HEARING AID COMPRISING A FEEDBACK CONTROL SYSTEM

Abstract

A hearing aid includes a forward path including a) an input transducer proving an electric input signal, b) a hearing aid processor, and c) an output transducer. The hearing aid further includes d) a feedback control system having d1) a feedback path estimator including an adaptive filter configured to provide an estimate of a current feedback path from the output transducer to at least one input transducer, the feedback path estimator being controllable via a feedback estimation control input, and d2) a combination unit in the forward path configured to subtract the estimate (v′) of the current feedback path signal (v) from a signal of the forward path (y) to provide a feedback corrected signal (e), and e) a detector for providing the feedback estimation control input in dependence of an offset control signal indicative of an offset in the electric input signal or a signal originating therefrom.

Claims

1. A hearing aid adapted to be worn by a user, or for being partially or fully implanted in the head of the user, comprising a forward path comprising at least one input transducer for converting a sound to corresponding at least one electric input signals representing said sound, the sound comprising target signal components and noise components, a hearing aid processor for providing a processed signal (u) in dependence of said at least one electric input signal, and an output transducer for providing stimuli perceivable as sound to the user in dependence of said processed signal, the hearing aid further comprising a feedback control system comprising a feedback path estimator comprising an adaptive filter configured to provide an estimate of a current feedback path from said output transducer to said at least one input transducer in dependence of said processed signal (u) and said at least one electric input signal (y) or a signal (e) originating therefrom, the feedback path estimator being controllable via a feedback estimation control input, and a combination unit in the forward path configured to subtract said estimate (v′) of the current feedback path signal (v) from a signal of the forward path (y) to provide a feedback corrected signal (e), and a detector for providing said feedback estimation control input in dependence of an offset control signal indicative of an offset in the at least one electric input signal or a signal originating therefrom.

2. A hearing aid according to claim 1 wherein an adaptation rate of the adaptive filter of the feedback path estimator is controllable via the feedback estimation control input provided by the detector.

3. A hearing aid according to claim 1 wherein the adaptive filter of the feedback path estimator comprises a Least Mean Square (LMS) or a Normalized LMS (NLMS) algorithm.

4. A hearing aid according to claim 1 comprising a filter bank allowing processing of the hearing aid to be performed in frequency sub-bands.

5. A hearing aid according to claim 3 wherein the feedback path estimator is configured to modify a normalization term of said Normalized LMS (NLMS) algorithm over different frequency sub-bands via said feedback estimation control input.

6. A hearing aid according to claim 1 wherein the detector is configured to provide said feedback estimation control input in dependence of a detected tonality of the electric input signal or a signal originating therefrom.

7. A hearing aid according to claim 6 wherein adaptation rate is decreased in case tonality above a threshold is detected.

8. A hearing aid according to claim 4 wherein the adaptation rate is controlled over several frequency sub-bands in dependence of a normalization over said frequency sub-bands.

9. A hearing aid according to claim 4 wherein the adaptation rate is controlled over several frequency sub-bands using min, max, mean or median operators.

10. A hearing aid according to claim 1 comprising a level detector configured to detect level changes in the at least one electric input signal or a signal originating therefrom.

11. A hearing aid according to claim 1 wherein the detector is configured to detect an offset as well as an onset in the at least one electric input signal or a signal originating therefrom.

12. A hearing aid according to claim 11 wherein the detector is configured to provide an offset control signal indicative of an offset as wells as an onset control signal indicative of an onset in the at least one electric input signal or a signal originating therefrom.

13. A hearing aid according to claim 12 wherein the detector is configured to provide said feedback estimation control input in dependence of said offset control signal as well as said onset control signal.

14. A hearing aid according to claim 1 being constituted by or comprising an air-conduction type hearing aid, a bone-conduction type hearing aid, a cochlear implant type hearing aid, or a combination thereof.

