1. An active noise reduction method, comprising:

collecting an original noise signal by a reference microphone;

collecting an error signal through an error microphone, wherein the error signal comprises an error between an original noise signal transmitted to a space point where the error microphone is located and a noise reduction signal, and the noise reduction signal is determined according to the original noise signal and an initial filter coefficient;

inputting the error signal into a non-causal secondary path inverse estimation unit to obtain a pre-estimated error signal, wherein the secondary path inverse estimation unit is used for simulating the inverse of a secondary path;

and determining a filter coefficient according to the original noise signal and the estimated error signal.

2. The active noise reduction method of claim 1, further comprising:

determining the secondary path inverse estimation unit according to a secondary path estimation unit, wherein the secondary path estimation unit is used for simulating the secondary path.

3. The active noise reduction method of claim 2, further comprising:

playing a test signal through a speaker, the test signal being uncorrelated with the original noise signal;

obtaining a delay test signal according to the test signal and an initial secondary path estimation unit;

determining the difference between the error signal and the delayed test signal as a test error signal, wherein the error signal further comprises the test signal transmitted to the spatial point of the error microphone;

determining the secondary path estimation unit based on the test error signal and the test signal.

4. The active noise reduction method according to claim 2 or 3, wherein the secondary path inverse estimation unit comprises an amplitude estimation unit for providing amplitude information of the estimated error signal and a time advance unit for providing phase information of the estimated error signal.

5. The active noise reduction method according to claim 2 or 3, wherein the secondary path inverse estimation unit comprises a feedback system, wherein an open loop transfer function of the feedback system is determined by a transfer function and a gain multiple of the secondary path estimation unit, and a modulus of the open loop transfer function is much larger than 1.

6. An active noise reduction device, comprising:

a reference microphone for collecting an original noise signal;

the error microphone is used for acquiring an error signal, wherein the error signal comprises an error between an original noise signal transmitted to a space point where the error microphone is located and a noise reduction signal, and the noise reduction signal is determined according to the original noise signal and an initial filter coefficient;

the first calculation module is used for inputting the error signal into a non-causal secondary path inverse estimation unit to obtain a pre-estimated error signal, wherein the secondary path inverse estimation unit is used for simulating the inverse of a secondary path;

and the second calculation module is used for determining a filter coefficient according to the original noise signal and the estimated error signal.

7. An active noise reduction earphone, comprising:

a filter, the filter coefficients of which are determined by the active noise reduction method of any of claims 1-5.

8. An active noise reduction earphone, comprising: a reference microphone, a speaker, an error microphone and a chip,

wherein the reference microphone, the loudspeaker, the error microphone and the chip are used to perform the active noise reduction method of any one of claims 1-5 for determining filter coefficients in real time.

9. An electronic device, comprising:

a processor;

a memory including computer instructions stored thereon that, when executed by the processor, cause the processor to perform the active noise reduction method of any of claims 1-5.

10. A computer readable storage medium comprising computer instructions stored thereon, which when executed by a processor, cause the processor to perform the active noise reduction method of any of claims 1-5.

Background

In recent years, with the dramatic increase in market demand, Active Noise Cancellation (ANC) headphones are gaining more and more attention. Active noise reduction earphoneThe meter core being a filter, e.g. feedforward filter W_ffFeedback filter W_fb。

However, the structure of the active noise reduction headphone leads to the presence of secondary paths in the headphone, which can adversely affect the stability of the active noise reduction system during the filter design process. Therefore, how to overcome the influence of the secondary path to improve the system stability becomes a problem to be solved in the field.

Disclosure of Invention

In view of this, embodiments of the present application provide an active noise reduction method and apparatus, and an active noise reduction earphone, so as to solve the technical problem that the stability of an active noise reduction system in the prior art cannot be further improved due to the influence of a secondary path.

A first aspect of the present application provides an active noise reduction method, including: collecting an original noise signal by a reference microphone; collecting an error signal through an error microphone, wherein the error signal comprises an error between an original noise signal transmitted to a space point where the error microphone is located and a noise reduction signal, and the noise reduction signal is determined according to the original noise signal and an initial filter coefficient; inputting the error signal into a non-causal secondary path inverse estimation unit to obtain a pre-estimated error signal, wherein the secondary path inverse estimation unit is used for simulating the inverse of a secondary path; and determining the filter coefficient according to the original noise signal and the estimated error signal.

In an embodiment, the active noise reduction method further includes: and determining a secondary path inverse estimation unit according to the secondary path estimation unit, wherein the secondary path estimation unit is used for simulating a secondary path.

In an embodiment, the active noise reduction method further includes: playing a test signal through a loudspeaker, wherein the test signal is irrelevant to an original noise signal; obtaining a delay test signal according to the test signal and an initial secondary path estimation unit; determining the difference between the error signal and the delayed test signal as a test error signal, wherein the error signal also comprises the test signal transmitted to the spatial point of the error microphone; a secondary path estimation unit is determined based on the test error signal and the test signal.

In one embodiment, the secondary path inverse estimation unit comprises an amplitude estimation unit for providing amplitude information of the estimated error signal and a time advance unit for providing phase information of the estimated error signal.

In another embodiment, the secondary path inverse estimation unit comprises a feedback system, wherein an open loop transfer function of the feedback system is determined by a transfer function and a gain multiple of the secondary path estimation unit, a modulus of the open loop transfer function being much larger than 1.

A second aspect of the present application provides an active noise reduction device comprising: a reference microphone for collecting an original noise signal; the error microphone is used for acquiring an error signal, wherein the error signal comprises an error between an original noise signal transmitted to a space point where the error microphone is located and a noise reduction signal, and the noise reduction signal is determined according to the original noise signal and an initial filter coefficient; the first calculation module is used for inputting the error signal into a non-causal secondary path inverse estimation unit to obtain a pre-estimated error signal, wherein the secondary path inverse estimation unit is used for simulating the inverse of a secondary path; and the second calculation module is used for determining the filter coefficient according to the original noise signal and the estimated error signal.

A third aspect of the present application provides an active noise reduction headphone comprising: a filter, the filter coefficients of which are determined by the active noise reduction method provided in any embodiment of the first aspect of the present application.

