transfer function C of the secondary path. The secondary path filter C^ is configured by, for example, an FIR filter. Note that the secondary path filter C^ may also be configured by another filter (for example, a single-frequency adaptive notch filter).

[0025] The secondary path filter unit 12 corrects the reference signal r by filtering the reference signal r using the secondary path filter C^. The secondary path filter unit 12 inputs the corrected reference signal r to the control update unit 13.

[0026] The control update unit 13 adaptively updates the control filter W of the control filter unit 11 using an adaptive algorithm such as an LMS algorithm (Least Mean Square Algorithm). Specifically, the control update unit 13 adaptively updates the control filter W so that the error signal e output from the microphone 30 is minimized. In this embodiment, an adaptive update algorithm (described later) is employed that adaptively updates the control filter W by adaptively updating the filter coefficients.

[0027] The sound field learning unit 40 is configured to include a canceling estimation signal generating unit 41, a secondary path updating unit 42, a noise estimation signal generating unit 43, a primary path updating unit 44, a canceling estimation signal inverting unit 45, a noise estimation signal inverting unit 46, and a virtual error signal generating unit 47.

[0028] The canceling noise estimation signal generation unit 41 is configured with a secondary path filter C^. Similar to the secondary path filter C^ of the secondary path filter unit 12, the secondary path filter C^ of the canceling noise estimation signal generation unit 41 is a filter that indicates an estimated value of the transfer function C of the secondary path. The secondary path filter C^ of the canceling noise estimation signal generation unit 41 is configured with, for example, an FIR filter. Note that the secondary path filter C^ of the canceling noise estimation signal generation unit 41 may also be configured with another filter (for example, a single-frequency adaptive notch filter).

[0029] The canceling sound estimation signal generation unit 41 generates a canceling sound estimation signal y^ indicating an estimated value of the canceling sound y by filtering the control signal u input from the control filter unit 11 of the noise control unit 10 with the secondary path filter C^. The canceling sound estimation signal generation unit 41 inputs the generated canceling sound estimation signal y^ to a canceling sound estimation signal inversion unit 45.

[0030] The secondary path update unit 42 adaptively updates the secondary path filter C^1 of the cancellation estimation signal generator 41 using an adaptive algorithm such as an LMS algorithm. Specifically, the secondary path update unit 42 adaptively updates the secondary path filter C^1 so that the virtual error signal e1 input from the virtual error signal generator 47 is minimized. In this embodiment, the secondary path update unit 42 also employs an adaptive update algorithm (described later).

[0031] The noise estimation signal generator 43 is configured with a primary path filter H^. The primary path filter H^ is a filter that indicates an estimated value of the transfer function H of the primary path. The primary path filter H^ is configured with, for example, an FIR filter. Note that the primary path filter H^ of the noise estimation signal generator 43 may be configured with another filter (for example, a single-frequency adaptive notch filter). Note that the primary path filter H^ is also referred to as a sound field characteristic filter.

[0032] The noise estimation signal generator 43 filters the reference signal r using the primary path filter H^ to generate a noise estimation signal d^ indicating an estimated value of the noise d. The noise estimation signal generator 43 inputs the generated noise estimation signal d^ to the noise estimation signal inverter 46.

[0033] The primary path update unit 44 adaptively updates the primary path filter H^ of the noise estimation signal generator 43 using an adaptive algorithm such as an LMS algorithm. Specifically, the primary path update unit 44 adaptively updates the primary path filter H^ so that the virtual error signal e1 input from the virtual error signal generator 47 is minimized. In this embodiment, the primary path update unit 44 also employs an adaptive update algorithm (described later).

[0034] The canceling sound estimation signal inverting unit 45 inverts the polarity of the canceling sound estimation signal y^ input from the canceling sound estimation signal generating unit 41. The canceling sound estimation signal inverting unit 45 inputs the canceling sound estimation signal y^ with the polarity inverted to the virtual error signal generating unit 47.

[0035] The noise estimation signal inverting unit 46 inverts the polarity of the noise estimation signal d^ input from the noise estimation signal generating unit 43. The noise estimation signal inverting unit 46 inputs the noise estimation signal d^ with the polarity inverted to the virtual error signal generating unit 47.