15. A method of operating a hearing aid adapted to be worn by a user, or for being partially or fully implanted in the head of the user, the hearing aid comprising a forward path comprising at least one input transducer for converting a sound to corresponding at least one electric input signals representing said sound, the sound comprising target signal components and noise components, a hearing aid processor for providing a processed signal (U) in dependence of said at least one electric input signal, and an output transducer for providing stimuli perceivable as sound to the user in dependence of said processed signal, the method comprising adaptively providing an estimate of a current feedback path from said output transducer to said at least one input transducer in dependence of said processed signal (u) and said at least one electric input signal (y) or a signal (e) originating therefrom, controlling the estimate of a current feedback path via a feedback estimation control input, and subtracting said estimate (v′) of the current feedback path signal (v) from a signal of the forward path (y) to provide a feedback corrected signal (e), providing said feedback estimation control input in dependence of an offset control signal indicative of an offset in the at least one electric input signal or a signal originating therefrom.

16. A hearing aid according to claim 2 wherein the adaptive filter of the feedback path estimator comprises a Least Mean Square (LMS) or a Normalized LMS (NLMS) algorithm.

17. A hearing aid according to claim 2 comprising a filter bank allowing processing of the hearing aid to be performed in frequency sub-bands.

18. A hearing aid according to claim 3 comprising a filter bank allowing processing of the hearing aid to be performed in frequency sub-bands.

19. A hearing aid according to claim 4 wherein the feedback path estimator is configured to modify a normalization term of said Normalized LMS (NLMS) algorithm over different frequency sub-bands via said feedback estimation control input.

20. A hearing aid according to claim 2 wherein the detector is configured to provide said feedback estimation control input in dependence of a detected tonality of the electric input signal or a signal originating therefrom.

Description

BRIEF DESCRIPTION OF DRAWINGS

[0092] The aspects of the disclosure may be best understood from the following detailed description taken in conjunction with the accompanying figures. The figures are schematic and simplified for clarity, and they just show details to improve the understanding of the claims, while other details are left out. Throughout, the same reference numerals are used for identical or corresponding parts. The individual features of each aspect may each be combined with any or all features of the other aspects. These and other aspects, features and/or technical effect will be apparent from and elucidated with reference to the illustrations described hereinafter in which:

[0093] FIG. 1 shows a first embodiment of a hearing aid comprising a feedback control system according to the present disclosure, and

[0094] FIG. 2 shows a second embodiment of a hearing aid comprising a feedback control system according to the present disclosure.

[0095] The figures are schematic and simplified for clarity, and they just show details which are essential to the understanding of the disclosure, while other details are left out. Throughout, the same reference signs are used for identical or corresponding parts.

[0096] Further scope of applicability of the present disclosure will become apparent from the detailed description given hereinafter. However, it should be understood that the detailed description and specific examples, while indicating preferred embodiments of the disclosure, are given by way of illustration only. Other embodiments may become apparent to those skilled in the art from the following detailed description.

DETAILED DESCRIPTION OF EMBODIMENTS

[0097] The detailed description set forth below in connection with the appended drawings is intended as a description of various configurations. The detailed description includes specific details for the purpose of providing a thorough understanding of various concepts. However, it will be apparent to those skilled in the art that these concepts may be practiced without these specific details. Several aspects of the apparatus and methods are described by various blocks, functional units, modules, components, circuits, steps, processes, algorithms, etc. (collectively referred to as “elements”). Depending upon particular application, design constraints or other reasons, these elements may be implemented using electronic hardware, computer program, or any combination thereof.

[0098] The electronic hardware may include micro-electronic-mechanical systems (MEMS), integrated circuits (e.g. application specific), microprocessors, microcontrollers, digital signal processors (DSPs), field programmable gate arrays (FPGAs), programmable logic devices (PLDs), gated logic, discrete hardware circuits, printed circuit boards (PCB) (e.g. flexible PCBs), and other suitable hardware configured to perform the various functionality described throughout this disclosure, e.g. sensors, e.g. for sensing and/or registering physical properties of the environment, the device, the user, etc. Computer program shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executables, threads of execution, procedures, functions, etc., whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise.

[0099] The present application relates to the field of hearing aids, in particular to feedback control in hearing aids. The present disclosure proposes a feedback canceller implemented using an adaptive filter and based on or utilizing sound offsets (e.g. in addition to sound onsets) to control the adaptation rate of the adaptive filter. The present disclosure proposes additional aspects of sound onsets, and other signal properties, such as tonality, to be related to adaptive filter adaptation speed, including normalization strategy and step size control.