A fourth aspect of the present application provides an active noise reduction headphone, comprising: the reference microphone, the loudspeaker, the error microphone and the chip are used for the active noise reduction method provided by any embodiment of the first aspect of the present application to determine the filter coefficients in real time.

A fifth aspect of the present application provides an electronic device, comprising: a processor; a memory including computer instructions stored thereon, which, when executed by the processor, cause the processor to perform the active noise reduction method provided by any of the embodiments of the first aspect of the present application.

A sixth aspect of the present application provides a computer-readable storage medium comprising computer instructions stored thereon, which, when executed by a processor, cause the processor to perform the active noise reduction method provided by any of the embodiments of the first aspect of the present application.

Based on the active noise reduction method, the active noise reduction device and the active noise reduction earphone, the error signal (namely, residual noise) is actively advanced in the process of adaptively adjusting the filter coefficient so as to accurately correspond to the original noise signal (namely, environmental noise) in time, adverse effects caused by a secondary path can be overcome, and the stability of the active noise reduction process is improved.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.

Drawings

In order to make the objects, technical solutions and advantages of the embodiments of the present application more apparent, the embodiments of the present application will be described in further detail below with reference to the accompanying drawings. It is to be understood that the drawings form a part of the specification, illustrate the present application together with embodiments thereof, and are not to be construed as limiting the present application. Unless otherwise indicated, like reference numbers and designations in the drawings generally refer to like steps or components.

FIG. 1 is a schematic diagram of an exemplary active noise reduction system.

Fig. 2 is a schematic diagram illustrating an exemplary active noise reduction system according to an embodiment of the present application.

Fig. 3 is a schematic flow chart of an active noise reduction method according to an embodiment of the present application.

Fig. 4 is a schematic flowchart of an active noise reduction method according to another embodiment of the present application.

Fig. 5 is a schematic diagram of an active noise reduction system according to an embodiment of the present application.

Fig. 6 is a schematic diagram of an active noise reduction system according to another embodiment of the present application.

Fig. 7 is a schematic diagram of a secondary path measurement system according to an embodiment of the present application.

Fig. 8 is a schematic diagram illustrating another exemplary active noise reduction system provided by an embodiment of the present application.

Fig. 9 is a schematic flowchart of an active noise reduction method according to another embodiment of the present application.

Fig. 10 is a schematic structural diagram of an active noise reduction device according to an embodiment of the present application.

Fig. 11 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

Application scenario overview

In an active noise reduction headphone, the noise reduction parameters (i.e., filter coefficients) of the filter may be determined by either an off-line design or an on-line design. The off-line design means that the filter coefficient is determined before the earphone leaves a factory and cannot be adjusted again after leaving the factory; the on-line design means that the active noise reduction system in the headset can adjust the filter coefficients at the user using stage to make it more fit the actual noise environment.

In any filter design mode, in the process of determining or adjusting the filter coefficient, the original noise signal collected by the reference microphone and the error noise signal collected by the error microphone can be used and input into the adaptive module, the filter coefficient is gradually adjusted through the adaptive calculation process, and the optimal filter coefficient is determined when the error noise signal is converged.

For example, fig. 1 shows an active noise reduction system using an adaptive algorithm, which may use an LMS (Least Mean Square) algorithm. The active noise reduction system includes: a reference microphone 110, a filter 120, a speaker (not shown), an error microphone 130, and an adaptation module 140.

Further, paths shown by dotted lines in fig. 1 represent propagation paths of acoustic signals other than the circuit, and specifically include a primary path (transfer function is P) formed with reference to a space between the microphone 110 and the error microphone 130, and a secondary path (transfer function is G) formed by the speaker itself and the space between the speaker and the error microphone 130 together.

As shown in fig. 1, the raw noise at the reference microphone 110 is transferred through a primary path to a point in space where the error microphone 130 is located. Meanwhile, the reference microphone 110 collects the original noise and converts it into an original noise signal d (n), and transmits it to the filter 120; the filter 120 calculates a noise reduction signal y (n) with a phase opposite to that of the original noise signal d (n) based on the filter coefficient W according to the original noise signal d (n), and outputs the noise reduction signal y (n) to the loudspeaker; the speaker plays the noise reduced sound wave based on the noise reduction signal y (n) so that the noise reduced sound wave is delivered to the spatial point where the error microphone 130 is located. That is, the noise reduction signal y (n) is output by the filter 120 and then is transmitted to the spatial point of the error microphone 130 via the secondary path. At this time, the original noise signal d (n) and the noise reduction signal y (n) are respectively transmitted to the error microphone 130 via different paths to form a superposition, so that the error microphone 130 collects the error therebetween (i.e. the error signal e (n)).

Furthermore, in order to adjust the filter coefficient W, the reference microphone 110 transmits the original noise signal d (n) to the adaptation module 140; error microphone 130 delivers an error signal e (n) to adaptation module 140. Based on the original noise signal d (n) and the error signal e (n), the adaptive module 140 iteratively updates the filter coefficient W to finally determine an optimal filter coefficient.

However, it can be understood from the above description that, unlike the original noise signal d (n) directly input to the adaptation module 140 from the reference microphone 110, the error signal e (n) can not be collected by the error microphone 130 until the noise reduction signal y (n) reaches the spatial point of the error microphone 130 via the secondary path. Thus, as shown in fig. 1, the error signal received by the adaptation module 140 at the same time as the original noise signal d (n) is not e (n) corresponding to d (n), but is e (n ') corresponding to the original noise signal d (n') whose acquisition time is earlier than d (n). That is, in the prior art, two input signals that are used as the basis for calculation of the adaptive module 140 do not correspond to each other exactly, and if the filter coefficients are adjusted according to such input signals, the stability of the active noise reduction system is impaired, and in a severe case, even a system downtime may occur, so that active noise reduction cannot be performed.

In order to solve the above problems faced by the existing active noise reduction technology, embodiments of the present application aim to provide an active noise reduction method, an active noise reduction device, and an active noise reduction earphone, which implement to improve the stability of an active noise reduction system and further implement optimized noise reduction by correcting a time difference between two input signals (an original noise signal and an error signal) of a self-adaptive module.

Exemplary System

Fig. 2 is a schematic diagram illustrating an exemplary active noise reduction system 200 according to an embodiment of the present application. The system comprises: reference microphone 210, processor 220, speaker 230, and error microphone 240, wherein processor 220 includes filter 221, adaptation module 222, and secondary path inverse estimation unit 223.

