The present invention relates to the technology field of environment noise attenuating, and more particularly to a design method for feedforward active noise control system.
The development of technology along with the advancement in science helps to bring in fast industrial manufacture, having good transport facilities and high tech electronic products, but also leads noise pollution to blanket the living environment. It should be known that, sound (noise) is measured in a unit called decibel (dB) or A-weighted decibels (dBA). For example, sound produced by an ordinary conversation is about 60 dBA. On the other hand, by using a decibel meter, it is able to know that the fridge noise and the air conditioner sound level are both around 60 dBA. Moreover, noises make by car horns, railway train, police sirens and take-off of airplanes are measured in a range between 100 dBA and 130 dBA. Not only that, there are also noise pollution blanketing the rural environment, including noise of leaf blower operation (˜110 dBA), noise of grain dryer operation (82-102 dBA) and noise of manure spreader operation (90-105 dBA).
From above descriptions, it is understood that, how to effectively attenuate environmental noises have now become an important issue. Currently, passive noise control (PNC) and active noise control (ANC) are two principal noise attenuating ways, and the ANC technique has been widely applied in noise attenuation because of the good development of adaptive signal processing techniques and digital signal processors (DSPs). For example, Hyundai motor company utilizes the ANC technique to attenuate engine noise, and Noctua (company) applies the ANC technique in noise attenuation of radiator fan.
During a normal operation of the ANC system 1′, however, the causality constraint will be violated in case of the acoustic/electric delays in the ANC system 1′ exceeding the acoustic delay of the primary path. As a result, the noise attenuating performance of the ANC system 1′ dramatically degrades as the degree of noncausality increases. Thus, the positions of the noise source, the reference microphone 1RM′ and the error microphone 1EM′ are critical when designing and manufacturing the ANC system 1′ in order to improve the noise attenuating performance.
As explained in more detail below, the primary path starts at the position of the reference microphone 1RM′ and ends at the position of the error microphone 1EM′. On the other hand, ANC technique follows the principle of the destructive wave interference, reducing an unwanted acoustic noise generated by a primary source through an anti-noise produced by a secondary source. The secondary path is composed by the transfer functions of the error microphone 1EM′, the pre-amplifier 13′, the anti-aliasing filter 14′, the analog-to-digital converter (ADC) in the DSP chip 1DP′, the digital-to-analog converter (DAC) in the DSP chip 1DP′, the reconstruction filter 11′, the power amplifier 12′, the loudspeaker 1LS′, and the acoustic path from loudspeaker 1LS′ to error microphone 1EM′. Therefore, computing the secondary path's transfer function (i.e., S(z)) lead the computing loading of the DSP chip 1DP′ to become heavy. As a result, not only the DSP chip 1DP′ needs spending even more time to achieve the convergence of the ANC computing, but also the adaptive filter is updated to be a high-order filter. However, heavy computing loading of the adaptive algorithm would enlarge the electronic delay in case of the design of the ANC system is in consideration of the causality constraint of the acoustic delay of the primary path and the electronic delay of the secondary path.
From above descriptions, it is understood that there are rooms for improvement in the conventional ANC system 1′. In view of that, inventors of the present application have made great efforts to make inventive research and eventually provided a design method for feedforward active noise control system.
The primary objective of the present invention is to disclose a design method for feedforward active noise control system. In which, two noise collecting systems are adopted for collecting a real environmental noise so as to generate a first reference signal, a target signal and a second reference signal. Subsequently, based on the target signal and the second reference signal, a first adaptive system identifying unit is enabled to complete a first system identification process for producing a first adaptive filter, and then a second adaptive system identifying unit is enabled to complete a second system identification process for producing a second adaptive filter. Consequently, after the second adaptive filter is converted to a low-order digitally-controlled filter by using a system identification tool, the digitally-controlled filter is implemented into a DSP chip of a feedforward active noise control system. Thus, after the digitally-controlled filter is implemented into the DSP chip, it is able to find that not only the computing loading of the DSP chip is significantly lowered while an adaptive algorithm executes an active noise control computing, but also the feedforward active noise control system exhibits a broad frequency bandwidth noise cancelling ability.
