Patent Grant
8565443
This patent application claims priority to European Patent Application serial number 08 010 843.4 filed on Jun. 13, 2008, which is hereby incorporated by reference in its entirety.
The present invention relates to active noise control and cancelling.
A disturbing noise (also referred to as “noise” or “disturbing sound signals”)—in contrast to a useful sound signal—is sound that is not intended to be heard or perceived, for example, by a listener. In a motor vehicle, disturbing noise may further include sound signals generated by mechanical vibrations of an engine and/or components mechanically coupled thereto (e.g., a fan), wind passing over and around the vehicle, and/or tires contacting, for example, a paved surface. Noise generation may be divided into three sub-processes: (1) generation of noise by a noise source; (2) transmission of noise away from a noise source; and (3) radiation of a noise signal.
Suppression of noise may take place directly at the noise source, for example, by damping. Suppression of noise may also be achieved by inhibiting or damping the transmission and/or the radiation of noise. However, in many applications these methods do not adequately reduce the noise, particularly in a bass frequency range, below an acceptable (or predetermined) limit. Additionally or alternatively, noise control systems and methods may be employed that eliminate or at least reduce the noise radiated into a listening room using a destructive interference (i.e., by superposing the noise signal with a compensation signal). These systems and methods are generally referred to by the term “active noise control” (ANC). However, the feasibility of these systems and methods relies on the development of cost effective, high performance digital signal processors, which may be used together with an adequate number of suitable sensors and actuators.
Typically, active noise suppressing or reducing systems (known as “active noise control” systems) generate a compensation sound signal having the same amplitude and the same frequency components as the noise signal to be suppressed. However, the compensation sound signal has a 180° (one hundred eighty degree) phase shift with respect to the noise signal. As a result, the noise signal is eliminated or reduced, at least at certain locations within the listening room, due to the destructive interference between the compensation sound signal and the noise signal.
Modern motor vehicles may include features such as a “rear seat entertainment” system (e.g., multimedia system) that provides a high-fidelity audio presentation using a plurality of loudspeakers arranged within the passenger compartment of the vehicle. Active noise control systems are used to improve the quality of the sound reproduction of the rear seat entertainment systems. In addition, active noise control systems may help facilitate conversations between persons sitting on the front seats and on the rear seats.
Modern active noise control systems implement digital signal processing and digital filtering techniques. Typically, a noise sensor (e.g., a microphone or a non-acoustical sensor) is used to provide an electrical reference signal representing the disturbing noise signal generated by a noise source. The reference signal is fed to an adaptive filter which supplies a filtered reference signal to an acoustic actuator (e.g., a loudspeaker). The acoustic actuator generates a compensation sound field having a phase opposite to that of the noise signal within a defined portion (“listening position”) of the listening room. The compensation sound field interacts with the noise signal thereby eliminating or at least damping the noise within the listening position. Residual noise within the listening environment and/or the listening room may be measured using a microphone. The resulting microphone output signal is used as an “error signal” and is provided to the adaptive filter, where the filter coefficients of the adaptive filter are modified such that a norm (e.g., the power) of the error signal is minimized.
Disadvantageously, adaptive filters may become unstable, and therefore cannot reliably ensure stability in all listening environments. Consequently, there is a need to continuously monitor the present operational state of the filter, and to make adjustments thereto where an unstable state of operation is detected. This is frequently accomplished using known digital signal processing methods such as an enhanced version of the least mean squares (LMS) method for minimizing error signals. These enhanced LMS methods include, for example, the so-called filtered-x-LMS (FXLMS) algorithm as well as related methods such as the filtered-error-LMS (FELMS) algorithm.
A model that represents the acoustic transmission path from the acoustic actuator (i.e., loudspeaker) to the error signal sensor (i.e., microphone) is used for applying the FXLMS (or any related) algorithm. This acoustic transmission path from the loudspeaker to the microphone is usually referred to as a “secondary path” of the ANC system, whereas the acoustic transmission path from the noise source to the microphone is usually referred to as a “primary path” of the ANC system. The corresponding process for identifying the transmission function of the secondary path is referred to as “secondary path system identification”.
A transmission function (i.e. the frequency response) of the secondary path system of the ANC system may have a considerable impact on the convergence behaviour of an adaptive filter that uses the FXLMS algorithm, and thus on the stability behaviour thereof, and on the speed of the adaptation. The frequency response (i.e., magnitude response and/or phase response) of the secondary path system may be subjected to variations during operation of the ANC system. A varying secondary path transmission function may have a negative impact on the performance of the active noise control, especially on the speed and the quality of the adaptation produced by the FXLMS algorithm. The negative impact is caused when the actual secondary path transmission function is subjected to variations and no longer matches an a priori identified secondary path transmission function that is used within the FXLMS (or related) algorithms.