Specifically, the reference microphone 210 is disposed on the earphone housing for collecting an original noise signal; the filter 221 is configured to receive the original noise signal from the reference microphone 210, calculate a noise reduction signal according to the original noise signal and a filter coefficient, and send the noise reduction signal to the speaker 230; the speaker 230 is used for playing the noise reduction sound wave according to the received noise reduction signal; the error microphone 240 is disposed near the ear canal of the user for collecting an error signal (i.e., an error between the original noise signal delivered to the spatial point where the error microphone 240 is located and the noise reduction signal); the adaptive module 222 is used for receiving the original noise signal from the reference microphone 210 and the error signal from the error microphone 240, and updating the filter coefficients according to the original noise signal and the error signal to obtain the optimal filter coefficients.

It should be understood that the paths shown by the dashed lines in fig. 2 represent the propagation paths of acoustic signals other than the circuit. Specifically, in the headphone, the space between the reference microphone 210 to the error microphone 240 forms a primary path, and the speaker 230 itself and the space between the speaker 230 to the error microphone 240 together constitute a secondary path. In the active noise reduction process, the original noise is transmitted to the error microphone 240 through the primary path, and the noise reduction signal is transmitted to the error microphone 240 through the secondary path, which are overlapped at the error microphone 240 to form an error signal.

In addition, as shown in fig. 2, in the active noise reduction system 200 provided in the embodiment of the present application, the processor 220 further includes a secondary path inverse estimation unit 223. Specifically, the secondary path inverse estimation unit 223 is disposed between the error microphone 240 and the adaptive module 222, and when the error microphone 240 sends an error signal to the adaptive module 222, the secondary path inverse estimation unit 223 is configured to perform advanced processing on the error signal to obtain an estimated error signal, and input the estimated error signal to the adaptive module 222.

Exemplary method

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments that can be derived from the embodiments given herein by a person of ordinary skill in the art are intended to be within the scope of the present disclosure.

Fig. 3 is a schematic flow chart of an active noise reduction method according to an embodiment of the present application. The method may be performed, for example, by processor 220 in active noise reduction system 200. As shown in fig. 3, the method includes:

s310: the original noise signal is collected by a reference microphone.

S320: the error signal is collected by an error microphone.

The error signal comprises an error between an original noise signal transmitted to a space point where the error microphone is located and a noise reduction signal, and the noise reduction signal is determined according to the original noise signal and an initial filter coefficient.

Specifically, after the reference microphone collects the original noise signal d (n) at the first time, the filter may calculate the noise reduction signal y (n) corresponding to d (n) based on d (n) and the initial filter coefficient (i.e., the filter coefficient to be adjusted), and output it to the secondary path. y (n) reaches the spatial point where the error microphone is located through the secondary path, so that the error microphone acquires error signals e (n) corresponding to d (n) and y (n) at the second moment.

In the process, due to the delay of the secondary path, the second time when the error microphone acquires the error signal e (n) is necessarily later than the first time when the reference microphone acquires the original noise signal d (n).

S330: and inputting the error signal into a non-causal secondary path inverse estimation unit to obtain an estimated error signal.

The secondary path inverse estimation unit is used for simulating the inverse of the secondary path.

At the first time, the error signal collected by the error microphone is not the error signal e (n) corresponding to d (n), but the error signal e (n ') is corresponding to the original noise signal d (n') collected earlier than d (n). If the error signal e (n ') is directly inputted to the adaptive module, the adaptive module will perform calculation based on e (n') and d (n) which are mutually different, resulting in poor system stability. As mentioned above, it is the delay caused by the secondary path that causes this phenomenon.

Therefore, if e (n ') acquired at the first time can be obtained in advance at the first time based on e (n') acquired at the first time, e (n) and the original noise signal d (n) can be input to the adaptive module at the first time, so that d (n) and e (n) corresponding to each other in time are received by the adaptive module at the same time.

In particular, in an embodiment of the present invention, a secondary path inverse estimation unit may be provided between the error microphone and the adaptation module. The secondary path inverse estimation unit is used for simulating the inverse of the secondary path and canceling the influence of the secondary path. After receiving the error signal e (n ') collected by the error microphone at the first time, the secondary path inverse estimation unit may perform advanced processing on the error signal e (n'), that is, obtain an estimated error signal e '(n) for simulating a real error signal e (n) by prediction in advance, and input the estimated error signal e' (n) to the adaptive module at the first time, so that the error signal and the original noise signal are mutually "aligned" in time, thereby eliminating the influence brought by the secondary path.

S340: and determining the filter coefficient according to the original noise signal and the estimated error signal.

After the self-adaptive module receives the original noise signal and the pre-estimated error signal, the initial filter coefficient can be adjusted, whether the adjusted filter coefficient is the optimal filter coefficient or not is judged, if not, the adjustment is carried out again, and the process is repeated until the filter coefficient is optimal.

Specifically, in one embodiment, it may be determined whether the filter coefficients are optimal based on the estimated error signal.

When the estimated error signal is judged not to reach the preset optimal condition, the initial filter coefficient can be adjusted, and the updated noise reduction signal is determined by adopting the adjusted filter coefficient. After the loudspeaker plays the updated noise reduction signal, the error microphone can acquire the updated error signal, so that the self-adaptive module obtains the updated estimated error signal. When the updated estimated error signal is judged to still not reach the preset optimal condition, the filter coefficient can be adjusted again to obtain the estimated error signal after being updated again. And repeating the steps until the estimated error signal meets the preset optimal condition, stopping adjustment, and determining the current filter coefficient (namely the filter coefficient after the last adjustment) as the final filter coefficient.

In one embodiment, for example, the energy of the predicted error signal reaching the minimum value may be set as the optimal condition, that is, whether the predicted error signal satisfies the preset optimal condition is determined by determining whether the energy of the residual noise signal reaches the minimum value.

Here, the process of repeatedly adjusting the filter coefficients and updating the estimated error signal may be implemented by using an adaptive algorithm, such as an LMS (Least Mean Square) algorithm, and the filter coefficients are updated each time until the estimated error signal is optimal. It should be understood that the embodiments of the present application do not limit the algorithm actually used.

