This disclosure relates generally to acoustic transducers having a non-linear transfer characteristic and more specifically to armature-based receivers having improved performance and corresponding methods.
Balanced armature receivers that convert an electrical input signal to an acoustic output characterized by a varying sound pressure level (SPL) are known generally. Such receivers generally comprise a motor having a coil to which an electrical excitation signal is applied. The coil is disposed about a portion of an armature (also known as a reed), a movable portion of which is disposed in equipoise between magnets, which are typically retained by a yoke. Application of the excitation or input signal to the receiver coil modulates the magnetic field, causing deflection of the reed between the magnets. The deflecting reed is linked to a movable portion of a diaphragm (known as a paddle) disposed within a partially enclosed receiver housing, wherein movement of the paddle forces air through a sound outlet of the housing.
The objects, features, and advantages of the present disclosure will be more apparent to those of ordinary skill in the art upon consideration of the following Detailed Description with reference to the accompanying drawings.
Those of ordinary skill in the art will appreciate that elements in the figures are illustrated for simplicity and clarity. It will be further appreciated that certain actions or steps may be described or depicted in a particular order of occurrence while those of ordinary skill in the art will understand that such specificity with respect to sequence is not actually required unless a particular order is specifically indicated. It will also be understood that the terms and expressions used herein have the ordinary meaning as is accorded to such terms and expressions with respect to their corresponding respective fields of inquiry and study except where specific meanings have otherwise been set forth herein.
Armature-based receivers (also referred to herein as a “receiver”) generally have a non-linear transfer characteristic dependent on various physical and operating characteristics of the transducer. Such characteristics include, for example, changing permeability of the armature due to a changing magnetic flux, among others. The output SPL of a receiver depends generally on the amplitude and frequency of the input signal. Receiver non-linearity tends to limit the undistorted output SPL, since higher SPL tends to aggravate distortion. Maximum output SPL is often specified for a particular level of distortion. The result is that the acoustic output of the receiver may not be an accurate reproduction of the desired acoustic output signal.
The present disclosure pertains to improving performance of an armature-based receiver by driving the receiver with a pre-distorted electrical excitation signal.
Armature-based receivers refer to a class of acoustic transducers having an armature (also known as a reed) with a movable portion that deflects relative to one or more magnets in response to application of an excitation signal to a coil of the receiver. Such receivers may be balanced or unbalanced. An armature-based receiver is ideally balanced when it has no magnetic flux, or at least negligible flux, in or through the armature when the armature is in a steady-state (stationary or rest) position (i.e., in the absence of an excitation signal applied to the coil). A receiver is unbalanced when there is magnetic flux in or through a stationary armature in its nominal rest position. An armature-based receiver with only one magnet is inherently unbalanced. Generally an unbalanced receiver will have decreased output SPL for a specified level of distortion compared to a balanced receiver. This imbalance can be detected by measuring a second harmonic of the distortion of an output signal produced in response to high amplitude input or drive signals. An armature-based receiver may be unbalanced due to deviation from manufacturing tolerances or for some other reason. Also, a balanced armature-based receiver may become unbalanced upon changing the rest position of the reed between the magnets. Such repositioning of the reed rest position may occur as a result of an impact from dropping the receiver or from some other shock imparted thereto.
One source of non-linearity in armature-based receivers is attributable to changing permeability of soft magnetic components of the receiver in response to an excitation signal applied to the receiver coil. Soft magnetic components include but are not limited to the armature, the yoke or other soft magnetic parts of the receiver. Nickel-Iron (Ni—Fe) is a soft magnetic component commonly used in armature-based receivers, although other soft magnetic materials may also be used. The relationship between an external magnetizing field H induced by a current in the receiver coil and the magnetic flux density B in the armature is nonlinear, particularly when driven by excitation signals having relatively high amplitude. At some point, when the magnetizing field H is strong enough, the magnetic field H cannot increase the magnetization of the armature further and the armature is said to be fully saturated when the permeability of material is equal to 1. In some armature-based receivers, this nonlinear relationship between the magnetizing field H and the magnetic flux density B is a primary source of nonlinearity, particularly at high output SPLs. However armature-based receivers exhibit non-linear behavior even where the receiver operates over a relatively linear portion of the magnetization curve.