[0100] Feedback cancellation systems using adaptive filters can be disturbed by sound onsets and/or transients. The onsets and transients can contribute a large gradient error to the adaptive filter adaptations, and thereby feedback performance can be degraded as the consequence.

[0101] In contrast, the sound offset situations are just the opposite of the onsets/transients, whereas the gradient to the adaptive filters consists of a very small error, and we can/should utilize this for the adaptive filter estimation.

[0102] In following, a strategy as to how the effective adaptation speed of the adaptive filter can be controlled, when considering signal onsets, offsets, and tonality.

[0103] FIG. 1 shows a first embodiment of a hearing aid comprising a feedback control system according to the present disclosure. FIG. 1 illustrates some basic components of a hearing aid: A) the forward path, B) an (unintentional, external) acoustic feedback path, and C) an electrical feedback cancellation path for reducing or cancelling acoustic feedback induced by the acoustic feedback path (FBP). The forward path comprises an input transducer (here a microphone (M)) for receiving an acoustic input from the environment (‘Acoustic input’ in FIG. 1) and providing an analogue or digital electric input signal y(n). The input transducer may comprise an analogue to digital converter (AD-converter) to provide the electric input signal as a stream of digital samples (y(n), n being a discrete time index. The forward path further comprises a digital signal processor (DSP) for adapting the signal to the needs of a wearer of the hearing aid (e.g. by providing a frequency and level dependent gain (amplification or attenuation) according to the user's needs, e.g. hearing impairment). The digital signal processor (DSP) provides a processed signal (u(n)) in dependence of the input signal (e(n) in FIG. 1) and a user's hearing profile. e.g. an audiogram). The forward path further comprises an output transducer (here a loudspeaker (SPK)) for generating an acoustic output (‘Acoustic output’ in FIG. 1) to the wearer of the hearing aid in dependence of the processed signal (u(n)). The output transducer may comprise a digital to analogue converter (DA-converter) for converting the processed (digital) signal u(n) to an analogue signal (as appropriate for the specific solution). An (external, unintentional) Acoustic Feedback path (FBP) from the output transducer to the input transducer is indicated. The electrical feedback cancellation path comprises an adaptive filter (Algorithm, Filter), whose filtering function (Filter) (e.g. defined by filter coefficients) is controlled by a prediction error algorithm (Algorithm), e.g. an LMS (Least Means Squared) algorithm, in order to predict and preferably cancel the part of the microphone signal (y(n)) that is caused by feedback from the loudspeaker (SPK) of the hearing aid (as indicated in FIG. 1 by bold arrow FBP). The adaptive filter (in FIG. 1 shown to comprise a ‘Filter’ part and a prediction error ‘Algorithm’ part) is aimed at providing a good estimate of the external feedback path from the electrical input of the output transducer (e.g. of a DA-converter) to the electrical output of the input transducer (e.g. of the AD-converter). The prediction error algorithm uses a reference signal (here the output signal u(n) from the digital signal processor DSP) together with the (feedback corrected) input signal e(n) from the microphone (the error signal) to find the setting (filter coefficients) of the adaptive filter that minimizes the prediction error when the reference signal is applied to the adaptive filter. The acoustic feedback is cancelled (or at least reduced) by subtracting (cf. SUM-unit ‘+’ in FIG. 1) the estimate (v′(n)) of the acoustic feedback path provided by the output of the Filter part of the adaptive filter from the input signal (y(n)) from the microphone (M) comprising acoustic feedback (v(n) to provide the feedback corrected input signal (e(n)=y(n)−v′(n)). The dotted rectangle indicates that the enclosed blocks of the hearing aid (HD) are located in the same physical body (in the depicted embodiment). Alternatively, the microphone and processing unit and feedback cancellation system can be housed in one physical body and the output transducer in a second physical body, the first and second physical bodies being in communication with each other. Other divisions of the listening device in separate physical bodies can be envisaged.

[0104] The gradient for the adaptive filter estimation consists of two part, the correct gradient information to minimize the adaptive filter output, and the incorrect distortion due to the incoming signal x(n).