Based on the active noise reduction method provided by the embodiment of the application, the error signal is actively advanced in the process of adaptively adjusting the coefficient of the filter so as to accurately correspond to the original noise signal in time, and adverse effects caused by a secondary path can be overcome, so that the stability of the active noise reduction process is improved.

Fig. 4 is a schematic flowchart of an active noise reduction method according to another embodiment of the present application. The method may be performed, for example, by processor 220 in active noise reduction system 200. As shown in fig. 4, on the basis of the method shown in fig. 3, the method further includes:

s410: a secondary path inverse estimation unit is determined from the secondary path estimation unit.

Wherein the secondary path estimation unit is used for simulating a secondary path.

It will be appreciated that the secondary path (transfer function G) is a causal system and therefore the inverse is a non-causal system. That is, the secondary path inverse estimation unit is non-causal and cannot be directly implemented by physical means. Therefore, in the embodiment of the present application, the secondary path estimation unit may be determined first by a physical method, and then the secondary path inverse estimation unit is determined based on the secondary path estimation unit.

Here, since the secondary path estimating unit may be predetermined or adjusted in real time during the usage of the headset, S410 may be implemented before S310 or after S320, which is not limited in this embodiment of the present application.

In an embodiment, the manner of determining the secondary path inverse estimation unit according to the secondary path estimation unit may be as shown in fig. 5.

In the active noise reduction system 500 shown in fig. 5, the secondary path inverse estimation unit 523 may include an amplitude estimation unit 510 and a time advance unit 520. The amplitude estimation unit 510 is obtained according to the secondary path estimation unit, and is configured to provide amplitude information of the estimated error signal; the time advance unit 520 is used to provide phase information of the estimated error signal.

Specifically, the amplitude estimation unit 510 is equivalent to a filter, and may process an error signal collected by an error microphone based on its own parameters to obtain amplitude information of the estimated error signal. Here, the parameters of the magnitude estimation unit 510 may be determined according to the secondary path estimation unit. That is, after determining the change condition of the secondary path to the amplitude of the error signal by the secondary path estimation unit, the changed amplitude can be modified to the amplitude before the change by the back-stepping. It should be understood that the amplitude estimation unit 510 can be implemented physically, for example, by designing the amplitude estimation unit 510 based on the secondary path estimation unit by designing the FIR filter.

The timing advance unit 520 can modify the phase of the error signal, and estimate the phase information of the error signal to be collected at the second time at the first time.

After the amplitude information and the phase information are obtained, the secondary path inverse estimation unit can obtain an estimated error signal according to the received error signal.

In another embodiment, the manner of determining the secondary path inverse estimation unit according to the secondary path estimation unit may be as shown in fig. 6.

In the active noise reduction system 600 shown in fig. 6, the secondary path inverse estimation unit 623 may be configured as the feedback system 610, wherein the open loop transfer function of the feedback system is determined by the gain multiple and the transfer function of the secondary path estimation unit, and the modulus of the open loop transfer function is much larger than 1.

Specifically, the transfer function of the secondary path estimation unit may be denoted as G ', the gain multiple may be denoted as K, and the open-loop transfer function of the feedback system 610 may be denoted as KG ', and the closed-loop transfer function may be denoted as K/(1+ KG '). Thus, when the modulus of the open-loop transfer function is much larger than 1, the closed-loop transfer function of the feedback system 610 is infinitely close to 1/G', i.e. the inverse of the secondary path estimation unit.

Based on the active noise reduction method provided by the embodiment of the application, the non-causal secondary path inverse estimation unit can be obtained based on the physically-achievable secondary path estimation unit, the technical scheme is easy to realize, a large amount of calculation resources are not required to be consumed, and a better solution is provided for the field.

It should be noted that, in some embodiments of the present application, the secondary path estimation unit may be predetermined by offline calculation; in another embodiment of the present application, the secondary path estimation unit may further adjust the optimization in real time through online calculation during the actual use process of the user so as to adapt to different use environments.

For example, in an embodiment of the present application, the secondary path estimation unit may be predetermined by an offline calculation using the secondary path measurement system 700 shown in fig. 7. As shown in fig. 7, the secondary path measurement system 700 includes a player 710, a processor 720, a speaker 730, and an error microphone 740. Processor 720 includes a secondary path estimation unit 721, an adder 722, and an adaptation module 723.

The method for determining the secondary path estimation unit based on the secondary path measurement system 700 may be implemented in a filter design stage before the earphone is shipped, and specifically may be executed by the processor 720 in the secondary path measurement system 700 shown in fig. 7. The method may comprise the steps of:

playing the test signal through a loudspeaker;

obtaining a delay test signal according to the test signal and an initial secondary path estimation unit;

determining a test error signal based on the delayed test signal and the test signal transmitted to the error microphone;

a secondary path estimation unit is determined based on the test error signal and the test signal.

Specifically, the test signal may be a white noise signal, a pink noise signal, or the like from the player 710, and the embodiment of the present application does not limit the specific selection of the test signal.

The player 710 inputs a test signal to the speaker 730 via the circuit, and the test signal is collected by the error microphone 740 after being transmitted from the speaker 730 to a spatial point where the error microphone 740 is located, that is, after passing through a secondary path. That is, the error microphone 740 collects the test signal affected by the delay of the secondary path.

Specifically, as shown in fig. 7, in the secondary path measurement system 700, a secondary path estimation unit 721 is provided between the player 710 and the adder 722. Here, the unadjusted secondary path estimation unit is the initial secondary path estimation unit.

The player 710 inputs the test signal to the initial secondary path estimating unit through the circuit, and after receiving the test signal, the initial secondary path estimating unit may perform delay processing on the test signal to obtain a delayed test signal, and input the delayed test signal to the adder 722.

Specifically, error microphone 740 may input the test signal after receiving the test signal affected by the secondary path delay to summer 722. The adder 722 may compare the delayed test signal with the test signal after being delayed by the secondary path to obtain an error therebetween, i.e., a test error signal.

It should be understood that when the secondary path estimation unit 721 is infinitely close to the true secondary path, the energy of the test error signal should be minimized, and the secondary path estimation unit at this time is the optimal secondary path estimation unit.