[0036] The virtual error signal generator 47 generates a virtual error signal e1 by adding together the error signal e input from the microphone 30, the canceling estimated signal y^ with inverted polarity input from the canceling estimated signal inverter 45, and the noise estimated signal d^ with inverted polarity input from the noise estimated signal inverter 46. The virtual error signal generator 47 inputs the generated virtual error signal e1 to the secondary path updater 42 and the primary path updater 44.

[0037] [Renewal of active noise and vibration reduction devices] Next, a description will be given of the update processing of the active vibration noise reduction apparatus 100 according to this embodiment. The update processing of the active vibration noise reduction apparatus 100 will be described with reference to FIGS.

[0038] Fig. 2 is an explanatory diagram showing the concept of adaptively updating the control signal generation unit of the noise control unit. The active vibration noise reduction device 100 shown in Fig. 2 shows that the control filter W of the control filter unit 11 is adaptively updated by the control update unit 13.

[0039] The control filter unit 11 generates a control signal u using the adaptively updated control filter W, and outputs the generated control signal u to the speaker 20. In response to this, the speaker 20 outputs a cancellation sound y. In this embodiment, the active vibration noise reduction device 100 constantly updates the control filter W, and may stop updating once, for example, the secondary path filter C^ of the secondary path filter unit 12 has converged.

[0040] FIG. 3 is an explanatory diagram showing the LMS algorithm for calculating filter coefficients that minimize the evaluation function.

[0041] In Figure 3, the evaluation function J (e.g., 2) is minimized, the filter coefficient is set using the update amount ΔW using the LMS algorithm, which adjusts the step size parameter μW(t). In a typical LMS algorithm, μ is a fixed value. For example, the algorithm shown in Figure 3 searches for the minimum value along the negative direction of the gradient of the evaluation function J. When the evaluation function J is minimized, the update amount ΔW becomes 0. The algorithm shown in Figure 3 also shows that when the evaluation function J is minimum, the indoor sound pressure (error signal e) after interference between the noise d and the control sound is minimum.

[0042] Here, the direction of adaptive updating of the control filter W is the direction of the angle (∠ΔW) indicated by the arrow of the update amount ΔW. Also, the update amount ΔW is the length (|ΔW|) on the W axis of the arrow shown in Figure 3. Therefore, the direction of adaptive updating of the control filter W depends on the phase of the secondary path filter C^.

[0043] The active vibration noise reduction device 100 according to this embodiment is characterized in that it learns changes in the secondary path filter C^ and automatically adjusts the direction of adaptive updating during control. That is, in this embodiment, the active vibration noise reduction device 100 constantly acquires the error signal e from the microphone 30 to monitor changes in the sound field.

[0044] In other words, the adaptive update algorithm proposed in this embodiment automatically adjusts the step size parameter μW(t) according to the input signal level of the control filter W and the correlation between the input signal and the error signal e.

[0045] Here, the magnitude of the update amount ΔW depends on the amplitude of the error signal e, the reference signal r, and the secondary path filter Ĉ, and the step size parameter μW(t) is automatically adjusted taking into account the level of the error signal e.

[0046] Therefore, in this embodiment, the control filter W is adaptively updated by an update amount ΔW calculated by multiplying the error signal e, a step-size parameter μW(t) calculated based on the error signal e, and the result of the convolution operation of the reference signal r and the secondary path filter C^.

[0047] In this way, the control update unit 13 adaptively updates the control filter W by adopting an adaptive update algorithm that adjusts the step size parameter μW(t) according to the input signal level of the adapted control filter W and the correlation between the input signal and the error signal e.

[0048] First, the step size parameter μW(t) for updating the control filter W is calculated by the following equations (1) and (2).

[0049]

number

[0050]

number

[0051] As a result, the control update unit 13 adaptively updates the control filter W according to the update formula given by the following formula (3).

[0052]

number

[0053] In this way, the control update unit 13 adaptively updates the control filter W of the control filter unit 11, and the control filter W continues to be updated.

[0054] For example, when the passenger seat is reclined, the step-size parameter μW(t) in equation (1) changes in accordance with changes in the secondary path transfer function C and the secondary path filter C^. The active vibration noise reduction device 100 then calculates the update amount ΔW from equation (3) by multiplying the error signal e, the step-size parameter μW(t) calculated based on the error signal e, and the result of a convolution operation between the reference signal r and the secondary path filter C^. In this way, the active vibration noise reduction device 100 adaptively updates the control filter W of the control filter unit 11 using the update amount ΔW.

