Patent Grant
9633645
This application claims priority to EP Application No. 14184290.6, filed Sep. 10, 2014, the disclosure of which is incorporated in its entirety by reference herein.
The present invention relates to an active noise control (ANC) system, in particular to an ANC system that is more robust with regard to variations of the secondary path transfer characteristics.
Disturbing noise—in contrast to a useful sound signal—is sound that is not intended to meet a certain receiver, e.g., a listener's ears. The generation process of noise and disturbing sound signals can generally be divided into three sub-processes. These are the generation of noise by a noise source, the transmission of the noise away from the noise source and the radiation of the noise signal. Suppression of noise may take place directly at the noise source by means of damping, for example. Suppression may also be achieved by inhibiting or damping the transmission and/or radiation of noise. However, in many applications, these efforts do not yield the desired effect of reducing the noise level in a listening room below an acceptable limit. Deficiencies in noise reduction can be observed especially in the bass frequency range. Additionally or alternatively, noise control methods and systems may be employed that eliminate or at least reduce the noise radiated into a listening room by means of destructive interference, i.e., by superposing the noise signal with a compensation signal. Such systems and methods are summarized under the term active noise canceling or active noise control (ANC).
Although it is known that “points of silence” can be achieved in a listening room by superposing a compensation sound signal and the noise signal to be suppressed such that they destructively interfere, a reasonable technical implementation was not feasible before the development of cost-effective, high-performance digital signal processors, which may be used together with an adequate number of suitable sensors and actuators.
Current systems for actively suppressing or reducing the noise level in a listening room (known as “active noise control” or “ANC” systems) generate a compensation sound signal with the same amplitude and frequency components for each noise signal to be suppressed, but with a phase shift of 180° with respect to the noise signal. The compensation sound signal interferes destructively with the noise signal; the noise is thus eliminated or damped at least at certain positions within the listening room. These positions in which a high damping of noise is achieved are often referred to as “sweet spots”.
In the case of a motor vehicle, the term noise covers, among other things, noise generated by mechanical vibrations of the engine or fans and components mechanically coupled to them, noise generated by the wind when driving and noise generated by the tires. Modern motor vehicles may comprise features such as so-called “rear seat entertainment”, which presents high-fidelity audio using a plurality of loudspeakers arranged within the passenger compartment of the motor vehicle. In order to improve the quality of sound reproduction, disturbing noise has to be considered in digital audio processing. Besides this, another goal of active noise control is to facilitate conversations between people sitting in the rear seats and the front seats.
Modern ANC systems depend on digital signal processing and digital filter techniques. A noise sensor (for example, a microphone or non-acoustic sensor) may be employed to obtain an electrical reference signal that represents the disturbing noise signal generated by a noise source. This reference signal is fed to an adaptive filter; the filtered reference signal is then supplied to an acoustic actuator (e.g., a loudspeaker) that generates a compensation sound field in phase opposition to the noise within a defined portion of the listening room (i.e., within the sweet spot), thus eliminating or at least damping the noise within this defined portion of the listening room. The residual noise signal may be measured by means of microphones in or close to each sweet spot. The resulting microphone output signals may be used as error signals, which are fed back 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 signals is minimized.
A known digital signal processing method frequently used in adaptive filters is an enhancement of the known least mean squares (LMS) method for minimizing the error signal, or more precisely the power of the error signal. These enhanced LMS methods include, for example, the filtered-x LMS (FXLMS) algorithm (or modified versions thereof) and 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 thereby used to apply the FXLMS (or any related) algorithm. This acoustic transmission path from the loudspeaker to the microphone is usually referred to as the “secondary path” of the ANC system, whereas the acoustic transmission path from the noise source to the microphone is usually referred to as the “primary path” of the ANC system.
In general, ANC systems have multiple inputs (at least one error microphone in each listening position, i.e., sweet spot) and multiple outputs (a plurality of loudspeakers); they are thus referred to as “multi-channel” or “MIMO” (multiple input/multiple output) systems. In the multi-channel case, the secondary paths are represented as a matrix of transfer functions, each representing the transfer behavior of the listening room from one specific loudspeaker to one specific microphone (including the characteristics of the microphone, loudspeaker, amplifier, etc.).