There is a general need for active noise control with improved speed and quality of adaptation, as well as the robustness of the entire active noise control system. Furthermore there is a need to provide a flexible selection and generation of the reference signal for the FXLMS algorithm.
According to one aspect of the invention, an active noise cancellation system is configured to reduce, at a listening position, the power of a noise signal radiated from a noise source to the listening position. The system includes an adaptive filter, at least one acoustic actuator and a signal processing device. The adaptive filter receives a reference signal representing the noise signal, and provides a compensation signal. The at least one acoustic actuator is configured to radiate an acoustic signal indicative of the compensation signal to the listening position. The signal processing device is configured to evaluate and assess the stability of the adaptive filter.
According to another aspect of the invention, an active noise cancellation system is configured to reduce, at a listening position, the power of a noise signal radiated from a noise source to the listening position. The system includes a filter arrangement and at least one acoustic actuator. The filter arrangement includes a first adaptive filter and an equalization filter. The filter arrangement receives an effective reference signal representing the noise signal, and provides a compensation signal, where a transfer characteristic of the equalization filter is characterized by a first transfer function. The at least one acoustic actuator is configured to radiate the compensation signal to the listening position, where a signal path between the acoustic actuator and the listening position is characterized by a secondary path transfer function, where the product of the first transfer function and the secondary path transfer function matches a given target function.
According to another aspect of the invention, an active noise cancellation method is provided for reducing, at a listening position, the power of a noise signal radiated from a noise source to the listening position. The method includes: providing a reference signal correlated to the noise signal; filtering the reference signal with an adaptive filter to provide a compensation signal; radiating the compensation signal to the listening position; sensing a residual error signal at the listening position; adapting filter coefficients of the adaptive filter as a function of the error signal and the reference signal; and evaluating and assessing the stability of the adaptive filter.
According to another aspect of the invention, an active noise cancellation method is provided for reducing, at a listening position, the power of a noise signal radiated from a noise source to the listening position. The method includes: providing a reference signal correlated to the noise signal; sequentially filtering the reference signal with an adaptive filter and an equalization filter to provide a compensation signal, where a transfer characteristic of the equalization filter is characterized by a first transfer function; radiating the compensation signal to the listening position with an acoustic actuator, where a signal path from the acoustic actuator to the listening position is characterized by a secondary path transfer function, and where the product of the first transfer function and the secondary path transfer function matches a given target function; sensing a residual error signal at the listening position; and adapting filter coefficients of the adaptive filter as a function of the error signal and the reference signal.
Equalization of the frequency response to the value of the transmission function of the overall secondary path of the active noise control arrangement may improve robustness and stability thereof. For example, the equalization may improve the speed and the performance of the adaptation as well as the robustness of the entire active noise control method executed therewith.
A further advantage may arise when a reference signal, which is formed from a combination of the signals from at least two different sensors, is provided to the active noise control arrangement. These sensors may be acoustic and/or non-acoustical sensors.
Still a further advantage may arise, when the reference signal and the residual error signal which is provided to the filtered-x-LMS algorithm, is filtered with an adaptive band-pass filter in such a manner that the algorithm adapts substantially to the harmonic of interest or to the harmonics of an interfering signal with the greatest amplitude.
Robustness is further increased due to the stability detection which allows the system to take opportune actions when unstable states of operation are detected. As a result, the system may reassume a stable state, or at least the adverse effects of instability are alleviated, faster.
The components in the drawings are not necessarily to scale; instead emphasis is placed upon illustrating the principles of the invention. Moreover, in the drawings, like reference numerals designate corresponding parts. In the drawings:
Active noise control systems (“ANC systems”) are used to suppress noise. For example, an ANC system may improve music reproduction or speech intelligibility in an interior of a motor vehicle. In another example, an ANC system may increase the quality of acoustic signals output from an active headset (e.g., a headset including an ANC system). The basic principle of such active noise control arrangements is based on the superposition of an existing undesired interfering signal with a compensation signal. The compensation signal, which has an opposite phase to that of the noise signal, is generated by the ANC system and added to the undesired disturbing noise signal. Ideally, by adding the compensation signal to the noise signal, the noise signal is completely suppressed.