Another source of nonlinearity in armature based receivers is attributable to the force/deflection characteristics of the reed and diaphragm. Ideally, for small displacements, there is a linear relationship between force and deflection as specified by Hooke's law. In reality this relationship is non-linear in many receivers. Air flow in armature-based receivers may also be a source of non-linearity. For example, in order to compensate for changes in barometric pressure, a small vent is often provided in the diaphragm paddle to equalize air pressure in front and back air chambers of the receiver. However air flowing through this vent during operation encounters a varying resistance to that flow which causes distortion. There may be other sources of distortion associated with air flow in or through other parts of the receiver or the load, including air flow in or through the acoustic output port, any tubing connected to the output port, the load (e.g., a human ear), load coupling parts, among other components of the receiver. The non-linear transfer characteristic of other acoustic transducers may result from other sources that are specific to the architecture of such transducers.
During the manufacture of armature-based receivers one or more permanent magnets are magnetized by exposure to a strong external polarizing magnetic field. The magnitude of the remnant magnetic field induced in the magnets is a primary factor in the sensitivity of the receiver. Increasing this remnant field (or magnetization) of the magnets generally increases sensitivity or efficiency of the receiver but also increases distortion. An over-magnetized receiver may have a reduced output SPL for a specified distortion level compared to a receiver that is not over-magnetized. This reduced output SPL tends to increase with increasing levels of magnetization. Thus the magnetization level of a receiver requires a tradeoff between sensitivity and distortion for most use cases.
Some armature-based receivers and particularly the magnets or other permanently magnetized portions thereof are over-charged or over-magnetized, or magnetized to a greater level than best practice would normally dictate. A receiver is strongly over-magnetized when the magnetic force is stronger than a mechanical restoring force of the movable portion of the armature (i.e., the restoring force of the reed, but not the restoring force of other parts of the receiver like the diaphragm). In a strongly over-magnetized receiver, in the absence of loading by other components (e.g., the diaphragm), the reed will tend to stick to one magnet or the other if the reed is offset from its equilibrium position. Over-magnetization may be intentional or it may result from a deviation from manufacturing tolerances, or lack thereof, when charging or magnetizing the magnets or other permanently magnetized parts of the receiver.
In
Output distortion of an acoustic transducer or receiver is reduced using a feed-forward algorithm that applies a pre-distorted electrical excitation signal to an input of the receiver. The feed-forward system can be open or closed. In an open system, a pre-distorted electrical excitation signal is applied to an input of the receiver without adapting the pre-distortion to changes in a characteristic of the receiver. In a closed system, information indicative of a change in a characteristic of the receiver is used to adaptively update the computable non-linear function used to pre-distort the input signal. The feed-forward system uses an inverse model to generate the pre-distorted electrical excitation signal. The inverse model can be created through testing or by numerically inverting a forward model. The inverse model may be efficiently implemented using a non-linear polynomial, among other computable non-linear functions. These and other aspects of the disclosure are described further herein.
The pre-distorted electrical excitation signal is an output of a computable non-linear function of an electrical input signal (x) representative of a desired acoustic output. For armature-based receivers, the pre-distorted electrical excitation signal compensates for non-linearity attributable to mechanical and magnetic hysteresis, runaway and saturation among other sources.
In
In
Generally, a pre-distorted electrical excitation signal is generated by applying an electrical input signal (x) representative of a desired acoustic output to a computable non-linear function before the pre-distorted electrical excitation signal is applied to the acoustic receiver. The function modifies the input signal to provide a desired acoustic output at an acoustic output port of the receiver. A computable function is one for which there exists an algorithm that can produce an output of the function for a given an input within the domain of the function. The computable non-linear function could be embodied as a continuous function or as a piecewise linear function. A piece-wise linear function could be based on a look-up table where linear interpolations are used to identify values between data points in the table. Other curve fitting schemes may be used to generate linear or nonlinear functions that approximate a data set representing an inverse model suitable for distorting an input signal.