In order to achieve the primary objective of the present invention, inventors of the present invention provides an embodiment of the design method for feedforward active noise control system, comprising following steps:
(1) recording a real environmental noise;
(2) establishing a first noise collecting system to collect a real environmental noise, thereby generating a first reference signal and a target signal;
(3) letting the first noise collecting system transmits the first reference signal and the target signal to a first system identifying unit having one first adaptive filter, and then enabling the first system identifying unit to complete an adaptive system identification of the first adaptive filter;
(4) establishing a second noise collecting system to collect the real environmental noise, thereby generating a second reference signal and the target signal;
(5) letting the first noise collecting system transmits the second reference signal and the target signal to a second system identifying unit having the at least one first adaptive filter and a second adaptive filter, and then enabling the second system identifying unit to complete an adaptive system identification of the second adaptive filter;
(6) converting the second adaptive filter to a low-order digitally-controlled filter by using a system identification tool; and
(7) establishing a feedforward active noise control system, comprising: a digital signal processor (DSP) unit, a first analog-to-digital (A/D) converter coupled to the DSP unit, a first microphone coupled to the first A/D converter, a digital-to-analog (D/A) converter coupled to the DSP unit, a loudspeaker coupled to the D/A converter, a second analog-to-digital (A/D) converter coupled to the DSP unit, and a second microphone coupled to the second A/D converter, wherein the DSP unit is provided with the low-order digitally-controlled filter therein.
In one embodiment, the second noise collecting system comprises:
In one embodiment, the first noise collecting system also comprises one noise source, one second pre-amplifier, a first A/D conversion circuit, and a second A/D conversion circuit, and further comprises:
In one embodiment, the first system identifying unit comprises:
In one embodiment, the second system identifying unit comprises:
In one embodiment, the system identification tool is a mathematical program, and the mathematical program is C programming language.
In one embodiment, the first adaptive filter and the second adaptive filter are both selected from the group consisting of finite impulse response (FIR) filter and infinite impulse response (IIR) filter, and the low-order digitally-controlled filter is an infinite impulse response (IIR) filter.
The invention as well as a preferred mode of use and advantages thereof will be best understood by referring to the following detailed description of an illustrative embodiment in conjunction with the accompanying drawings, wherein:
To more clearly describe a design method for feedforward active noise control system disclosed by the present invention, embodiments of the present invention will be described in detail with reference to the attached drawings hereinafter.
The present invention discloses a design method for feedforward active noise control system. In which, two noise collecting systems are adopted for collecting a real environmental noise so as to generate a first reference signal, a target signal and a second reference signal. Subsequently, based on the target signal and the second reference signal, a first adaptive system identifying unit is enabled to complete a first system identification process for producing a first adaptive filter Ŝ(z), and then a second adaptive system identifying unit is enabled to complete a second system identification process for producing a second adaptive filter W′(z). Consequently, after the second adaptive filter W′(z) is converted to a low-order digitally-controlled filter W(z) by using a system identification tool, the digitally-controlled filter W(z) is implemented into a DSP chip of a feedforward active noise control system. Thus, after the digitally-controlled filter W(z) is implemented into the DSP chip, it is able to find that not only the computing loading of the DSP chip is significantly lowered while an adaptive algorithm executes an active noise control computing, but also the feedforward active noise control system exhibits a broad frequency bandwidth noise cancelling ability.
Engineers skilled in development and manufacture of active noise control (ANC) systems certainly know that, the ANC system is commonly designed to form a quiet zone by taking the error microphone as a center of the quiet zone. Therefore, for an earbud that integrated with one ANC system, to form a quiet zone in an inner ear of a user makes the earbud exhibit the best noise attenuating effect. In fact, however, it is impossible to let the earbud has an error microphone disposed in the inner ear of the user. For above reason, the present invention discloses a design method for feedforward active noise control system, which utilizes virtual sensing technique to transfer the quiet zone from the center of the error microphone to the inner ear of the user.