A feedforward control is characterized in that a signal which is correlated to the undesired disturbing noise (also referred to as a “reference signal”) is used for driving a compensation actuator. In acoustic ANC systems, the compensation actuator is a loudspeaker. In contrast, a feedback system is characterized in that the system response is measured and redirected before driving the compensation actuator. In practice, the “system” is the overall transmission path from the noise source to a listening position where noise cancellation is desired (hereinafter referred to as a “listening position”). The “system response” to a noise input from the noise source is represented by at least one microphone output signal that is fed back via a control system to the compensation actuator (e.g., a loudspeaker). The compensation actuator generates “anti-noise” (also referred to a “compensation signal”) for suppressing the actual noise signal in a desired position/location.
It is known in the art that feedforward systems are typically more effective than feedback arrangements, in particular due to the possibility of the broadband reduction of disturbing noises. This is a result of the fact that a signal representing the disturbing noise may be directly processed and used to actively counteract the disturbing noise signal. Such a feedforward arrangement is illustrated in
In feedback systems (see
Referring now to
Noise suppression arrangements are typically adaptive since the noise level and the spectral composition of the noise, which is to be reduced, is generally subjected to chronological changes due to changing ambient conditions. For example, when ANC systems are used in motor vehicles, the ambient conditions may change due to fluctuation of wind and tire noises at different driving speeds, different load states and engine speeds or by one or a plurality of open windows.
It is known in the art that an unknown system may be iteratively estimated by an adaptive filter. The filter coefficients of the adaptive filter are modified such that the transfer characteristic of the adaptive filter approximately matches the transfer characteristic of the unknown system. In ANC applications, digital filters are used as adaptive filters, for examples finite impulse response (FIR) filters or infinite impulse response (IIR) filters whose filter coefficients are modified according to a given adaptation algorithm.
Typically, adaptation of the filter coefficients is a recursive process which, for example, permanently optimizes the filter characteristic of the adaptive filter. This may be accomplished by minimizing an error signal that is essentially the difference between the output of the unknown system and the adaptive filter, wherein both are supplied with the same input signal. The transfer characteristic of the adaptive filter approaches the transfer characteristic of the unknown system where a norm of the error signal approaches zero. Therefore, in ANC applications the unknown system may represent the transmission path (i.e., a primary path) the noise signal travels from the noise source to the spot/location where noise suppression is to be achieved (i.e., the listening position). The noise signal is thereby “filtered” by the transfer characteristic of the primary signal path (i.e., a primary path transfer function) which—in case of a motor vehicle—includes mostly the passenger compartment. The primary path may additionally include the transmission path from the actual noise source (e.g., the engine, the tires, etc.) to the car-body and further into the passenger compartment.
The adaptive filter, which may be implemented in a digital signal processor (“DSP”), uses the LMS algorithm to approximate the solution for least square means problems. The algorithm is based on the “method of the steepest descent” (also referred to as “gradient descent method”) and computes the gradient in a relatively “simple” manner. The algorithm thereby operates in a time-recursive manner. That is, after a first iteration, the algorithm is run through again and the solution is updated with each new data set provided. Due to its relatively small complexity and small memory requirement, the LMS algorithm is often used for adaptive filters and/or for adaptive controls, which may be realized in digital signal processors. Alternative methods may include, but are not limited to, recursive least squares, QR decomposition least squares, least squares lattice, QR decomposition lattice or gradient adaptive lattice, zero-forcing, stochastic gradient and so forth.
In active noise control arrangements, the filtered-x-LMS (FXLMS) algorithm and modifications and extensions thereof may be used as special embodiments of the LMS algorithm. One example of such a modification is the “modified filtered-x-LMS” (MFXLMS) algorithm. The basic structure of the filtered-x-LMS algorithm is illustrated in
The ANC system of
The input signal x[n] represents the noise signal generated by a noise source and therefore is also referred to as a “reference signal”. The input signal x[n] is measured, for example, by an acoustic or non-acoustical sensor and is supplied to the primary path system 10, the adaptive filter 22 and the secondary path estimation system 24. When using a non-acoustical sensor, the input signal may be indirectly derived from the sensor signal. The primary path system 10 provides an output signal d[n]. The adaptive filter 22 provides a filtered signal y[n] having a 180 degree phase shift to that of the input signal x[n]. The filtered signal y[n] is supplied to the secondary path system 21 which provides a modified filtered signal y′[n] that destructively superposes with the output signal d[n] of the primary path system 10. The “result” of the superposition is a measurable residual signal used as an error signal e[n] for the LMS adaptation unit 23. An estimated model of the secondary path transfer function S(z) is used for calculating updated filter coefficients wk. The estimated model compensates for the decorrelation between the noise signal x[n] and the error signal e[n] due to the signal distortion in the secondary path. The estimated secondary path system 24, having a transfer function S′(z), provides a modified input signal x′[n] to the adaptation unit 23.