In one embodiment, the computable non-linear function is any function that can be approximated by a rational polynomial. Such functions include polynomials, hyperbolic and inverse hyperbolic functions, logarithmic and inverse logarithmic functions, among other function forms. These and other functions may be approximated by a summation of a limited set of terms having odd or even exponents (e.g., a truncated Taylor series) as is known generally. Rational polynomial and polynomial functions are readily and efficiently implemented by a digital processor. In other embodiments, other computable non-linear functions may be used. Such other functions may have negative exponents, exponents that are less than unity, or non-integer exponents, a set of orthogonal functions, an inverse sigmoid form or some other form. Thus many suitable functional forms will include at least one term that is proportional to xn where n is not equal to unity or the value of one (1). The form of the computable non-linear function and parameters thereof (e.g., number of terms, order, coefficients, etc.) required for adequate compensation will depend in part on the particular receiver, the particular application or use case, and on the desired output.
In one embodiment, the non-linear function is a polynomial having the following general form:
y(x)=k1x+k2x2 +k3x3 +. . . +knxn Eq. (1)
In Equation (1), the variable x is an electrical input signal representative of the desired acoustic signal and the function parameters are coefficients. The electrical input signal could originate from a microphone associated with a hearing-aid, from an audio source like a media player, or from any other source. The coefficients kn represent constants for the nth order terms in the series. The signal resulting from the summation of terms is non-linear and the terms and polynomial coefficients are selected to compensate for non-linearity of the acoustic receiver as discussed below. Odd ordered terms generally compensate for symmetric non-linearity and even ordered terms generally compensate for asymmetric non-linearity. Thus the polynomial of Equation (1) compensates for both symmetric and asymmetric non-linearity. In armature-based receivers symmetric non-linearity may be attributable to magnetic saturation of the receiver, air noise, receiver suspension, among other characteristics, and asymmetric non-linearity may be attributable to reed imbalance, receiver suspension, among other receiver characteristics.
The polynomial of Equation (1) compensates most effectively for non-linearity at frequencies below the primary mechanical resonance of the receiver where the frequency response is substantially flat (as shown in
y=(h1(x)+(h2(x))2+(h3(x))3 +. . . +(hn(x))n Eq. (2)
In Equation (2), hn(x) is a time-domain filter wherein the output of the filter h1(x) is added to the square of the output of filter h2(x) and to the cube of filter h3(x), and so on where the filter powers are taken on a per sample basis. It will be appreciated that a special case of Equation 2 is where one or more of the time-domain filters are identical. In such a case, efficiencies can be realized by processing the input signal through identical filters only once and then simply exponentiating those outputs to different degrees before adding. Equation (2) extends the applicability of polynomial-based compensation to higher frequencies.
Equation (2) could be implemented using an Autoregressive Moving-Average (ARMA) filter. An ARMA filter is a digital filter that uses present and past values of the input signal and past values of the output signal to compute a current output signal. The same input is applied to each filter, but the filter outputs are different, due at least in part to the order of various terms. A typical ARMA filter implementation is as follows:
y[n]=b0x[n]+b1x[n−1]+b2x[n−2]+a1y[n−1]+a2y[n−2] Eq. (3)
In Equation (3), x[n] is the filter input, y[n] is the filter output, and the constants an and bn are filter parameters, where n=0, 1, 2 . . . .