Therefore, to obtain an optimal secondary path estimation unit, the test signal and the test error signal may be input to the adaptation module 723, and the initial secondary path estimation unit may be iteratively adjusted by an adaptive algorithm to obtain an updated test error signal. When the expected power of the current updated test error signal is judged to reach the minimum value, the current (after the last adjustment) secondary path estimation unit can be determined to be the secondary path estimation unit closest to the real secondary path.

Based on the active noise reduction method provided by the embodiment of the application, the secondary path estimation unit is obtained through self-adaptive adjustment in the filter design stage, so that the secondary path inverse estimation unit can be determined, the stability of the system is improved by using the secondary path inverse estimation unit in the active noise reduction process, and good use experience is brought to users.

In some other embodiments, other methods may be used to perform offline calculation on the secondary path estimation unit. For example, a white noise signal x (n) is played through a loudspeaker and is collected through an error microphone to obtain y (n); solving for the self-power spectrum P of x (n) respectively_xxAnd cross-power spectra P of x (n) and y (n)_xy(ii) a According to P_xxAnd P_xyThe transfer function of the secondary path is calculated, and the secondary path estimation unit is set based on the transfer function of the secondary path.

Fig. 8 is a schematic diagram of another exemplary active noise reduction system 800 provided by an embodiment of the present application. This system differs from the exemplary active noise reduction system 200 shown in fig. 2 by further comprising a player 810, while the processor 820 comprises a filter 221, a first adaptation module 222, a secondary path inverse estimation unit 823, a secondary path estimation unit 824, an adder 825, and a second adaptation module 826.

Fig. 9 is a schematic flowchart of an active noise reduction method according to another embodiment of the present application. The method may be performed, for example, by processor 820 in active noise reduction system 800 shown in FIG. 8.

As mentioned above, fig. 7 of the present application illustrates a system for off-line computing a secondary path estimation unit. However, since the secondary path estimation unit obtained by off-line calculation is predetermined before the earphone is shipped from the factory, it cannot be changed during actual use. Therefore, in the actual use of the user, the calibrated secondary path estimation unit cannot adapt to the real secondary path of each earphone in real time, and the system cannot determine a more effective secondary path inverse estimation unit according to the calibrated secondary path estimation unit, so that the user experience cannot be optimal.

In view of this, embodiments of the present application provide an active noise reduction method as shown in fig. 9, which can implement online calculation of the secondary path estimation unit and real-time update of the secondary path inverse estimation unit. For example, during the actual use process of the user, the processor 820 may execute the method to optimize the secondary path inverse estimation unit in real time, and perform active noise reduction by using the optimized secondary path inverse estimation unit.

It should be understood that, according to actual requirements, the method shown in fig. 9 may also be used for calculating the secondary path estimation unit offline, and the embodiment of the present application is not limited to a specific application scenario of the method.

As shown in fig. 9, the online calculation method includes:

s910: the original noise signal is collected by a reference microphone.

S920: the test signal is played through a speaker.

Where the test signal is a signal that is uncorrelated with the original noise signal. In particular, the test signal may be a frequency sweep signal from the player 810, or the like.

Preferably, in another embodiment, since the active noise reduction method provided by this embodiment may be implemented in actual use of a user, the test signal may also be an acoustic signal actually played by the user, such as a voice call signal, a media audio signal, and the like. Such signals are non-stationary signals (predominantly medium to high frequencies) and can be considered as having no correlation with the original noise signals (stationary, predominantly low frequencies) from the environment.

It should be understood that S920 may also be performed before S910.

S930: the error signal is collected by an error microphone.

Similar to the previous embodiment, the filter 221 generates a noise reduction signal according to the original noise signal and the filter coefficient and transmits it to the speaker 230 for playing. The original noise signal and the noise reduction signal respectively pass through the primary path and the secondary path to reach the spatial point where the error microphone 240 is located, so that the error microphone 240 collects the error between the two signals.

In addition, in the present embodiment, the test signal also passes through the secondary path to the spatial point where the error microphone 240 is located. Since the test signal is uncorrelated with the original noise signal, it can be understood that the error signal collected by the error microphone 240 includes the error between the original noise signal delivered to the spatial point of the error microphone 240 and the noise reduction signal, and also includes the test signal.

S940: and obtaining a delay test signal according to the test signal and the initial secondary path estimation unit.

Similarly to the embodiment shown in fig. 7, in the present embodiment, a secondary path estimation unit 824 for simulating a secondary path may be provided between the player 810 and the adder 825. Here, the unadjusted secondary path estimation unit is the initial secondary path estimation unit.

The player 810 may directly transmit the test signal to the initial secondary path estimation unit through the circuit, and after receiving the test signal, the initial secondary path estimation unit may perform delay processing on the test signal to obtain a delayed test signal, and transmit the delayed test signal to the adder 825.

In another embodiment, the secondary path estimation unit 824 may also be disposed between the speaker 230 and the adder 825 (not shown). At this time, the speaker 230 may input the noise reduction signal and the test signal to the initial secondary path estimating unit through the circuit while playing the noise reduction signal and the test signal after receiving the noise reduction signal from the filter 221 and the test signal from the player 810. After the initial secondary path estimation unit receives the noise reduction signal and the test signal, delay processing can be performed to obtain a delay test signal. It should be understood that in the present embodiment, the delayed test signal includes the noise reduction signal and the test signal that are subjected to the delay processing.

S950: the difference between the error signal and the delayed test signal is determined as a test error signal.

Specifically, the error microphone 240, upon receiving the error signal, may input it to the summer 825. The summer 825 may compare the error signal from the error microphone 240 with the delayed test signal from the initial secondary path estimation unit to obtain an error therebetween, i.e., a test error signal.

S960: a secondary path estimation unit is determined based on the test error signal and the test signal.

Similar to the previous embodiment, when the secondary path estimation unit 824 is infinitely close to the real secondary path, the energy of the test error signal should be minimized, and the secondary path estimation unit at this time is the optimal secondary path estimation unit.

Thus, to obtain an optimal secondary path estimation unit, the test signal and the test error signal may be input to the second adaptation module 826, and the initial secondary path estimation unit may be iteratively adjusted by an adaptation algorithm to obtain an updated test error signal. When the expected power of the current updated test error signal is judged to reach the minimum value, the current (after the last adjustment) secondary path estimation unit can be determined to be the secondary path estimation unit closest to the real secondary path.

S970: and determining a secondary path inverse estimation unit according to the secondary path estimation unit.