Functionally, the system in
The adaptivity of the algorithms realized in a digital ANC system, such as the above-mentioned FXLMS algorithm, may cause instabilities therein. Typically, such instabilities are also inherent to many further adaptive methods. These instabilities may, for example, cause self-oscillations of the ANC systems and similar undesired effects which may be perceived as a particularly unpleasant noise such as whistling, screeching, etc. Instabilities may occur in adaptive ANC arrangements which use LMS algorithms for the adaptation of the filter characteristics when the reference signal rapidly changes chronologically, and thus includes, e.g., transient, impulse-containing sound portions. For example, these instabilities may result where the convergence parameter or the step size of the adaptive LMS algorithm is not chosen properly for an adaptation to impulse-containing sounds.
Referring to the ANC system of
The adaptation step-size of the MFXLMS algorithm may be larger than the maximum step-size of the “simple” FXLMS algorithm due to the reduced delay. This results in a faster convergence of the MFXLMS algorithm as compared to the FXLMS algorithm. In addition, the robustness of the system is improved since sensitivity of errors in magnitude and phase in the transfer function S′(z) of the secondary path estimation system 24 is reduced compared to the FXLMS algorithm.
The manner in which both of the input signals are generated depend on the actual application. As set forth above, these input signals may be acoustic signals, which are converted into electric signals by microphones as part of acoustic ANC systems. The electrical representation of the reference signal x[n], which represents the undesired noise signal of a noise source, may also be generated by non-acoustical sensors such as, but not limited to, piezoelectric vibration sensors, revolution sensors in combination with oscillators for synthesizing the reference signal, etc.
Generally, both of the input signals (i.e., the reference signal x[n] and the distorted signal d[n]) are stochastic signals. Where the reference signal is synthesized, it is a composition of sine and cosine waves. In case of acoustic ANC systems, the input signals (e.g., x[n] and d[n]) are noisy measuring signals, i.e. audio signals. The power of the error signal e[n] (e.g., the mean square error (“MSE”)) may be used as quality criterion for the adaptation, where
MSE=E{e2[n]}.
The quality criterion expressed by the MSE may be minimized/reduced using a “simple” recursive algorithm (e.g., the least mean square (LMS) algorithm).
In the LMS method, the function to be minimized is the square of the error. That is, to determine an improved approximation for the minimum of the square of the error, the estimated gradient, multiplied by a constant, is added to the last previously-determined approximation (method of steepest descent). The finite impulse response of the adaptive FIR filter is chosen to be at least as long as (i.e., the filter order must be chosen accordingly) the relevant portion of the unknown impulse response of the unknown system to be approximated, such that the adaptive filter has sufficient degrees of freedom to minimize the error signal e[n]. The filter coefficients are thereby gradually changed in the direction of the negative gradient of the mean square error MSE, wherein convergence parameter “μ” controls the step-size.
A typical LMS algorithm for computing the filter coefficients wi[n] of an Nth-order adaptive FIR filter may be described as follows, whereby in the FXLMS algorithm signal x[n] is replaced by x′[n] (see
wi[n+1]=wi[n]+2·μ·e[n]·x[n−i] for i=0, . . . , N−1.
The updated filter coefficients wi[n+1] correspond to the old filter coefficients wi[n] plus a correction term, which is a function of the error signal e[n] (see
As known in the art, the adaptive filter (i.e., a FIR filter) may be converted to a “Wiener filter” in response to the use of the LMS algorithm, when the following applies for the convergence factor μ:
0<μ<μmax=1/[N·E{x2[n]}],
wherein “N” represents the order of the FIR filter and “E{x2[n]}” represents the expected value of the signal power of the reference signal x[n]. In practice, the convergence parameter μ may be selected such that μ=μmax/10.
The LMS algorithm for adapting the coefficients of the adaptive FIR filter may be summarized as follows:
The convergence parameter μ (i.e., the step size) influences both the speed of convergence of the adaptation filter and the “quality” of the mean-square-error (MSE). For example, the greater the convergence parameter μ is chosen for between individual integration steps, the faster the adaptation filter converges. In another example, the smaller the convergence parameter μ is chosen, the smaller the eventual deviation is to the iteratively approached target value (i.e., the smaller the error signal e[n] attained by the adaptive filter becomes). A small error signal e[n], ideally an error signal e[n]=0, is desirable so as to attain the most effective noise reduction (i.e., the most complete elimination of the error signal in the listening position). However, the smaller the convergence parameter μ is chosen, the greater number of iteration steps may be needed for approaching the desired target value. As a result, the required convergence time of the adaptive filter may increase. As a result, in practice, a compromise is struck between (1) the quality of the approach to the target value and (2) the quality of the attainable noise reduction and of the speed of the adaptation of the underlying algorithm when selecting the convergence parameter μ.