For many applications, polynomials with frequency independent terms like Equation (1) will provide reasonably good compensation for receiver non-linearity, since much of the energy in the input signal is below the primary mechanical resonance of the receiver. In one particular implementation, the non-linearity of an armature-based receiver is compensated by modifying an electrical input signal applied to the receiver coil by a current amplifier with the following polynomial:
y=k
1
x+k
3
x
3
+k
5
x
5
+. . . +k
2n+1
x
2n+1 Eq. (4)
In Equation (4), the variable x represents an electrical input signal representative of a desired acoustic output. The coefficients kn for the odd order terms compensate for predominant components of non-linearity of the receiver, mostly at frequencies below the primary mechanical resonance of the receiver. As discussed, odd order terms, for example, the 1st, 3rd and 5th order terms in Equation (4), compensate for symmetric non-linearity of the acoustic receiver. In armature-based receivers, symmetric non-linearity is attributable to magnetic saturation among other characteristics, some of which were discussed above. Thus the polynomial in Equation (4) compensates for non-linearity in the saturation region illustrated in
y=0.28x+0.63x3+0.10x5 Eq. (5)
where y is the “Output” and x is the “Input”.
Generally, the computable non-linear function is selected and optimized for a particular receiver or for a class of receivers and in some implementations for particular processor. The term “optimize” or variations thereof as used herein means the selection of a computable non-linear function or parameters of such a function tending to reduce the output distortion of the receiver, at a specified SPL, when the receiver is driven by an electrical input signal that is pre-distorted by the function compared to the output distortion that would be obtained at the specified SPL when the receiver is driven by the electrical input signal without pre-distortion. Alternatively, optimization may also mean the selection of a computable non-linear function or parameters of such a function tending to increase SPL output of the receiver, for a specified distortion level, when the receiver is driven by an electrical input signal that is pre-distorted by the function compared to the SPL that would be obtained at the specified distortion level when the receiver is driven by the electrical input signal without pre-distortion. Optimization may also mean the selection of a computable non-linear function or parameters of such a function that satisfy a power consumption or processing and memory resource utilization constraints, among other considerations.
Optimization of the computable non-linear function may take many forms, including one or more of the selection of the function form or the selection of function parameters. Polynomial functions can be computed efficiently and selection of form of the computable non-linear function (e.g., odd or even order polynomial, approximated hyperbolic function . . . ) may be dictated, at least in part, by the receiver type or the predominant distortion (symmetric, asymmetric, or both) that requires compensation. Optimization may also occur by selection of a set of one or more parameters of the computable non-linear function. In embodiments where the computable non-linear function is approximated by a summation of a series of terms, the function may be optimized by selection of the order or coefficients of the function. These forms of optimization may be implemented readily and efficiently using a digital processer, for example, by implementing one or more iterative algorithms, examples of which are described below.
In some embodiments, the computable non-linear function (e.g., the polynomials in the examples above) are determined experimentally or using a numerical model of the acoustic receiver. A mathematical algorithm or some other iterative scheme may be used to select the form of the computable non-linear function and to select parameters of the function. Generally the form of the computable non-linear function is selected initially. A trial and error approach may be used to select the computable non-linear function that best compensates for a predominant distortion in a particular type of receiver or for a particular use case. Such an approach may be implemented by generating a pre-distorted excitation signal using different non-linear function forms, applying the pre-distorted excitation signal to a receiver, and evaluating the receiver output. Machine learning techniques or other mathematical algorithms are suitable for this purpose and may be used to facilitate form selection. The function form that results in the most desirable receiver output may be selected. Other than distortion compensation efficacy, the form of the function may be selected based on processor or memory resource requirements. Constraints may be imposed to ensure that the selection of the function does not result in undesirable results.