[0055] More specifically, in equation (1), the step-size parameter μW(t) is adjusted according to the input signal level of the control filter W and the correlation between the input signal and the error signal e. Because the step-size parameter μW(t) has the norm of the signal vector in the denominator, it is inversely proportional to the level of the input signal and proportional to the correlation between the input signal and the error signal e.

[0056] Therefore, when the input signal is small, the step size parameter μW(t) becomes large, and the convergence speed can be maintained according to equation (1). On the other hand, when the input signal is large, the step size parameter μW(t) becomes small, preventing divergence due to an update amount ΔW being too large and ensuring control stability.

[0057] In addition, immediately after the start of control or after a change in the secondary path filter C^, if the reduction in noise d is small, the correlation between the input signal and the error signal e is large and the step size parameter μW(t) is also large, resulting in a fast convergence speed.

[0058] On the other hand, when the control progresses and the noise d is reduced, the correlation between the input signal and the error signal e decreases, so the step size parameter μW(t) also decreases, allowing the filter coefficient of the control filter W to be adjusted with high precision.

[0059] Furthermore, when a disturbance is mixed into the error signal e, the correlation becomes smaller and the step size parameter μW(t) also becomes smaller, resulting in high control stability. For example, this applies to a case where a truck is running alongside the vehicle and the level of the vehicle body vibration signal, which is the input signal, does not change even when the microphone 30 picks up the running sound of the truck running alongside.

[0060] In addition, in equation (2), the control update unit 13 obtains the step size parameter μW(t) by performing a convolution operation between the error signal e, which corresponds to the correlation, and the reference signal r.

[0061] As described above, the active vibration noise reduction device 100 according to this embodiment is configured to include the speaker 20, the microphone 30, the control filter W, and the secondary path filter C^.

[0062] The speaker 20 outputs a cancellation sound y to cancel out the noise d. The microphone 30 generates an error signal e from the noise d and the cancellation sound y. The control filter W generates a control signal u that controls the cancellation sound y from a reference signal r. The control filter W is adaptively updated by an update amount ΔW calculated by multiplying the error signal e, a step-size parameter μW(t) calculated based on the error signal e, and the result of the convolution operation of the reference signal r and the secondary path filter C^ according to equation (3).

[0063] With this configuration, the active vibration noise reduction device 100 automatically calculates the step-size parameter μW(t) in accordance with the magnitude of the error signal e, so it is possible to update the adaptive filter coefficients with optimal values ​​and easily set them in the control filter W. That is, when the error signal e is large, for example, at the beginning of control, the active vibration noise reduction device 100 increases the amount of update, making it possible to improve the convergence speed. On the other hand, when the error signal e is small, for example, after control has converged, the active vibration noise reduction device 100 also reduces the amount of update, making it possible to improve the accuracy of adaptive updating.

[0064] In this way, the active vibration noise reduction device 100 can improve the convergence speed and ensure control stability and control performance. In particular, even when the error signal e contains a disturbance, the active vibration noise reduction device 100 can improve (ensure) stability. Furthermore, since it does not use a convergence coefficient storage table as in Patent Document 4, it is easy to set the filter coefficients in the control filter W of the control filter unit 11.

[0065] Alternatively, the step size parameter μW(t) may be calculated by convolution of the error signal e and the reference signal r, as shown in equation (2).

[0066] With this configuration, the step-size parameter μW(t) can grasp the magnitude of the correlation between the error signal e and the reference signal r by performing a convolution operation using equation (2). Here, a high correlation means that the noise d has not been reduced, and indicates that the noise d is included in the error signal e. Therefore, the active vibration noise reduction device 100 can improve (speed up) convergence by calculating the step-size parameter μW(t) of equation (1) including equation (2) based on the correlation.

[0067] Furthermore, if the error signal e generated by the microphone 30 contains a disturbance, the correlation with the reference signal r decreases, so the step size parameter μW(t) also decreases, improving control stability.

[0068] Furthermore, according to equation (2), the control filter W performs a convolution operation corresponding to the correlation between the reference signal r and the error signal e. In equation (2), the secondary path filter C^ is not used in the correlation operation by convolution of the reference signal r and the error signal e. Therefore, it is possible to perform an operation for each combination of the reference signal r and the error signal e, and similar operations are not repeated for each control channel, thereby suppressing an increase in the amount of calculation due to the correlation operation.