As previously mentioned, the secondary path inverse estimation unit is non-causal and is used to model the inverse of the secondary path. The specific manner of determining the secondary path inverse estimation unit according to the secondary path estimation unit can refer to the embodiments shown in fig. 4 to 6, and is not described herein again.

S980: and inputting the error signal into a secondary path inverse estimation unit to obtain an estimated error signal.

Similarly to the embodiment shown in fig. 3, in order to overcome the time difference between the original noise signal and the error signal, the error signal may be processed in advance by the secondary path inverse estimation unit to obtain a corresponding estimated error signal, and the estimated error signal is input to the first adaptive module 222.

It should be appreciated that in the active noise reduction system 800, as shown by the dashed line in fig. 8, after the secondary path estimation unit 824 is determined by the second adaptation module 826, the secondary path inverse estimation unit 823 is updated according to the secondary path estimation unit, and the error signal is processed in advance by using the updated secondary path inverse estimation unit 823, so as to obtain the estimated error signal.

S990: and determining the filter coefficient according to the original noise signal and the estimated error signal.

Similar to the embodiment shown in fig. 3, after receiving the original noise signal and the estimated error signal, the first adaptive module 222 may adjust the initial filter coefficient, and determine whether the adjusted filter coefficient is the optimal filter coefficient, if not, perform adjustment again, and repeat the above process until the filter coefficient reaches the optimal filter coefficient.

Specifically, in one embodiment, it may be determined whether the filter coefficients are optimal based on the estimated error signal. When the estimated error signal is judged not to reach the preset optimal condition, the initial filter coefficient can be adjusted, and the updated noise reduction signal is determined by adopting the adjusted filter coefficient. After the speaker 230 plays the updated noise reduction signal, the error microphone 240 can acquire the updated error signal, so that the first adaptive module 222 obtains the updated estimated error signal. When the updated estimated error signal is judged to still not reach the preset optimal condition, the filter coefficient can be adjusted again to obtain the estimated error signal after being updated again. And repeating the steps until the estimated error signal meets the preset optimal condition, stopping adjustment, and determining the current filter coefficient as the final filter coefficient.

In one embodiment, for example, the energy of the predicted error signal reaching the minimum value may be set as the optimal condition, that is, whether the predicted error signal satisfies the preset optimal condition is determined by determining whether the energy of the residual noise signal reaches the minimum value.

Here, the process of repeatedly adjusting the filter coefficients and updating the estimated error signal may be implemented by using an adaptive algorithm, such as an LMS algorithm, and the filter coefficients are updated each time until the estimated error signal is optimized. It should be understood that the embodiments of the present application do not limit the algorithm actually used.

In an embodiment, S960 may specifically include the following steps:

adjusting the initial secondary path estimation unit according to the test error signal;

a. determining an updated test error signal based on the test signal and the adjusted secondary path estimation unit;

b. when the expected power of the updated test error signal does not reach the minimum value, adjusting the adjusted secondary path estimation unit;

iteratively executing the steps a and b until the expected power of the test error signal reaches the minimum value;

and determining the current adjusted secondary path estimation unit as the secondary path estimation unit.

Here, the process of iteratively performing steps a, b until the expected power of the updated test error signal reaches a minimum value may be implemented using an adaptive algorithm, such as an LMS algorithm.

It should be understood that in other embodiments of the present application, some steps of the method shown in fig. 9 may be omitted, or may be performed in other orders.

For example, in an embodiment, the above method may be performed without turning on the active noise reduction function, i.e., S980 and S990 may be omitted, and filters and noise reduction signals are not required to be used during the performance of each step. That is, when the user does not turn on the active noise reduction, the secondary path inverse estimation unit can be debugged only by using the original noise signal and the test signal, and the filter coefficient is not adjusted. Therefore, when the user opens the noise reduction function, the secondary path inverse estimation unit is already in the optimal state, and the link of optimizing the filter coefficient can be directly entered, so that a large amount of debugging time can be saved.

For another example, in another embodiment, the filter coefficients may be iteratively adjusted while the secondary path estimation unit is iteratively adjusted. That is, before determining the secondary path estimation unit and then determining the secondary path inverse estimation unit, that is, when the secondary path inverse estimation unit has not reached the optimum yet, after adjusting the secondary path estimation unit each time, dynamically determining the current secondary path inverse estimation unit according to the current adjusted secondary path estimation unit, and using the current adjusted secondary path inverse estimation unit in the adaptive link of the filter coefficients, the error signal is processed in advance by the current adjusted secondary path inverse estimation unit and input to the first adaptive module 222. Through the execution sequence, the adjustment operation of the secondary path inverse estimation unit and the filter coefficient can be synchronously performed, so that the optimization of the filter coefficient is started as soon as possible, and the user experience is improved.

Based on the active noise reduction method provided by the embodiment of the application, the acoustic signal actually played by the user is used as the test signal, so that the optimization of the secondary path inverse estimation unit can be naturally realized when the user uses the earphone to play audio or calls, the stability of the active noise reduction system is improved, the debugging process is more friendly and softer, and great convenience and good experience are brought to the user.

In real life, the human ear experiences different frequencies of sound to different extents. Therefore, the sound pressure level of the real noise collected by the microphone and the noise heard by the human ear at the same frequency are not necessarily the same. For active noise reduction headphones, the underlying goal is to reduce the noise level heard by the human ear, and not simply focus on the true noise level.

From this perspective, the active noise reduction method provided by another embodiment of the present application may adaptively update the filter coefficient based on considering the response characteristics of the human ear to different frequency noises, so as to achieve an active noise reduction effect more fitting the auditory sense of the human ear.

In an embodiment, on the basis of the active noise reduction system shown in fig. 2 or fig. 8, an acoustic weighting unit disposed between the error microphone and the adaptive module (or the first adaptive module) may be further included in the processor. It should be understood that the acoustic weighting unit may be disposed between the error microphone and the secondary path inverse estimation unit, or may be disposed between the adaptive module (or the first adaptive module) and the secondary path inverse estimation unit, which is not limited in this embodiment of the present application.