During operation of the ANC system, the transfer characteristics of the secondary paths may be subject to variations. A particular secondary path transfer function may vary due to many different causes: for example, when the number of listeners in the listening room changes, when a person in a listening position moves, when a window is opened, etc. Such variations result in a mismatch between the actual secondary path transfer characteristics and the transfer characteristics in the model used by the aforementioned LMS methods. Such a mismatch may result in stability problems, a reduced damping of the noise and, consequently, smaller sweet spots.
A method for determining an estimation of a secondary path transfer characteristic in an ANC system is described herein. In accordance with one example of the invention, the method includes the positioning of a microphone array in a listening room symmetrically with respect to a desired listening position and reproducing at least one test signal using a loudspeaker arranged within the listening room to generate an acoustic signal. The acoustic signal is measured with the microphones of the microphone array to obtain a microphone signal from each microphone of the microphone array, and a numerical representation of the secondary path transfer characteristic is calculated for each microphone signal based on the test signal and the respective microphone signal. The method further includes averaging the calculated numerical representations of the secondary path transfer characteristic to obtain the estimation of the secondary path transfer characteristic to be used in the ANC system.
The invention can be better understood with reference to the following description and drawings. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the invention. Moreover, in the figures, like reference numerals designate corresponding parts. In the drawings,
An exemplary active noise control (ANC) system improves music reproduction, speech intelligibility in the interior of a motor vehicle and/or the operation of an active headset with the suppression of undesired noises to increase the quality of the presented acoustic signals. The basic principle of such active noise control systems is thereby based on the superposition of an existing undesired disturbing signal (i.e., noise) with a compensation signal generated with the help of the active noise control system and superposed in phase opposition with the undesired disturbing noise signal, thus yielding destructive interference. In an ideal case, complete elimination of the undesired noise signal is thereby achieved.
In a feedforward ANC system, a signal correlated with the undesired disturbing noise (often referred to as the “reference signal”) is used to generate a compensation signal that is supplied to a compensation actuator. In acoustic ANC systems, the compensation actuator is a loudspeaker. However, a feedback ANC system is present if the compensation signal is derived not from a measured reference signal correlated to the disturbing noise, but rather only from the system response. That is, the reference signal is estimated from the system response in feedback ANC systems. In practice, the “system” is the overall transmission path from the noise source to the listening position where noise cancellation is desired. The “system response” to a noise input from the noise source is represented by at least one microphone output signal that is fed back to the compensation actuator (loudspeaker) via a control system, generating anti-noise to suppress the actual noise signal in the desired position. By means of basic block diagrams,
Feedforward systems may encompass a higher effectiveness 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 (i.e., reference signal x[n]) may be directly processed and used to actively counteract disturbing noise signal d[n]. Such a feedforward system is illustrated in
In feedback systems, the effect of a noise disturbance on the system must initially be awaited. Noise suppression (active noise control) can only be performed when a sensor determines the effect of the disturbance. An advantageous effect of feedback systems is that they can thereby be effectively operated even if a suitable signal (i.e., a reference signal) correlating with the disturbing noise is not available to control the active noise control arrangement. This is the case, for example, when applying ANC systems in environments, in which specific information about the noise source is not available (i.e., when no specific noise source is available to which a reference sensor could be assigned).
The principle of a feedback structure is illustrated in
In a practical use of arrangements for noise suppression, such arrangements are implemented to be adaptive, because the noise level and the spectral composition of the noise to be reduced may, for example, also be subject to chronological changes due to changing ambient conditions. For example, when ANC systems are used in motor vehicles, the changes of the ambient conditions can be caused by different driving speeds (wind noises, tire noises), different load states, different engine speeds or one or more open windows. Moreover, the transfer characteristics of the primary and secondary paths may change over time, which will be discussed later in more detail.
An unknown system may be iteratively estimated by means of an adaptive filter. The filter coefficients of the adaptive filter are thereby 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 example, finite impulse response (FIR) or infinite impulse response (IIR) filters), whose filter coefficients are modified according to a given adaptation algorithm.
The adaptation of the filter coefficients is a recursive process that permanently optimizes the filter characteristic of the adaptive filter by minimizing an error signal that is essentially the difference between the outputs of the unknown system and the adaptive filter, wherein both are supplied with the same input signal. If a norm of the error signal approaches zero, the transfer characteristic of the adaptive filter approaches the transfer characteristic of the unknown system. In ANC applications, the unknown system may thus represent the path of the noise signal from the noise source to the spot where noise suppression should be achieved (primary path). The noise signal is thereby “filtered” by the transfer characteristic of the signal path, which—in the case of a motor vehicle—essentially comprises the passenger compartment (primary path transfer function). The primary path may additionally comprise the transmission path from the actual noise source (e.g., the engine or tires) to the car body or the passenger compartment, as well as the transfer characteristics of the microphones used.