[0069] The step size parameter μW(t) may also be calculated by squaring the error signal e. That is, the step size parameter μW(t) may be calculated by applying the square of the error signal e instead of the convolution of the error signal e and the reference signal r.

[0070] In this case, the step size parameter μW(t) is calculated by the following equation (4) instead of equation (2).

[0071]

number

[0072] According to this configuration, the step size parameter μW(t) is calculated by squaring the error signal e using equation (4), which eliminates the need for a convolution operation between the reference signal r and the secondary path filter C^ compared to equation (2), thereby reducing the amount of calculation.

[0073] As a result, the active vibration noise reduction device 100 can further improve the convergence speed, and can also ensure control stability and control performance.

[0074] Furthermore, the step size parameter μW(t) may be calculated by dividing the value ρW(t) calculated from the error signal e shown in equation (2) in equation (1) by the reference signal r.

[0075] According to this configuration, the step size parameter μW(t) increases when the reference signal r is small, thereby maintaining the convergence speed. On the other hand, the step size parameter μW(t) decreases when the reference signal r is large, thereby preventing divergence caused by an update amount ΔW being too large.

[0076] In addition, the step size parameter μW(t) may be calculated by dividing the value calculated by adding a predetermined second positive number β to the value calculated by the error signal e shown in equation (2) in equation (1) by the value calculated by adding a predetermined first positive number σ to the value calculated by the reference signal r.

[0077] With this configuration, when the reference signal r in equation (1) is small, the step-size parameter μW(t) can be prevented from becoming too large and diverging, and when the reference signal r is large, the step-size parameter μW(t) can be prevented from becoming too small and causing learning to stop.

[0078] In equation (1), the predetermined first positive number σ is set to a small positive number that does not make the denominator too small so that the update amount ΔW does not become too large and the control does not diverge. For example, if you want to prevent the maximum value of the step-size parameter μW(t) from becoming 10 times or more the fixed value μ0 (see equation (2)) without considering the influence of the numerator, you can set the predetermined first positive number σ to, for example, 0.1 or more.

[0079] In addition, in equation (1), the predetermined second positive number β is set to a small positive number so that the numerator is not too small, so that learning does not stop due to a small update amount ΔW. For example, if you want to ensure that the minimum value of the step-size parameter μW(t) does not become equal to or smaller than 0.1 times the set fixed value μ0, without considering the influence of the denominator, you can set the predetermined second positive number β to, for example, 0.1 or more.

[0080] In this way, by setting a predetermined first positive number α and a predetermined second positive number β, the range in which the step size parameter μW(t) changes can be limited.

[0081] The active vibration noise reduction device 100 may further include a primary path filter H^ that indicates an estimated value of the transfer function of the primary path from the noise source d to the microphone 30. The primary path filter H^ may be adaptively updated by an update amount calculated by multiplying a virtual error signal e1 calculated based on the error signal e1 and the cancellation sound y, a step-size parameter μHC(t) calculated based on the virtual error signal e1, and a reference signal r.

[0082] With this configuration, the active vibration noise reduction device 100 automatically calculates the step-size parameter μHC(t) according to the magnitude of the virtual error signal e1, so that the adaptive filter coefficients can be updated with optimal values ​​and easily set in the primary path filter H^.

[0083] Here, as described above, the virtual error signal e1 is generated in the virtual error signal generator 47. Specifically, the virtual error signal generator 47 generates the virtual error signal e1 by adding together the error signal e input from the microphone 30, the canceling estimated signal y^ with inverted polarity input from the canceling estimated signal inverting unit 45, and the noise estimated signal d^ with inverted polarity input from the noise estimated signal inverting unit 46.

[0084] The step size parameter μHC(t) for updating the primary path filter Ĥ will be explained (later) using equations (5) and (6) along with the update of the secondary path filter Ĉ.

[0085] In addition, the secondary path filter C^ may be adaptively updated by an update amount calculated by multiplying a virtual error signal e1 calculated based on the error signal e and the cancellation sound y, a step size parameter μHC(t) calculated based on the virtual error signal e1, and the result of the convolution operation of the reference signal r and the control filter W.

[0086] With this configuration, the active vibration noise reduction device 100 automatically calculates the step-size parameter μHC(t) according to the magnitude of the virtual error signal e1, so that the adaptive filter coefficients can be updated with optimal values ​​and easily set in the secondary path filter C^.