In this embodiment, before inputting the error signal into the secondary path inverse estimation unit, the error signal may be first input into the acoustic weighting unit to obtain a weighted error signal, and then the weighted error signal is input into the secondary path inverse estimation unit to calculate a weighted estimated error signal; alternatively, the estimated error signal may be input to the acoustic weighting unit after the estimated error signal is obtained from the error signal and the secondary path inverse estimation unit, so as to obtain a weighted estimated error signal.

Specifically, the weighted error signal can be obtained by performing weighted correction on the error signal or the spectral shape of the estimated error signal by the acoustic weighting unit. For example, the acoustic weighting unit may a-weight the error signal to make the noise spectrum of the weighted estimated error signal more similar to the hearing of the human ear.

Based on the active noise reduction method provided by the embodiment of the application, the weighted estimated error signal replaces the unprocessed estimated error signal and is input into the adaptive module to participate in the adjustment process of the filter coefficient, and the filter coefficient can be guided to be optimized towards the actual noise reduction demand direction of human ears, so that the active noise reduction effect is obviously improved, and better hearing experience is brought to users.

It should be noted that, the embodiments of the present application do not limit whether the earphone is further provided with a feedback noise reduction system, and a feedback noise reduction loop may be added to the active noise reduction system provided in any of the embodiments of the present application to form a hybrid active noise reduction earphone including a feedforward adaptive active noise reduction system and a feedback active noise reduction system.

Exemplary devices

Fig. 10 is a schematic structural diagram of an active noise reduction device 1000 according to an embodiment of the present disclosure.

As shown in fig. 10, the active noise reduction device 1000 includes: a reference microphone 1010 for collecting an original noise signal; an error microphone 1020 for acquiring an error signal, wherein the error signal includes an error between an original noise signal transmitted to a spatial point where the error microphone 1020 is located and a noise reduction signal, and the noise reduction signal is determined according to the original noise signal and an initial filter coefficient; the first calculating module 1030 is configured to input the error signal to a non-causal secondary path inverse estimation unit to obtain an estimated error signal, where the secondary path inverse estimation unit is configured to simulate an inverse of a secondary path; the second calculating module 1040 is configured to determine a filter coefficient according to the original noise signal and the estimated error signal.

Specifically, in order to enable the second calculation module 1040 to perform the adjustment operation of the filter coefficient based on the original noise signal and the error signal that accurately correspond to each other, the first calculation module 1030 provided in the embodiment of the present application is provided with a secondary path inverse estimation unit for simulating an inverse of the secondary path. After receiving the error signal collected by the error microphone, the first calculating module 1030 may utilize the secondary path inverse estimation unit to perform advanced processing on the error signal to obtain an estimated error signal corresponding to the original noise, and input the estimated error signal to the second calculating module 1040, so that the error signal (i.e., the estimated error signal) received by the second calculating module 1040 and the original noise signal are "aligned" in time, thereby canceling the influence caused by the secondary path.

The second calculating module 1040 may include an adaptive module, and after receiving the original noise signal and the estimated error signal, the adaptive module may adjust the initial filter coefficient, and determine whether the adjusted filter coefficient is the optimal filter coefficient, if not, adjust again, and repeat the above process until the filter coefficient reaches the optimal filter coefficient.

Specifically, in one embodiment, the second calculation module 1040 may include an adaptation module that may determine whether the filter coefficients are optimal based on the estimated error signal.

When the estimated error signal is judged not to reach the preset optimal condition, the initial filter coefficient can be adjusted, and the updated estimated noise reduction signal is determined by adopting the adjusted filter coefficient. After the loudspeaker plays the updated noise reduction signal, the error microphone 1020 can acquire the updated error signal, and the secondary path inverse estimation unit processes the updated error signal to obtain an updated estimated error signal. When the updated estimated error signal is judged to still not reach the preset optimal condition, the adaptive module can adjust the filter coefficient again to obtain the estimated error signal after being updated again. And repeating the steps until the estimated error signal meets the preset optimal condition, stopping adjustment, and determining the current filter coefficient (namely the filter coefficient after the last adjustment) as the final filter coefficient.

In one embodiment, for example, the energy of the predicted error signal reaching the minimum value may be set as the optimal condition, that is, whether the predicted error signal satisfies the preset optimal condition is determined by determining whether the energy of the residual noise signal reaches the minimum value.

Here, the process of repeatedly adjusting the filter coefficients and updating the estimated error signal may be implemented by using an adaptive algorithm, such as an LMS algorithm, and the filter coefficients are updated each time until the estimated error signal is optimized. It should be understood that the embodiments of the present application do not limit the algorithm actually used.

Based on the active noise reduction device provided by the embodiment of the application, the error signal is actively advanced in the process of adaptively adjusting the coefficient of the filter so as to accurately correspond to the original noise signal in time, and adverse effects caused by a secondary path can be overcome, so that the stability of the active noise reduction process is improved.

In an embodiment, the first calculating module 1030 is further configured to determine a secondary path inverse estimation unit according to the secondary path estimation unit. Wherein the secondary path estimation unit is used for simulating a secondary path.

Since the secondary path inverse estimation unit is non-causal and cannot be directly implemented in a physical manner, in this embodiment, the secondary path estimation unit may be determined first, and then the secondary path inverse estimation unit may be determined based on the secondary path estimation unit.

The manner of determining the secondary path inverse estimation unit according to the secondary path estimation unit may refer to the embodiments described in fig. 4 to 6 in the exemplary method, and is not described herein again.

It should be noted that, in some embodiments of the present application, the secondary path estimation unit may be predetermined by offline calculation, and in another embodiment of the present application, the secondary path estimation unit may further adjust optimization in real time by online calculation during actual use of the user so as to adapt to different use environments.

The manner of predetermining the secondary path estimation unit by an offline calculation manner may refer to the relevant content of the embodiment shown in fig. 7 in the exemplary method, and is not described herein again.

An embodiment of the present application provides an active noise reduction apparatus capable of adjusting a secondary path inverse estimation unit through online calculation, where the active noise reduction apparatus is based on the apparatus shown in fig. 10, and further includes a third calculation module.

Specifically, when the active noise reduction device provided based on this embodiment performs active noise reduction, a test signal from the player may be played by using a speaker in the earphone, where the test signal is uncorrelated with the original noise signal.