In view of the desired attainable accuracy of the adaptation of the active noise control arrangement, a relatively small step size μ may be chosen. However, an undesirable effect of a small step size μ is that the adaptation of the LMS algorithm cannot adapt itself in a sufficiently rapid manner to correct for a rapidly changing reference signal/noise signal. Such rapid changes may be due to transient, impulse-containing sound portions. As a result, an elimination may not reduce the impulse-containing sound portions to the desired extent. Under some circumstances, as set forth above, a small step size μ may lead to an undesired instability of the entire adaptive active noise control arrangement in response to rapidly changing signals.
The quality of the estimation (i.e., the transmission function S′(z), see
Instabilities may still occur even with the improved MFXLMS algorithm, for example, where the ambient conditions in an interior of a motor vehicle change during operation. For example, the opening of a window while the vehicle is driving (i.e., moving) may considerably change the acoustic environment and thus also the transmission function of the secondary path of the active noise control arrangement, among other things. This change may further lead to an instability of the entire arrangement.
In such a case, the transmission function of the secondary path may no longer be approximated to a sufficiently high degree by using the a priori determined estimation, as may be used in the systems of
The dynamic system identification of the secondary path system may be realized using an adaptive filter arrangement, which is connected in parallel to the secondary path system (see
Referring now to
The base frequency calculation unit 33 may extract the base frequency f0 of the noise signal from the output of the non-acoustical sensor (e.g., the revolution sensor connected to the engine) or, additionally or alternatively, from the error signal e[n], a simulated primary path output d′[n], or a filtered primary path output d′0[n]. The simulated primary path output d′[n] is generated by adding the output signal y″[n], estimated by the secondary path system 24, and the measured residual error signal e[n]. In contrast to the system of
In modern automobiles the sensor signal from the revolution sensor 32′ may be provided as a digital bus signal via, for example, a CAN-bus with a relatively low sampling rate (e.g., about 10 samples per second). This low sampling rate may result in poor noise damping performance of the ANC system (e.g., slow reactions to rapid changes of rotational speed and thus rapid changes in the reference/noise signal x[n]). To avoid such adverse effects, the base frequency may be extracted from other suitable signals, for example, from the aforementioned simulated primary path output signals d′[n], d′0[n] using, for example, adaptive notch filters, Kalman frequency trackers or other suitable systems and/or methods.
The secondary path estimation system 50 includes an adaptive filter 51, a LMS adaptation system 52 and a measurement signal generator 53. The adaptive filter 51 is connected in parallel to the transmission path of the sought secondary path system 21. A measurement signal m[n] is generated by a measurement signal generator 53 and superposed (i.e., added) to the compensation signal y[n] (i.e., to the output signal of the adaptive filter 22). The output signal m′[n] of the adaptive filter 51 is subtracted from the microphone signal providing the resulting residual signal e[n]. The residual signal e[n] is used as an error signal for calculating updated filter coefficients gk[n] for the adaptive filter 51. The updated filter coefficients gk[n] are calculated using the LMS adaptation unit 53. The transfer function G(z) of the adaptive filter 51 follows the transfer function S(z) of the secondary path 21, for example, even where the transfer function S(z) varies over time. The transfer function G(z) may be used as an estimation S′(z) of the secondary path transfer function within the FXLMS algorithm.
It may be desirable to dynamically adjust the measuring signal m[n] with reference to its level and its spectral composition such that even though it covers the respective active spectral range of the variable secondary path (i.e., system identification), it is, at the same time, inaudible in the listening position for listeners. This may be attained in that the level and the spectral composition of the measuring signal are dynamically adjusted in such a manner that this measuring signal is always reliably covered or masked by other signals, such as speech or music.
The arrangement for the dynamic approximation of the transmission function of the secondary path of an ANC system (e.g., the secondary path estimation system 50 of
Depending on the application, it may be necessary to continuously determine the present operational state, regarding stability, of the ANC system which, for example, may not include an adaptive dynamic system identification of the secondary path. Further, it may be necessary to identify “stable” and “unstable” states of the ANC system. From these identified states, appropriate actions may be taken, which may include, for example, a temporary shutdown of the ANC system. By taking appropriate actions, it is possible to implement technically less complex and more cost effective ANC systems, for example, without a dynamic system identification of the secondary path, while being able to reliably ensure, in the case of unstable operating states, that the unstable states may be identified and that corresponding actions may be initiated.