[0087] Here, the step-size parameter μHC(t) used to update the primary path filter H^ and the secondary path filter C^ will be explained. The step-size parameter μHC(t) is calculated by the following equations (5) and (6).

[0088]

number

[0089]

number

[0090] As a result, the primary path update unit 44 adaptively updates the primary path filter H^ of the noise estimation signal generation unit 43 using the update formula given by the following formula (7).

[0091]

number

[0092] Further, the secondary path update unit 42 adaptively updates the secondary path filter C^ of the cancellation estimation signal generation unit 41 according to the update formula given by the following formula (8).

[0093]

number

[0094] In this way, the primary path update unit 44 adaptively updates the primary path filter H^ of the noise estimation signal generation unit 43, and the secondary path update unit 42 adaptively updates the secondary path filter C^ of the cancellation estimation signal generation unit 41.

[0095] As a result, the active vibration noise reduction device 100 automatically adjusts the update amount as shown in equations (7) and (8). That is, when the virtual error signal e1 is large, for example, at the beginning of control, the active vibration noise reduction device 100 increases the update amount, thereby improving the convergence speed. On the other hand, when the virtual error signal e1 is small, for example, after control has converged, the active vibration noise reduction device 100 also decreases the update amount, thereby improving the accuracy of adaptive updating. [Explanation of symbols]

[0096] 10 Noise control section 11 Control filter section 12 Secondary path filter section 13 Control Update Unit 20 speakers 30. Mike 40 Sound Field Learning Section 41 Cancellation sound estimation signal generation unit 42 Secondary Route Update Unit 43 Noise estimation signal generator 44 Primary Route Update Unit 45 Cancellation estimation signal inversion unit 46 Noise estimation signal inversion unit 47 Virtual error signal generator 100 Active vibration and noise reduction device u Control signal r reference signal e error signal e1 Virtual error signal C Secondary path transfer function C^ Secondary Path Filter H Primary path transfer function H^ Primary Path Filter d. Noise d^ Noise estimation signal y cancellation sound y^ Cancellation estimated signal

Claims

1. a speaker that outputs a canceling sound to cancel out noise; a microphone for generating an error signal from the noise and the cancellation sound; a control filter that generates a control signal for controlling the cancellation from a reference signal; a secondary path filter that provides an estimate of a transfer function from the speaker to the microphone; The control filter adaptively updating the error signal by an update amount calculated by multiplying a step size parameter calculated based on the error signal by a convolution operation result of the reference signal and the secondary path filter; The step size parameter is The error signal is calculated by convolution of the error signal and the reference signal. An active vibration noise reduction device characterized by:

2. a speaker that outputs a canceling sound to cancel out noise; a microphone for generating an error signal from the noise and the cancellation sound; a control filter that generates a control signal for controlling the cancellation from a reference signal; a secondary path filter that provides an estimate of a transfer function from the speaker to the microphone; The control filter adaptively updating the error signal by an update amount calculated by multiplying a step size parameter calculated based on the error signal by a convolution operation result of the reference signal and the secondary path filter; The step size parameter is The value obtained by dividing the error signal by the reference signal is obtained; The error signal is calculated by adding a predetermined first positive number to the value calculated from the error signal, and dividing the result by adding a predetermined second positive number to the value calculated from the reference signal. An active vibration noise reduction device characterized by:

3. a first-order path filter that provides an estimate of a transfer function of a first-order path from a noise source to the microphone; The primary path filter comprises: the reference signal is adaptively updated by an update amount calculated by multiplying a virtual error signal generated by adding together the error signal, the cancellation estimation signal with inverted polarity input from the cancellation estimation signal inverting unit, and the noise estimation signal with inverted polarity input from the noise estimation signal inverting unit, a step size parameter calculated based on the virtual error signal, and the reference signal; 3. An active vibration noise reduction device according to claim 1 or 2.

4. The secondary path filter the control filter is adaptively updated by an update amount calculated by multiplying a virtual error signal generated by adding together the error signal, the cancellation estimation signal with inverted polarity input from a cancellation estimation signal inverting unit, and the noise estimation signal with inverted polarity input from a noise estimation signal inverting unit, a step size parameter calculated based on the virtual error signal, and a result of a convolution operation of the reference signal and the control filter; 3. An active vibration noise reduction device according to claim 1 or 2.

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