The LMS algorithm thereby represents an algorithm for the approximation of the solution to the least mean squares (LMS) problem, as it is often used when utilizing adaptive filters, which are realized, for example, in digital signal processors. The algorithm is based on the method of the steepest descent (gradient descent method) and computes the gradient in a simple manner. The algorithm thereby operates in a time-recursive manner. That is, the algorithm is run again with each new data set, and the solution is updated. Due to its relatively low complexity and low memory requirement, the LMS algorithm is often used for adaptive filters and adaptive control. Further methods may include the following: recursive least squares, QR decomposition least squares, least squares lattices, QR decomposition lattices, gradient adaptive lattices, zero forcing, stochastic gradients, etc.
In active noise control arrangements, the filtered-x LMS (FXLMS) algorithm and modifications or extensions thereof are quite often used as special embodiments of the LMS algorithm. The modified filtered-x LMS (MFXLMS) algorithm is an example of such a modification.
The model of the ANC system of
Input signal x[n] represents the noise signal generated by a noise source and is therefore often referred to as “reference signal”. It is measured by an acoustic or non-acoustic sensor for further processing. Input signal x[n] is transported to a listening position via primary path system 10, which provides disturbing noise signal d[n] as an output at the listening position where noise cancellation is desired. When using a non-acoustic sensor, the input signal may be indirectly derived from the sensor signal. Reference signal x[n] is further supplied to adaptive filter 22, which provides filtered signal y[n]. Filtered signal y[n] is supplied to secondary path system 21, which provides modified filtered signal y′[n] (i.e., the compensation signal); modified filtered signal y′[n] destructively superposes with disturbing noise signal d[n], which is the output of primary path system 10. Therefore, the adaptive filter has to impose an additional 180° phase shift on the signal path. The result of the superposition is a measurable residual signal that is used as error signal e[n] for adaptation unit 23. To calculate updated filter coefficients wk, estimated model S*(z) of secondary path transfer function S(z) is used. This may be required to compensate for the decorrelation between filtered reference signal y[n] and compensation signal y′[n] due to the signal distortion in the secondary path. Estimated secondary path transfer function S*(z) (system 24) also receives input signal x[n] and provides modified reference signal x′[n] to adaptation unit 23.
The function of the algorithm is summarized below. Due to the adaptation process, the overall transfer function W(z)·S(z) of the series connection of adaptive filter W(z) and secondary path transfer function S(z) approaches primary path transfer function P(z), wherein an additional 180° phase shift is imposed on the signal path of adaptive filter 22; disturbing noise signal d[n] (the output of primary path 10) and compensation signal y′[n] (the output of secondary path 21) thus destructively superpose, thereby suppressing disturbing noise signal d[n] in the respective portion (sweet spot) of the listening room.
Residual error signal e[n], which may be measured by means of a microphone, is supplied to adaptation unit 23 and modified input signal x′[n], which is provided by estimated secondary path transfer function S*(z). Adaptation unit 23 is configured to calculate filter coefficients wk of adaptive filter transfer function W(z) from modified reference signal x′[n] (filtered x) and error signal e[k] such that a norm (e.g., the power or L2 norm) of error signal ∥e[k] ∥ becomes minimal. An LMS algorithm may be a good choice for this purpose, as already discussed above. Circuit blocks 22, 23 and 24 form active noise control unit 20, which may be fully implemented in a digital signal processor; together these circuit blocks are referred to as FXLMS ANC filter 20 in the example of
In narrowband ANC applications, acoustic sensor 32 may be replaced by a non-acoustic sensor (e.g., a rotational speed sensor) and a signal generator to synthesize reference signal x[n]. The signal generator may use the base frequency, which is measured with the non-acoustic sensor, and higher order harmonics to synthesize reference signal x[n]. The non-acoustic sensor may be, for example, a rotational speed sensor that gives information on the rotational speed of a car engine, which may be regarded as a main noise source.