The ANC system of
As set forth with reference to
The compensation signal y[n] is supplied to the secondary path system 21 whose output signal y′[n] destructively superposes with the output signal d[n] provided by the primary path system 10 by phase shifting the signal path by 180° (degrees). The “result” of the superposition is a measurable residual signal that is used as an error signal e[n] for the adaptation unit 23. An estimated model of the secondary path transfer function S(z) is used, as set forth with reference to
In addition to the elements in
The error signal e[n], the estimated noise signal d′[n] and the estimated compensation signal y″[n] are each supplied to signal processing units 41, 42, and 43, respectively. The signal processing units 41, 42, 43 are adapted to perform functions such as, but not limited to, band-pass filtering, Fourier-transforming, signal power estimating, etcetera.
The outputs of the signal processing units 41, 42, 43 are connected to corresponding inputs of a decider unit 50, which is connected downstream thereof. The decider unit 50 provides a control signal “ST” to the LMS adaptation unit 23.
The ANC system and at least part of the functional blocks are implements using of one or more digital signal processors. In alternate embodiments, the ANC system and the functional blocks may be implemented using analog circuits or a hybrid of digital and analog devices/systems.
The acoustic reference signal x[n] (i.e., the noise signal) of signal source 31, which is converted into an electric signal xe[n] by the acoustic sensor 32, may be processed in a narrow-band or broad-band manner or its spectral composition may be changed, for example, by filtering. Of course, as already discussed with reference to
In addition to the acoustic transmission path 212 (having a transmission function S1(z)) and the electro-acoustic transducer 212 (e.g., loudspeaker), the secondary path system 21 may include corresponding amplifiers (not shown), and, where appropriate, digital-to-analog (“D/A”) and analog-to-digital (“A/D”) converters (not shown). The distorting effects of the at least one microphone 33 and, for example, subsequent amplifiers and A/D or D/A converters may also be included in the secondary path system 21. That is, the secondary path transfer function S(z) may take into account the distorting effects of the overall signal path from the output signal y[n] of the adaptive filter 22 to the error signal e[n] provided by the microphone 33 for the disturbing noise d[n] equal zero.
As a function of the operating state, which is determined by the decider unit 50, certain parameters of the ANC system may subsequently be influenced, for example, to counteract the danger of an unstable operating state, to increase the adaptation speed and the adaptation accuracy, or, to shut down the active noise control arrangement. The results of the evaluation performed by the decider unit 50, via the output signal ST, are available for optional control of other components of the overall ANC system via line 51, for example external components.
x[n]=u[n]·sin(2π·f0·n/fSAMP),
wherein “u[n]” is the Heaviside function (i.e., unity step), “f0” is the base frequency of the disturbing noise (see
The time in the unit iteration steps (e.g., 0 to 5500 iteration steps) are plotted on the abscissa (i.e., the x-axis), while the normalized signal power of the respective signals is plotted on the ordinate (i.e., the y-axis). As illustrated, the signal d′[n] rises from the value 0 in iteration step 0 after approximately 2000 iteration steps to a stable value (e.g., 1) after the turn-on procedure and after the onset of the iteration of the system, respectively.
The error signal e[n] initially increases in the same manner as the signal d′[n], since during the first approximately 300 iteration steps, it is not yet possible to provide a compensation signal y[n] for destructively superposing to the disturbance signal d[n] using the adaptive filter and the FXLMS algorithm of the ANC system. Furthermore, from
With the rise of the secondary path output signal y″[n], the error signal e[n] decreases during the same time interval from approximately iteration step 300 to iteration step 4500, and asymptotically reaches zero in the steady state of the adaptive filter 23 of the exemplary ANC system of
A conclusion about the stability of the ANC system of
Variable A may represent a relation between the error signal e[n] and the (simulated) disturbance signal d′[n], for example where A=E{e[n]2}/E{d′[n]2}, and thus represents the quality of the active noise cancellation. The operator “E{e[n]2}” represents the expected value of the power of a signal e[n], wherein the expected value is calculated by averaging (see
Variable B may represent a relation between the (simulated) disturbance d′[n] and the (simulated) secondary path output signal y″[n], for example where B=E{y″[n]2}/E{d′[n]2}. Since after a successful adaptation of the adaptive filter 22 (see
Variable C may represent a relation between the error signal e[n] and the (simulated) secondary path output signal y″[n], for example where C=min{1, E{e[n]2}/E{y″[n]2]}, and thus represents another way of characterizing the actual attenuation of the disturbance signal d[n] (and d′[n] respectively). After a successful adaptation of the adaptive filter 22 (see
The stability variables A, B, and C are evaluated in the decider unit 50 for determining whether the ANC system is operating in a stable state of operation. For this purpose the following conditions may be evaluated:
The ANC system is regarded as stable where one of the above three conditions is evaluated as “true” by the decider unit 50. In contrast, the ANC system is regarded as unstable where none of the above conditions are met (i.e., evaluated as “true”).