[0105] The gradient to each adaptive filter coefficient is computed as e(n)u(n)=(x(n)+v(n)−v′(n))u(n), and it is derived by minimizing the cost function E[|e(n)|.sup.2] with respect to the adaptive filter h′(n). Hence, e(n) u(n) is used as the gradient for the first adaptive filter coefficient, and e(n) u(n−1) as the gradient for the second coefficient, etc. Furthermore, the part (v(n)-v′(n))u(n) provides the correct gradient, whereas x(n)u(n) gives an error. Each signal e(n), u(n), etc., may be a frequency sub-band signal (e.sub.k(n), u.sub.k(n), etc., where sub-script k denotes the k.sup.th frequency sub-band), e.g. in case of a frequency domain adaptive filter, etc.

[0106] In more detail, the gradient is derived as outlined in the following:

[0107] First, the cost function J(n) to be minimized is.

[00001]$\begin{array}{c}J\left(n\right)=E\left[{.Math.e\left(n\right).Math.}^2\right]\\ {=E[.Math.y\left(n\right)-u^T\left(n\right)*h’\left(n\right).Math.}^2]\\ =E[y^2\left(n\right)-2*y\left(n\right)*u^T\left(n\right)*h’\left(n\right)+u^T\left(n\right)*h’\left(n\right)*u^T\left(n\right)*h’\left(n\right))\end{array}$

[0108] Then, take the partial derivative

[00002]$\begin{array}{c}dJ\left(n\right)/dh’\left(n\right)=E[-2*y\left(n\right)*u\left(n\right)+2*u^T\left(n\right)*h’\left(n\right)*u\left(n\right)]\\ =E[-2*(y\left(n\right)-u^T\left(n\right)*h’\left(n\right))*u\left(n\right)]\\ =-2*E\left[e\left(n\right)*u\left(n\right)\right]\end{array}$

[0109] Where E[.] denotes the expectation operator, and * represents the mathematical multiplication operator, and for the adaptive estimation, an estimate of the negative gradient e(n)*u(n) is used for the adaptation, where u(n)=[u(n), u(n−1), . . . , u(n−L+1)], and L is the length of the adaptive filter. For convenience, we refer to e(n)*u(n) as the gradient used for the adaptive filter estimation. The elements of the adaptive filter vector h′(n) are also referred to as the ‘filter coefficients’ at the given time index n. Each adaptive filter coefficient, may be identified by a ‘coefficient index’, 1=0, 1 . . . L−1, whereby the adaptive filter vector h′(n) can be expressed as:

h′(n)=[h.sub.0′(n),h.sub.1′(n), . . . h.sub.L−1′(n)]

[0110] In the extreme case of onsets/transients, x(n) is dominant compared to v(n)-v′(n), and hence the gradient e(n)u(n) is dominated by the undesired part of x(n)u(n), and ideally we should not use this undesired gradient in the feedback path estimation. This can e.g. be achieved by (at least) reducing the adaptation speed of the adaptive filter.

[0111] However, in the case of offsets, where x(n)≈0, and the gradient is dominated by (v(n)-v′(n))u(n), and we should make use of it, by e.g. increasing the adaptation speed of the adaptive filter.

[0112] The effect of the onset/offset on the adaptive filter based on an example LMS algorithm is:

h′(n)=h′(n−1)+μ*e(n)*u(n),

h′(n)=h′(n−1)+μ*(x(n)+v(n)−v′(n))*u(n)

h′(n)=h′(n−1)+μ*(x(n)+u.sup.T(n)*(h(n)−h′(n)))*u(n),

[0113] where ‘*’ represents a mathematical multiplication operator, either for scalar values, or for vectors and matrices.

[0114] For onsets, where x.sup.2(n)>>u.sup.2(n), or |x(n)|>>|u(n)| (i.e. where the magnitude of the x(n) term is greater than the magnitude of the u(n) term),

h′(n)˜=h′(n−1)+μ*x(n)*u(n),

[0115] and an incorrect gradient of x(n)*u(n) is used, (where * represents the mathematical multiplication operator).

[0116] However, for offsets, where u.sup.2(n)>>x.sup.2(n), or |u(n)|>>|x(n)| (i.e. where the magnitude of the u(n) term is greater than the magnitude of the x(n) term),

h′(n)˜=h′(n−1)+μ*u.sup.T(n)*(h(n)−h′(n))*u(n).