Upon selection of the form of the computable non-linear function, parameters of the function may be selected or optimized, through an iterative process, to improve performance of the receiver. For non-linear functions that comprise a summation of a series of terms, the order of and coefficients for the terms in the series among other parameters may be optimized through one or more iterative processes. To optimize a set of one or more parameters for a computable non-linear function, a known input signal, like a sinusoid, is pre-distorted using a previously selected non-linear function with a preliminary set of parameters. For example, a preliminary set of parameters could be coefficients or exponents of the polynomial of Equation (5). The preliminary set of parameters used during the first iteration may be based on a best guess, empirical data, or on parameters used previously for a similar receiver. The pre-distorted excitation signal is then applied to the input of a receiver or to a numerical model of the receiver and then the distortion of the resulting acoustic output of the receiver is evaluated. In a subsequent iteration, a new intermediate set of parameters is selected or determined based on the output distortion. The process iterates by making incremental changes to one or more parameters of the selected function based on a measure of the output distortion of the receiver until a desired output is attained. Considerations other than receiver output may also bear on the selection of the function parameters. For example, the form of the function or the number of terms in a series may impact the computational load on processing and memory resources. Additional terms in a series may provide a more linear output, or could be used to reduce clipping of the amplifier. Thus constraints may be imposed to ensure that the selection of the function parameters do not result in undesirable results.
The distortion of the acoustic output of the receiver may be determined using known techniques. For example, the distortion of the output signal may be estimated by computing its Total Harmonic Distortion (THD). Another approach is to compute THD+Noise for the output. Other measures of distortion may also be used. Algorithms for implementing these and other techniques for determining the distortion or linearity of an output signal are well known and not discussed further herein.
One such iterative methodology suitable for selecting or optimizing parameters of a computable non-linear function is a gradient descent algorithm. Other algorithms may also be used. These algorithms generally converge on a local minimum of the function. A minimum is identified when a rate of change of output signal distortion, with respect to some characteristic of the function, approaches zero. In some implementations however it may not be necessary to iterate until a minimum is reached. For example, the non-linear function could be optimized for a specified level of distortion without attaining a local minimum. The optimized function or a set of parameters associated with the function may be stored in a memory device associated with the acoustic receiver for subsequent use.
Optimization of the computable non-linear function may be implemented by a test system after production of the acoustic receiver as discussed in connection with the system 800 of
In
In one implementation, the inverse model generator 802, the pre-distorted excitation signal generator 804, and the distortion calculator 816 are implemented by a digital processing device 818. While the inverse model generator, the pre-distorted signal generator, and the distortion calculator are schematically illustrated as separate functions, these functions may be implemented by executing one or more algorithms on one or more processors represented schematically as processor 818. In some embodiments, the input signal used to optimize the non-linear function is also generated by the processor 818 and thus the input signal source 806 may also be implemented as a signal generating algorithm, like a sine wave generator, executed by the processor. Alternatively, the input signal may be obtained from an external source.
In another implementation, the receiver 810 and the test load 812 of
After selection or optimization of the computable non-linear function, the function is written to a memory device on, or associated with, the receiver for end-use. The memory device may be a discrete component or it may be part of an integrated circuit, like an ASIC, disposed in or on the receiver. The memory device or integrated circuit may also be located on another component used with the receiver or in or on a device or system with which the receiver is integrated. Such a device or system may be a hearing instrument, like a set of headphones or a hearing-aid device, among other examples discussed herein. In
In some implementations, an alternative set of parameters is determined for a characteristic of the acoustic receiver that is different than the initial characteristic. The alternative set of one or more parameters are optimized by iteratively applying intermediate parameters to the receiver with the different characteristic and assessing the output distortion as discussed above. A parameter model representative of the alternative set or sets of parameters is stored in the memory device associated with the receiver in anticipation of changes in a characteristic of the receiver while in use by the end-user. The parameter model generally relates the alternative set or sets of parameters to information indicative of corresponding characteristics of the receiver. The alternative sets of parameters may be generated by the system 800 of
In use, the acoustic receiver having a non-linear transfer characteristic is associated with an electrical signal conditioning apparatus including a processor that generates the pre-distorted electrical excitation signal by applying an electrical input signal (x) representative of a desired acoustic output to a computable non-linear function optimized for the receiver. As discussed above, the pre-distorted electrical excitation signal is the output of the non-linear function. In one implementation, the non-linear function includes at least one term that is proportional to xn, where n is not equal to unity. Generally, when applied to an input of the receiver having a non-linear transfer characteristic, the pre-distorted electrical excitation signal improves the performance of the receiver. In armature-based receivers, an acoustic output of the receiver is produced by deflecting the armature relative to one or more magnets upon applying the pre-distorted electrical excitation signal to a coil of the receiver. In one embodiment, for a specified distortion level, a sound pressure level of the acoustic output produced in response to the pre-distorted electrical excitation signal is greater than a sound pressure level that would be produced, at the specified distortion level, in response to the electrical excitation signal without pre-distortion. In another embodiment, for a specified acoustic sound pressure level, the acoustic output produced in response to the pre-distorted electrical excitation signal has less distortion than an acoustic output that would be produced in response to the electrical excitation signal without pre-distortion. In other implementations, the pre-distorted electrical excitation signal provides some other beneficial effect, like efficient processing and memory resource utilization.