The overall secondary path transfer function S(z) comprises the following: the transfer characteristics of loudspeaker LS1, which receives filtered reference signal y[n]; the acoustic transmission path characterized by transfer function S11(z); the transfer characteristics of microphone M1; and the transfer characteristics of necessary electrical components such as amplifiers, analog-digital converters, digital-analog converters, etc. In the case of a single-channel ANC system, only one acoustic transmission path transfer function S11(z) is relevant, as illustrated in
As mentioned above, estimations Svw*(z) of secondary path transfer functions Svw(z) are used by the LMS adaptation algorithms, which regularly calculate updated filter coefficients wv,k for adaptive filter transfer functions Wv(z). The estimations of transfer functions Svw(z) are obtained based on measurements carried out in the listening room in which the ANC system is to be installed. Alternatively, the measurements may be carried out in a listening room that is a replica or a model of the listening room in which the ANC system is to be installed.
Once measured, numerical representations of the secondary path transfer functions are stored (for example, in the memory of a digital signal processor) so they can be used by the adaptive ANC filter (see
The negative effect of a mismatch between the actual secondary path transfer functions Svw(z) and the stored estimations Svw*(z) may at least be alleviated when estimations Svw*(z) are obtained by measurement not with a single microphone but rather with an array of microphones; the estimations obtained with the individual microphones of the array are then averaged to obtain the “final” estimated secondary path transfer function for a particular combination of loudspeaker LSv and the listening position.
The present example illustrated in
With the measurement setup illustrated in
S11*(z)=(S11,1*(z)+S11,2*(z)+ . . . +S11,16*(z))/16. (eq. 1)
The procedure may be analogously repeated for each loudspeaker/listening position combination to obtain estimated secondary path transfer functions Svw(z).
The diagram of
Using a microphone array to measure data for determining estimations of secondary path transfer functions (by averaging) improves the robustness of the ANC system regarding two aspects. First, the estimations obtained by averaging are less susceptible to inexact positioning of the microphones used during the estimation procedure. Second, the performance of the ANC system is less susceptible to variations of the secondary path transfer functions during operation of the ANC system.
Some important aspects of the methods and systems described herein are summarized below. It is understood that the following is not an exhaustive enumeration but rather an exemplary outline. One aspect relates to a method for determining an estimation of a secondary path transfer characteristic in an ANC system. In accordance with one example of the invention, a microphone array is positioned in a listening room symmetrically with respect to a desired listening position (e.g., a seat installed in the passenger compartment of a motor vehicle; see
The microphone array may be placed such that its axis of symmetry is substantially vertical and the desired listening position is on the axis of symmetry. The microphones of the microphone array are arranged substantially in a plane (see
In the case of a multi-channel ANC system, the procedure to determine an estimation of a secondary path transfer characteristic is repeated for each loudspeaker/listening position combination in the listening room. A set of V×W estimations is thus obtained for V loudspeakers LS1, . . . , LSV and W listening positions (defining the sweet spots). Generally, a multi-channel ANC system includes either at least two loudspeakers and at least one listening position or at least one loudspeaker and at least two listening positions. The secondary path estimations are used in an adaptive ANC filter (see
Another aspect of the invention relates to an ANC method for reducing acoustic noise in at least one listening position of a listening room in which at least one loudspeaker is installed. In accordance with one example of the invention, at least one reference signal x[n] that is correlated with the noise is provided. In the case of a feedforward ANC system, only one reference signal is usually used. At each listening position, error signal ew[n] is measured, which represents the (residual) noise at the respective listening position. The reference signal(s) is (are) filtered with an adaptive ANC filter bank to provide, as a filter output signal, compensation signal yv[n] for each loudspeaker LSv (see
As mentioned, the at least one reference signal x[n] that is correlated with the noise may be determined by an acoustic or non-acoustic sensor (see
While various embodiments of the invention have been described, it will be apparent to those of ordinary skill in the art that many more embodiments and implementations are possible within the scope of the invention. Accordingly, the invention is not to be restricted except in light of the attached claims and their equivalents. With regard to the various functions performed by the components or structures described above (assemblies, devices, circuits, systems, etc.), the terms (including a reference to a “means”) used to describe such components are intended to correspond, unless otherwise indicated, to any component or structure that performs the specified function of the described component (i.e., that is functionally equivalent), even if not structurally equivalent to the disclosed structure that performs the function in the exemplary implementations of the invention illustrated herein.
| Number | Date | Country | Kind |
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| 14184290 | Sep 2014 | EP | regional |
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