Referring to
In order to distinguish short transients from undesired instabilities, counteracting actions are, for example, only taking where the ANC system operates in an instable state of operation for more than a given time span. In practice, the stability variables A, B, C and the above conditions for stability (condition 1 to 3) are not continuously (i.e. at every sampling instance) evaluated. Rather, the stability variable A, B and C are evaluated for intervals which are relatively longer than a typical sampling interval, for example, in intervals of about 0.5 ms to 2 ms (e.g. 1500 samples per second).
Actions may be taken where the system is evaluated as unstable, for example, at every time instance where stability is evaluated. In order to make the system more robust, a counter may be increased where the system is evaluated as unstable and decreased where evaluated as stable and the counter exceeds a predefined maximum value, actions are taken against instability. This algorithm may be written as follows:
In the above example “COUNTER” is the counter variable, “UNSTABLE” is a variable which is set to a positive value (e.g., 1) where the system is evaluated as unstable and to a negative value (e.g., −1) where the system is evaluated as stable. It will be clear to a person skilled in the art that many equivalent algorithms exist that fulfil the same function as the one above.
The ANC system may be muted to counteract against instability. Furthermore, the unstable state of operation may be signalled via the status signal ST (see line 51 in
In addition, the step-size μ or the leakage parameter λ of the LMS algorithm may be modified such that the algorithm becomes more robust where there is an unstable state of operation of the ANC system. In this case the above-mentioned step 5 of the LMS algorithm may be expressed as follows:
Other useful measures may be taken too. Furthermore, different measures may be taken depending on how long the instable state of operation lasts (i.e., at different values of the counter variable COUNTER). In the present example, the possible counteracting measures have different priority wherein the last and strongest measure, namely to mute the ANC system, may be the last action where other measures (e.g., modification of step size and leakage parameter) are not effective.
As set forth above, the shape of the secondary path transfer function is directly proportional to the performance and the stability of the FXLMS or MFXLMS algorithms used within the active noise cancellation system. To improve stability of and avoid unstable states of operation of the ANC, the “effective” secondary path transfer function may be equalized by a transfer function C(z) of a compensation filter 26 connected upstream to the “real” secondary path system 21 (see FIGS. 7 and 11-13). For equalization, the actual secondary path transfer function S(z) is estimated, for example, as set forth above with respect to
The output signal of the sensors 32′ (e.g., rotational speed sensors) may include information on the base frequency of both the reference signal x[n] and its electrical representation xe[n]. As a result, the output signal a[n] of the sensor 32′ typically may not be directly superposed. Therefore, a second reference signal a′[n], mixed with the reference signal xe[n], is generated by the oscillator 29 whose oscillation frequency (or frequencies) are controlled by a “base frequency extractor” 28 that receives the second input signal a[n]. The base frequency extractor 28 determines the fundamental frequency f0 of the second input signal a[n] and controls the oscillation frequency of the oscillator 27. Thus, the second reference signal a′[n] includes the base frequency f0 and is strongly correlated with the reference signal xe[n]. Alternatively, the oscillator 29 may provide a superposition of harmonic oscillations of the base frequency f0 and higher order harmonics.
The adder 27 is connected downstream to the acoustic sensor 32, receiving the electric reference signal xe[n] and providing a modified reference signal xe*[n]. In the present example, the “effective” reference signal xe*[n] is supplied to the adaptive filter 22, which takes the place of the reference signal xe[n] in the previous examples.
The use of a weighted superposition of two reference signals (e.g., xe[n] and a[n]) for generating the effective reference signal xe*[n] has several advantages. The first reference signal xe[n] may be a broadband sensor signal representing the noise generated by the noise source 31, whereas the second reference signal a′[n] may be a narrow-band representation of the noise generated by the noise source 31. Therefore, the second reference signal a′[n] may be generated by an oscillator or a synthesiser controlled by signal a[n] (see
The output signal y[n] of the adaptive filter 22 is supplied to the secondary path compensation filter 26, which is connected upstream to the secondary path 21 (i.e., the loudspeaker 210). In order to provide a proper function of the FXLMS algorithm for optimizing the filter coefficients of the adaptive filter 22, another secondary path compensation filter 26 is used upstream of the estimated secondary path system 24 in the signal path supplying the filtered effective reference signal xe*[n] to the LMS adaptation unit 23.