Subsequently, step S2 is executed so as to establish a first noise collecting system NC2 to collect a real environmental noise, thereby generating a first reference signal and a target signal.
As described in more detail below, the digital signal processor DCp is provided with a A/D converter, a DSP unit, and a D/A converter therein, wherein the A/D converter is coupled to the noise source 2, and the D/A converter is coupled to the audio broadcasting device AB. Moreover, in the second noise collecting system NC2, the first A/D conversion circuit AD1 is coupled to the noise source 2, and is configured for converting the audio signal of the real environmental noise to a first reference signal xS(n). On the other hand, the second A/D conversion circuit AD2 is coupled to the second pre-amplifier PA2, and is configured for converting the first audio signal to a target signal d(n).
Subsequently, the method flow proceeds to step S3, so as to let the first noise collecting system NC2 transmits the first reference signal xS(n) and the target signal d(n) to a first system identifying unit AI1 having one first adaptive filter Ŝ(z), and then enabling the first system identifying unit AI1 to complete an adaptive system identification of the first adaptive filter Ŝ(z). As
During the normal operation of the first system identifying unit AI1, the first adaptive filter Ŝ(z) produces a first output signal yS(n) based on the first reference signal xS(n), and the first digital subtracter A1 subsequently applies a subtraction operation to the first output signal yS(n) and the target signal d(n) so as to produce a first error signal eS(n). Thus, first adaptive algorithm unit ALc1 adaptively modulates at least one filter parameter of the first adaptive filter Ŝ(z) according to the first reference signal xS(n) and the first error signal eS(n), thereby making the first error signal eS(n) approach zero. In a practicable embodiment, the first adaptive algorithm unit ALc1 is an algorithm, such as least mean square (LMS) algorithm, normalized least mean square (NLMS) algorithm or Filtered-x LMS algorithm. Of course, the first adaptive algorithm unit ALc1 provided in the first system identifying unit AI1 is not limited to be the forgoing LMS, NLMS or Filtered-x LMS. In other words, engineers skilled in development and manufacture of ANC system should know that, there are many other mathematical algorithms suitable for being used as the first adaptive algorithm unit ALc1. On the other hand, the first adaptive filter Ŝ(z) can be a finite impulse response (FIR) filter or an infinite impulse response (IIR) filter.
For example, when using LMS algorithm as the first adaptive algorithm unit ALc1 so as to be provided in the first system identifying unit AI1, it utilizes following mathematical formulas to complete the adaptive system identification of the first adaptive filter Ŝ(z):
e
S(n)=d(n)−yS(n); and (II)
Ŝl(n+1)=Ŝl(n)+μxS(n−1)eS(n). (III)
In the above-listed mathematical formulas, yS(n) is the first output signal, d(n) is the target signal, xS(n) is the first reference signal, eS(n) is the first error signal, Ŝl(n) is a weight vector, μ is a step size of the first adaptive filter Ŝ(z), and L is a filter length of the first adaptive filter Ŝ(z). It is understood that, the first system identifying unit AI1 is adapted for completing an adaptive system identification of the first adaptive filter Ŝ(z). During the execution of the adaptive system identification, the first adaptive algorithm unit ALc1 adaptively modulates at least one filter parameter of the first adaptive filter Ŝ(z) according to the first reference signal xS(n) and the first error signal eS(n), thereby making the first error signal eS(n) approach zero. After the adaptive system identification of the first adaptive filter Ŝ(z) is completed, an estimated transfer function of the secondary path S(z) is acquired (i.e., the first adaptive filter Ŝ(z)).
Subsequently, the method flow proceeds to step S4, so as to establish a second noise collecting system NC1 to collect the real environmental noise, thereby generating a second reference signal x(n) and the target signal d(n).