In some embodiments, a processor associated with the receiver generates an updated computable non-linear function to accommodate a change in characteristic of the receiver. The non-linear function is updated with an alternative set of parameters. For this purpose, a condition of the receiver indicative of a change in characteristic is sensed and information indicative of the change is fed back to the processor. Such conditions of the receiver can be detected by monitoring or sensing changes in receiver impedance, front volume pressure, back volume pressure, receiver output SPL, among other detectable conditions of the receiver. The processor generates an updated non-linear function, for example, by applying an updated set of parameters to the non-linear function.
In
As suggested above with reference to
As suggested above, some or all of the functionality of the circuits of
One circumstance that may affect receiver output is a change in the initial steady-state (i.e., rest) position of the reed between the magnets. The initial rest position of the reed is typically a balanced position but in some embodiments it may be unbalanced. Such a change in rest position of the reed may result from an impact or other shock to the receiver. As discussed above, it may be desirable to update the computable non-linear function to accommodate the change in reed rest position. One approach, among others, is to update the function by applying an alternative set of parameters to the function. Table 1 below shows an initial set of polynomial coefficients for an initial rest position of the reed identified as position xo. According to this example, alternative sets of optimized parameters may be computed for different reed rest positions (e.g., +/−x1, +/−x2 . . . ) relative to the initial rest position (i.e., x0). The alternative parameters may be computed by the system of
Generally, there may be more or less parameter sets than those illustrated in Table 1, depending on the particular non-linear function implemented. For example, Equation (4) above requires computation of only coefficients for the 1st, 3rd and 5th order terms. In some embodiments, the data of Table 1 are stored in the memory of the receiver as a look-up table. The look-up table may be subsequently referenced by the receiver processor to determine an updated set of parameters based on a detected change in rest position. The updated parameters may then be applied to the non-linear function for use in pre-distorting the input signal. In some embodiments, the algorithm implementing the look-up table includes interpolation functionality that computes sets of parameters for reed rest positions that are between the rest positions for which the tabulated data was determined. The algorithm implementing the look-up table may also include extrapolation functionality that computes sets of parameters for reed rest positions that are beyond the positions for which the tabulated data was determined. The interpolation and extrapolation functions may be based on linear or non-linear approximations relative to the tabulated data points.
In other embodiments, the alternative sets of parameters of Table 1 may be used to formulate one or more mathematical functions that model the relationship between reed rest positions and corresponding sets of function parameters. The functional model could be a single function or a set of piece-wise linear or non-linear functions. For example, a separate function or set of functions could be used to model each parameter as a function of reed rest position. Such functions may be generated using known curve fitting techniques such as regression analysis or other function approximation methodologies. Like the look-up tables, these functional models may be stored on the receiver for use in updating the set of parameters upon detecting a change in reed rest position. The use of interpolation or extrapolation algorithms may not be required where mathematical functions are used to model the relationship between reed rest position and sensed information indicative of the change in reed rest position. The look-up table or the function relates information from the receiver representative of the change in reed rest position (e.g., impedance, strain, pressure . . . ) to corresponding set of parameters.
A change in reed rest position, also referred to as change in receiver balance, may be detected directly or indirectly. In one implementation, a reed rest position change is detected by monitoring a change in receiver impedance. Receiver impedance may be detected directly by measurement at the receiver coil. Alternatively, a change in reed rest position may be monitored using a reed strain gauge.