Here, the third calculation module includes a secondary path estimation unit. The third computing module may receive the test signal from the player through the circuit, and then obtain the delay test signal according to the test signal and the initial secondary path estimating unit (the unadjusted secondary path estimating unit is the initial secondary path estimating unit). Or, the third computing module may also receive the noise reduction signal and the test signal from the speaker through the circuit, and simultaneously process the two signals based on the initial secondary path estimation unit, so as to obtain the processed noise reduction signal and the processed test signal as the delay test signal together.

Further, the third computing module may receive an error signal from the error microphone and determine a difference between the error signal and the delayed test signal as the test error signal. It should be understood that, in the present embodiment, the error signal includes both the error between the original noise signal and the noise reduction signal delivered to the spatial point of the error microphone, and the test signal delivered to the spatial point of the error microphone.

It should be understood that the energy of the test error signal should be minimized when the secondary path estimation unit is infinitely close to the true secondary path, and the secondary path estimation unit at this time is the optimal secondary path estimation unit.

Therefore, in order to obtain the optimal secondary path estimation unit, the third calculation module may repeatedly adjust the initial secondary path estimation unit through an adaptive algorithm according to the test error signal and the test signal to obtain an updated test error signal. When the expected power of the current updated test error signal is judged to reach the minimum value, the current (after the last adjustment) secondary path estimation unit can be determined to be the secondary path estimation unit closest to the real secondary path.

After determining the secondary path estimation unit, the third computation module may synchronize the secondary path estimation unit to the first computation module such that the first computation module determines an updated secondary path inverse estimation unit based on the updated secondary path estimation unit and adjusts the filter coefficients using the updated secondary path inverse estimation unit.

It should be appreciated that in other embodiments of the present application, the above-described portions of the active noise reduction apparatus may be configured to actively reduce noise in other manners or sequences.

For example, in one embodiment, the above method may be performed without turning on the active noise reduction function, i.e., the filter and noise reduction signal may not be used during the performance of the steps. That is to say, when the user does not turn on the active noise reduction, the active noise reduction apparatus may only use the original noise signal and the test signal to debug the secondary path inverse estimation unit, and temporarily does not adjust the filter coefficient. Therefore, when the user opens the noise reduction function, the secondary path inverse estimation unit is already in the optimal state, and the link of optimizing the filter coefficient can be directly entered, so that a large amount of debugging time can be saved.

For another example, in another embodiment, the filter coefficients may be iteratively adjusted while the secondary path estimation unit is iteratively adjusted. That is, before determining the secondary path estimation unit, i.e., when the secondary path estimation unit has not reached the optimum yet, the third calculation module may dynamically synchronize the currently adjusted secondary path estimation unit to the first calculation module after each adjustment of the secondary path estimation unit, so that the first calculation module updates the secondary path inverse estimation unit based on the currently adjusted secondary path estimation unit and uses it in the adaptation of the filter coefficients. Through the execution sequence, the adjustment operation of the secondary path inverse estimation unit and the filter coefficient can be synchronously performed, so that the optimization of the filter coefficient is started as soon as possible, and the user experience is improved.

Based on the active noise reduction device provided by the embodiment of the application, the acoustic signal actually played by the user is used as the test signal, the optimization of the secondary path inverse estimation unit can be naturally realized when the user uses the earphone to play audio or calls, the stability of the active noise reduction system is improved, the debugging process is more friendly and softer, and great convenience and good experience are brought to the user.

Further, in another embodiment, the first computing module may further include an acoustic weighting unit. In the active noise reduction process, the acoustic weighting unit may process an error signal (the acoustic weighting unit is disposed between the error microphone and the secondary path inverse estimation unit) or an estimated error signal (the acoustic weighting unit is disposed between the secondary path inverse estimation unit and the adaptive module), so as to obtain a weighted error signal or an estimated error signal. Thus, the weighted estimated error signal is input to the second calculation module for adjusting the filter coefficients instead of the unweighted estimated error signal.

Specifically, the acoustic weighting unit may perform weighting correction on the spectral shape of the error signal or the estimated error signal to obtain a weighted error signal or an estimated error signal. For example, the acoustic weighting unit may perform a-weighting to make the noise spectrum of the weighted error signal or the estimated error signal closer to the auditory sense of the human ear.

Based on the active noise reduction device provided by the embodiment of the application, the weighted pre-estimated error signal is used as an input signal of self-adaptive calculation to participate in the adjustment process of the filter coefficient, and the filter coefficient can be guided to be optimized towards the actual noise reduction demand direction of human ears, so that the active noise reduction effect is obviously improved, and better hearing experience is brought to users.

It should be understood that the functions and technical effects of the modules in the active noise reduction apparatus provided in the foregoing embodiments may refer to corresponding contents in the exemplary method, and are not described in detail here.

Exemplary device

Fig. 11 is a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in fig. 11, the electronic apparatus includes: a processor 1110; memory 1120, memory 1120 includes computer instructions stored thereon, which when executed by processor 1110, cause processor 1110 to perform an active noise reduction method as provided by any of the embodiments described above.

Exemplary computer readable storage Medium

Other embodiments of the present application further provide a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the active noise reduction method according to any of the above embodiments. It is understood that the computer storage medium can be any tangible medium, such as: floppy disks, CD-ROMs, DVDs, hard drives, network media, or the like.

The block diagrams of apparatuses, devices, systems referred to in this application are only given as illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. Those skilled in the art will appreciate that the devices, apparatus, systems, etc. may be connected, arranged, or configured in any manner. Words such as "comprising," "including," "having," and the like are open-ended words to "including, but not limited to," and may be used interchangeably therewith unless the context clearly dictates otherwise. The word "such as" is used herein to mean, and is used interchangeably with, the phrase "such as but not limited to".

It should also be noted that in the devices, apparatuses, and methods of the present application, the modules or steps may be decomposed and/or recombined. These decompositions and/or recombinations are to be considered as equivalents of the present application.

The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the application. Thus, the present application is not intended to be limited to the above aspects but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

The above description is intended to be illustrative and descriptive of the present technology. Furthermore, the description is not intended to limit embodiments of the application to the form disclosed above. While a number of exemplary aspects and embodiments have been discussed above, other variations, modifications, changes, additions, and sub-combinations will readily occur to those skilled in the art based upon the foregoing.

The above description is only exemplary of the present application and should not be taken as limiting the present application, as any modifications, equivalents and the like that are within the spirit and principle of the present application should be included in the scope of the present application.