The first audio collecting device AC1 is disposed at a position for being faced a non-audio broadcasting side of an audio broadcasting device AB, so as to collect the audio signal of the real environmental noise. As
As
Subsequently, method flow proceeds to step S5, letting the first noise collecting system NC2 transmits the second reference signal x(n) and the target signal d(n) to a second system identifying unit AI2 having the at least one first adaptive filter Ŝ(z) and a second adaptive filter W′(z), and then enabling the second system identifying unit AI2 to complete an adaptive system identification of the second adaptive filter W′(z). As
During the normal operation of the second system identifying unit AI2, the second digital subtracter A2 applies a subtraction operation to the third output signal y′(n) and the target signal d(n), so as to produce and transmit a second error signal e(n) to the second adaptive algorithm unit ALc2. Thus, the second adaptive algorithm unit ALc2 adaptively modulates at least one filter parameter of the second adaptive filter W′(z) according to the third reference signal x′(n) and the second error signal e(n), thereby making the second error signal e(n) approach zero.
In a practicable embodiment, the second adaptive algorithm unit ALc2 is an algorithm, such as least mean square (LMS) algorithm, normalized least mean square (NLMS) algorithm or Filtered-x LMS algorithm. Of course, the second adaptive algorithm unit ALc2 provided in the second system identifying unit AI2 is not limited to be the forgoing LMS, NLMS or Filtered-x LMS. Of course, engineers skilled in development and manufacture of ANC system certainly know that, there are many other mathematical algorithms suitable for being used as the second adaptive algorithm unit ALc2. On the other hand, the second adaptive filter W′(z) can be a finite impulse response (FIR) filter or an infinite impulse response (IIR) filter. For example, when using LMS algorithm as the second adaptive algorithm unit ALc2 so as to be provided in the second system identifying unit AI2, it utilizes following mathematical formulas to complete the adaptive system identification of the second adaptive filter W′(z):
In the above-listed mathematical formulas, y(n) is the second output signal, y′(n) is the third output signal, d(n) is the target signal, x(n) is the second reference signal, x′(n) is the third reference signal, e(n) is the second error signal, wl(n) is a weight vector, Ŝm(n) is a weight vector, μ is a step size of the second adaptive filter W′(z), and L and M is both a filter length. It is understood that, the second system identifying unit AI2 is adapted for completing an adaptive system identification of the second adaptive filter W′(z). After the adaptive system identification is completed, the second adaptive filter W′(z) is acquired.
Subsequently, method flow proceeds to step S6 for converting the second adaptive filter W′(z) to a low-order digitally-controlled filter W(z) by using a system identification tool. In a practicable embodiment, the system identification tool is a mathematical program, such as C programming language. Of course, engineers skilled in use of system identification tool certainly know that, there are many other programs suitable for completing the system identification; for example, Assembly.
In one embodiment, the low-order digitally-controlled filter W(z) is established by serially connecting several 2-order IIR filters, wherein each the 2-order IIR filter can be presented by following mathematical equation:
y(n)=[b0x(n)+b1x(n−1)+b2x(n−2)]−[a1x(n−1)+a2x(n−2)
In the above-listed mathematical equation, the y(n) is the second output signal, and b0, b1, b2, a1, and a2 are filter parameters. From above descriptions, it is understood that, the design method for feedforward active noise control system firstly utilizes two noise collecting systems (NC1, NC2) to collect a real environmental noise so as to generate a first reference signal x(n), a target signal d(n) and a second reference signal xS(n). Subsequently, based on the target signal d(n) and the second reference signal xS(n), a first adaptive system identifying unit AI1 is enabled to complete a first system identification process for producing a first adaptive filter Ŝ″(z), and then a second adaptive system identifying unit AI2 is enabled to complete a second system identification process for producing a second adaptive filter W′(z). Consequently, the second adaptive filter W′(z) is converted to a low-order digitally-controlled filter W(z) by using a system identification tool like C programming language.
In a normal case, the second system identifying unit AI2 shown in
The above description is made on embodiments of the present invention. However, the embodiments are not intended to limit scope of the present invention, and all equivalent implementations or alterations within the spirit of the present invention still fall within the scope of the present invention.
Number | Date | Country | Kind |
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110106484 | Feb 2021 | TW | national |
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