In the present example, the dynamic secondary path estimation system 50 works similarly to the estimation system 50 of
The compensation filters are adapted to compensate the effects (e.g., magnitude, phase or magnitude and phase) of the secondary path 21 (or system 21′). Ideally, the transfer function C(z) of the compensation filters 26 is equal to the inverse of S(z) (i.e., C(z)=S−1(z)), where S(z) is the secondary path transfer function. In practice, the transfer function S−1(z) is calculated from the estimated secondary path transfer function S′(z). Alternatively, for example, only the magnitude response |S(z)| of the estimated secondary path transfer function may be considered, and the transfer function C(z) of the compensation filters 26 may be calculated as C(z)=|S(z)|−1 plus, optionally, an additional time delay to ensure causality of the compensation filter. In still another embodiment, only the phase response arg {S(z)} of the estimated secondary path transfer function is inverted. It should be noted that the estimated secondary path transfer function S′(z) is not necessarily invertible (i.e., the inverted filter S−1(z) is not necessarily causal). Thus, to ensure causality, an additional time delay may have to be added to the compensation filter 26.
In the present narrow band version of the ANC system, the implementation of the adaptive filters 22 and the compensation filters 26 is easier and less computational power is required during operation of the system. In contrast to the broad band version (see
xu[n]=Σu{cos(2π·uf0·n/fSAMP)+j·sin(2π·uf0·n/fSAMP)},
where u=1, . . . , U, and U is the order of the highest harmonic. This signal is provided by the oscillator 29 which generates orthogonal oscillations (i.e., sine and cosine components at the base frequency and each harmonic). The adaptive filter 22 may be characterised by U complex coefficients Wu, and the compensation filter 26 may be characterised by U complex coefficients Cu. Note that one embodiment of implementing the serial connection of adaptive filter 22 and compensation filter 26 is explained later with reference to
The complex filter coefficients of the compensation filter are calculated by the coefficient extraction unit 25 from the estimated secondary path transfer function S′(z)=G(z) as follows:
The secondary path compensation allows the FXLMS algorithm to converge faster, and thus increase the adaptation speed and the performance of the entire system. That is, the pre-filtering of the effective reference signal xe*[n] in the signal path upstream to the LMS adaptation unit may be omitted where an ideal compensation of the secondary path is achieved (i.e., where the condition C(z)S′(z)=1 is true). This is particularly true for narrow band ANC systems using the complex calculation as described above. This is a further improvement of the overall ANC system performance since the inevitable delay due to the pre-filtering is avoided or reduced.
In broad band systems, when using FIR filters, the product C(z)S′(z) may, for example, always include a time delay, since otherwise the compensation filter C(z) would not be causal. However, a flat magnitude response |C(z)S′(z)≈1 may also have positive effects on the overall performance of the system, especially where the magnitude response of the secondary path includes significant peaks and/or notches.
Optionally, a band-pass filter 15 may be arranged in the signal paths upstream to the LMS adaptation unit 23. The band-pass filter 15 has a number of “U” pass bands with corresponding center frequencies where fu=u·f0. In the example of
Until now, the ANC systems have been illustrated as single channel systems having one reference signal, one actuator (loudspeaker), and one microphone located in the listening position (i.e., the listening location where noise cancellation is desired). However, the above described innovations for improving robustness by improving stability (see
The LMS adaptation unit is adapted to execute a multi-channel filtered-x-LMS (FXLMS) adaptation algorithm, where the reference signal xu[n] is pre-filtered with the estimated secondary path transfer function S′vw(z), wherein each of the U vector components of the signal xu[n] is filtered with each of the V·W transfer functions of S′vw(z) yielding a number of U·V·W filtered-x samples in each of the adaptation steps which are processed by the LMS adaptation unit 23.
When using a narrow band ANC system, the MIMO filtering may be replaced by a complex multiplication for each considered harmonic of the reference signal xu[n], as already explained with reference to
Although various examples to realize the invention have been disclosed, it will be apparent to those skilled in the art that various changes and modifications may be made which will achieve some of the advantages of the invention without departing from the spirit and scope of the invention. Especially all the embodiments explained by example of a single-channel ANC system may be configured as multi-channel ANC systems. Furthermore it may be useful to combine the stability detection (see
It will be obvious to those reasonably skilled in the art that other components performing the same functions may be suitably substituted. Such modifications to the inventive concept are intended to be covered by the following claims. Furthermore the scope of the invention is not limited to automotive applications, but may also be applied in any other environment (e.g., in consumer applications like home cinema or the like, and also in cinema and concert halls or the like).
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