[0117] and a correct gradient of u.sup.T(n)*(h(n)−h′(n))*u(n)=(v(n)−v′(n))*u(n) is used. In practice, this can be done by detecting onsets/offsets and then control the adaptation speed (e.g. via a step size μ). An onset/offset detection (cf. detector. DET in FIG. 1) can be simply realized by comparing frame-based signal energy over a number of different time/frequency indices of the signal y(n) or e(n), cf. FIG. 1. An appropriate delay between the frames under consideration can e.g. be related to the feedback path delay. The output of the (onset/offset) detector (DET) is then used to control the adaptation speed (e.g. step size) in the adaptive algorithm (cf. signal d(n) from the detector (DET) to the algorithm part (Algorithm) of the adaptive filter. Onset or transient detection is e.g. discussed in EP3252074A1.

[0118] The onset/offset can e.g. be determined by first computing the ratio r(n) as

r(n)=E[y.sup.2(n)]/E[y.sup.2(n−D)].

[0119] where D is a delay, e.g. the loop delay, or a delay corresponding to the forward path (also called forward path delay), or, preferably, a delay corresponding to the feedback path (also called feedback path delay).

[0120] The value of the feedback path delay can for example be between 0.2 millisecond and 0.5 millisecond. The value of the feedback path delay is less than the value of the loop delay, as the loop delay is the sum of the feedback path delay and the forward path delay, and as the value of the forward path delay is typically between 5 milliseconds and 10 milliseconds.

[0121] Then, an onset is detected if r(n)>threshold1, and an offset is detected if r(n)<threshold2, where threshold1 is a positive number bigger than 1 such as 2, 4, 8 . . . , threshold2 is a positive number smaller than 1, such as 0.5, 0.25, 0.125 . . . .

[0122] In practice, E[y.sup.2(n)] and E[y.sup.2(n−D)] are calculated by averaging y.sup.2(n) and y.sup.2(n−D) over time. This can also be done in (e.g. P) concatenated data frames (e.g. [Frame (‘now’−P+1), . . . , Frame (‘now’−1), Frame (‘now’)]).

[0123] Furthermore, the signal property of x(n) may also be used to control the adaptation speed. If the x(n) has a tonal behavior (e.g., flute music or many alarm signals), it is also desirable to decrease the adaptation speed of the adaptive filter, to slow down or even stop the adaptation in such a case to avoid a biased adaptive filter estimation. In practice, the signal x(n) is not available for processing, however, the signal y(n) or e(n) can then be used as an approximation for analyzing the property of x(n). The detector (DET) may thus be configured to detect a tonality parameter (e.g. a tone detector detecting specific narrow-band frequency content in a signal of the forward path of the hearing aid, e.g. (as here) in electric input signal (y(n) in FIG. 1), or in a feedback corrected input signal (e(n) in FIG. 1).

[0124] The hearing aid (HD) comprises a ‘forward’ (or ‘signal’) path for processing an audio signal between the input transducer (microphone M in FIG. 1) and the output transducer (loudspeaker SPK in FIG. 1). The hearing aid (HD) comprises an ‘analysis’ path comprising functional components for analyzing signals and/or controlling processing of the forward path (in FIG. 1, e.g., a) the detector (DET) determining characteristics of a signal of the forward path and controlling an adaptive filter (AF, via signal d(n)), and b) the adaptive filter (AF) for estimating acoustic feedback and providing a modification signal (v′(n)) to the forward path, etc.). Some or all signal processing of the analysis path and/or the forward path may be conducted in the frequency domain, in which case the hearing aid comprises appropriate analysis and synthesis filter banks. Some or all signal processing of the analysis path and/or the forward path may be conducted in the time domain.

[0125] The adaptation speed control may be carried out differently over frequencies, as the signal onset, offset, and tonality can be frequency limited. Moreover, it is also desirable that the adaptation speed control to handle onset, offset, and tonality have a wide (r) coverage over frequencies to ensure effectiveness and robustness, this is typically done by making the adaptation speed control to include neighbor frequency bands or a wide frequency region. For instance, one can divided the whole frequency range of the signal into different frequency regions (either uniform or non-uniform), and if an adaptation speed control is determined to be beneficial in one frequency region, it then always includes the neighbor frequency regions. In an example Normalized Least Mean Square (NLMS) algorithm, the adaptation speed control can be done by changing the step size, or by modifying the normalization term over different frequencies.