Another circumstance that may affect receiver output is a change in frequency response of the receiver. Such a change may be attributable to acoustic leakage in the hearing instrument (e.g., hearing aid, headphones, etc.), ear wax accumulation in a hearing-aid acoustic passage, among other changing characteristics of the receiver or system that occur in use. As suggested above, an optimized set of initial parameters are calculated for an initial frequency response fo of the receiver. Alternative sets of parameters may also be determined for different frequency responses of the receiver. For example the frequency response could be changed by incrementally changing acoustic leakage of the test load and new sets of parameters may be calculated for each incremental change. Alternative sets of parameters may also be determined for incremental changes in acoustic blockage that correspond to wax accumulation in a hearing-aid. The frequency response of the receiver may also be changed based on other changing characteristics of the receiver as well and alternative sets of parameters may be determined accordingly. Like the example above, the alternative sets of parameters are iteratively optimized for each incremental change to an actual receiver. Alternatively, the alternative sets of parameters are optimized using a model of the receiver and the load. The alternative set of parameters optimized for different frequency responses of the receiver may be tabulated as follows:
Generally, there may be more or less parameter sets than illustrated in Table 2 depending on the function implemented (e.g., whether the function is odd or even). In some embodiments, the data in Table 2 are stored in the memory of the receiver as a look-up table. The look-up table may be subsequently used by the receiver to determine updated parameters based on detected changes in various receiver characteristics (including load characteristics) indicative of a change in frequency response. In some embodiments, the algorithm implementing the look-up table includes interpolation or extrapolation functionality that computes sets of parameters for changes in frequency response between or beyond the positions for which the tabulated data was determined, as discussed above. In other embodiments, the parameters in Table 2 are used to formulate one or more mathematical functions that model the relationship between frequency response and information indicative of the change in receiver characteristic. For example, a separate function could be used to model each parameter as a function of frequency response. Such functional models may be generated using known curve fitting techniques like as regression analysis or other function approximation methodologies as discussed above. Like the look-up tables, these functions may be stored on the receiver for use in updating the parameters upon detecting a condition indicated of a change in frequency response.
The change in receiver frequency response may be detected by monitoring changes in resonance peaks and other characteristics of the frequency response. In one embodiment, the frequency response of the receiver is monitored using a Fast Fourier transform (FFT) or Discrete Fourier Transform (DFT) applied to an electrical signal representative of the receiver output. The electrical signal may be generated using a microphone disposed at the output of the receiver.
In some embodiments, it may be desirable to control the amplifier output for changes in a characteristic of the receiver. For voltage driven receivers, it may be desirable to adjust the output (e.g., magnitude or phase) of a voltage amplifier to compensate for a changing impedance of the receiver. For example, the magnitude or phase of the voltage amplifier output may be adjusted as the receiver impedance changes to provide a more constant current level or to control the phase of the amplifier output signals. The receiver impedance can be measured directly at the receiver coil and sensed changes may be used to control the voltage of the amplifier. For current amplifier driven receivers, it may be desirable to adjust the output (e.g., amplitude or phase) to compensate for changing receiver characteristics. In
In one embodiment, the computable non-linear function or parameters of the function are selected by the electrical circuits associated with the receiver system rather than by a test system like the system 800 of
While the present disclosure and what is presently considered to be the best mode thereof has been described in a manner that establishes possession by the inventors and that enables those of ordinary skill in the art to make and use the same, it will be understood and appreciated that there are many equivalents to the exemplary embodiments disclosed herein and that myriad modifications and variations may be made thereto without departing from the scope and spirit of the disclosure, which is to be limited not by the exemplary embodiments but by the appended claims.
Filing Document | Filing Date | Country | Kind |
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PCT/US2017/056873 | 10/17/2017 | WO | 00 |
Number | Date | Country | |
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62409341 | Oct 2016 | US |