[0126] An example of changing the step size and/or normalization of the NLMS algorithm is provided below. The NLMS adaptation is expressed by

h′(n)=h′(n−1)+μ(n)*u(n)*e(n)/(s1(n)*∥u(n)∥.sup.2+s2(n)),

[0127] The step size μ(n), and the scaling factors s1(n) and s2(n) are time varying.

[0128] In case of onsets/onsets, it would be appropriate to reduce/increase the step size μ(n), and/or to increase/reduce the scaling factors s1(n) and s2(n). The scaling factor s1(n) may e.g. take on values (e.g. in steps): . . . , 2.sup.−3, 2.sup.−2 . . . , 2.sup.2, 2.sup.3, . . . . The scaling factor s2(n) may e.g. take on values (e.g. in steps): . . . , 10.sup.−2, 10.sup.−1, 10.sup.0, 10.sup.1, 10.sup.2, . . . .

[0129] Furthermore, operations such as min, max, mean or median can be used to better control the adaptation over frequencies (to include a wider frequency region).

[0130] In an example case, taking the max value of the normalization terms over neighboring frequencies and apply it to all these neighboring frequencies can be beneficial if there is a high tonality in the signal x(n). The effect is a lowered adaptation speed in a bigger frequency region, to avoid the biased estimation problem in the adaptive filter.

[0131] In another example case, taking the min (or max) value of step size values over neighboring frequencies and apply it to all these neighboring frequencies can be beneficial if there is an onset (or offset) in the signal x(n).

[0132] FIG. 2 shows a second embodiment of a hearing aid comprising a feedback control system according to the present disclosure. The embodiment of FIG. 2 is similar to the embodiment of FIG. 1, apart from the specifically included analysis (A) and synthesis (S) filter banks included in the forward path of the hearing aid (HD) of FIG. 2. The location of the filter bank in the forward path of FIG. 2 indicates that all processing may be performed in the frequency domain (as indicated by signal names having a frequency index k as subscript and a time frame index m, e.g. y.sub.k(m), instead of time index n in FIG. 1. The analysis and synthesis filter banks may, however, be located elsewhere in the circuitry of the hearing device, e.g. allowing the subtraction of the feedback path estimate v′(n) from the electric input signal y(n) to be performed in the time domain. Considerations regarding minimizing processing delays due to the filter bank may decide where the domain transform is located (if at all used). The detection of onsets/offsets in the electric input signal may e.g. be based on the time domain electric input signal y(n) (cf. e.g. EP3252074A1). Other considerations may decide or influence the location of the filter bank(s).

[0133] It is intended that the structural features of the devices described above, either in the detailed description and/or in the claims, may be combined with steps of the method, when appropriately substituted by a corresponding process.

[0134] Embodiments of the disclosure may e.g. be useful in applications such as hearing aids or other devices or systems where feedback estimation is relevant.

[0135] As used, the singular forms “a.” “an.” and “the” are intended to include the plural forms as well (i.e. to have the meaning “at least one”), unless expressly stated otherwise. It will be further understood that the terms “includes,” “comprises,” “including.” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will also be understood that when an element is referred to as being “connected” or “coupled” to another element, it can be directly connected or coupled to the other element but an intervening element may also be present, unless expressly stated otherwise. Furthermore, “connected” or “coupled” as used herein may include wirelessly connected or coupled. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items. The steps of any disclosed method are not limited to the exact order stated herein, unless expressly stated otherwise.

[0136] It should be appreciated that reference throughout this specification to “one embodiment” or “an embodiment” or“an aspect” or features included as “may” means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosure. Furthermore, the particular features, structures or characteristics may be combined as suitable in one or more embodiments of the disclosure. The previous description is provided to enable any person skilled in the art to practice the various aspects described herein. 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.

[0137] The claims are not intended to be limited to the aspects shown herein but are to be accorded the full scope consistent with the language of the claims, wherein reference to an element in the singular is not intended to mean “one and only one” unless specifically so stated, but rather “one or more.” Unless specifically stated otherwise, the term “some” refers to one or more.

REFERENCES

[0138] EP3252074A1 (Oticon) Jun. 12, 2017 [0139] WO2003081947A1 (Oticon) Feb. 10, 2003