HEARING DEVICE COMPRISING A FEEDBACK CONTROL SYSTEM

Abstract

A hearing aid comprises a) at least one input transducer for providing at least one electric input signal representing said sound; b) an output transducer for providing stimuli perceivable to the user as sound; c) a feedback control system configured to minimize feedback from said output transducer to said at least one input transducer, and to at least provide a feedback corrected version of said at least one electric input signal; and d) an audio signal processor configured to apply one or more processing algorithms to said feedback corrected version of said at least one electric input signal, and to provide a processed signal in dependence thereof. The feedback control system is based on a machine learning model receiving input data at least representing said at least one electric input signal; and said processed signal; and providing said feedback corrected version of the at least one electric input signal as an output. A method of training a machine learning model is further disclosed.

Claims

1. A hearing aid adapted for being worn by a user at or in an ear of the user, the hearing aid comprising at least one input transducer for converting sound in an environment around the user to at least one electric input signal representing said sound; an output transducer for converting an output signal provided in dependence of said least one electric input signal to stimuli perceivable to the user as sound; a feedback control system configured to minimize feedback from said output transducer to said at least one input transducer, and to at least provide a feedback corrected version of said at least one electric input signal; and an audio signal processor configured to apply one or more processing algorithms to said feedback corrected version of said at least one electric input signal, and to provide a processed signal in dependence thereof>; wherein the feedback control system is based on a machine learning model receiving input data at least representing said at least one electric input signal; and said processed signal; and wherein the feedback control system is configured to provide said feedback corrected version of the at least one electric input signal as an output.

2. A hearing aid according to claim 1 wherein said feedback control system is configured to provide said output signal as a further output.

3. A hearing aid according to claim 1 wherein said machine learning model is configured to receive further input data representing information about said one or more processing algorithms.

4. A hearing aid according to claim 1 wherein said feedback control system is configured to provide a control input signal to the audio signal processor as a further output, said control input signal comprising parameters providing inputs to said one or more processing algorithms.

5. A hearing aid according to claim 1 wherein said machine learning model is trained with input data at least representing said at least one electric input signal; and said processed signal.

6. A hearing aid according to claim 5 wherein said machine learning model is trained with further input data representing information about said one or more processing algorithms.

7. A hearing aid according to claim 1 wherein the machine learning model is trained with synthetic input data at least representing said external part of said at least one electric input signal; said feedback part of said at least one electric input signal; and said processed signal; and with synthetic output data at least representing said feedback corrected version of the at least one electric input signal.

8. A hearing aid according to claim 1 wherein said processed signal from the processor provides said output signal.

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

10. A hearing aid according to claim 1 comprising at least one analysis filter bank for providing said at least one electric input signal in a time-frequency domain representation.

11. A hearing aid according to claim 10 wherein the input data to the machine learning model are said at least one electric input signal; and said processed signal, which for each time index l each are arranged as a vector with K elements, K being the number of frequency bands in the time-frequency domain representation (k,l).

12. A hearing aid according to claim 1 wherein the output transducer comprises a) a loudspeaker for providing said stimuli as an acoustic signal to the user, or b) a vibrator for providing said stimuli as mechanical vibration of a skull bone to the user.

13. A method of training a machine learning model for use in a feedback control system of a hearing aid, the hearing aid comprising at least one input transducer for converting input sound in an environment around the user to at least one electric input signal representing said input sound; an output transducer for converting an output signal provided in dependence of said at least one electric input signal to stimuli perceivable to the user as sound; wherein said input sound comprises an external sound and a feedback sound generated by said output transducer and leaked to said input transducer via feedback path, and wherein said at least one electric input signal likewise comprises an external part originating from said external sound and a feedback part originating from said feedback sound; a feedback control system for minimizing said feedback part of said at least one electric input signal and at least providing a feedback corrected version of said at least one electric input signal, the feedback control system comprising said machine learning model; and an audio signal processor configured to apply one or more processing algorithms to said feedback corrected version of said at least one electric input signal and to provide a processed signal in dependence thereof; wherein the machine learning model is trained with synthetic input data at least representing said external part of said at least one electric input signal; said feedback part of said at least one electric input signal; and said processed signal; and with synthetic output data at least representing said feedback corrected version of the at least one electric input signal.

14. A method according to claim 13 wherein said synthetic output data further represents said output signal.

15. A method according to claim 13 wherein said synthetic input data further represents information about said one or more processing algorithms.

16. A method according to claim 13 wherein said synthetic output data further represents parameters providing inputs to said one or more processing algorithms.

17. A method according to claim 13 wherein at least said synthetic output data are generated by computer simulation.

18. A method according to claim 13 wherein at least said synthetic output data are generated by computer simulation to reflect an imaginary and perfect feedback control system reacting instantly and accurately to feedback changes.

19. A method according to claim 13 wherein an imaginary and perfect feedback control system is used to generate data for the training of the machine learning model, both in static feedback situations and with dynamic feedback path changes.

20. A method according to claim 13 wherein the input signals for the training of the machine learning model comprise white noise, or speech, or music signals, or a mixture thereof.

21. A hearing aid comprising at least one input transducer for converting input sound in an environment around the user to at least one electric input signal representing said input sound; an output transducer for converting an output signal provided in dependence of said at least one electric input signal to stimuli perceivable to the user as sound; wherein said input sound comprises an external sound and a feedback sound generated by said output transducer and leaked to said input transducer via feedback path, and wherein said at least one electric input signal likewise comprises an external part originating from said external sound and a feedback part originating from said feedback sound; a feedback control system for minimizing said feedback part of said at least one electric input signal and at least providing a feedback corrected version of said at least one electric input signal, the feedback control system comprising said machine learning model; and an audio signal processor configured to apply one or more processing algorithms to said feedback corrected version of said at least one electric input signal and to provide a processed signal in dependence thereof; wherein the machine learning model is trained according to the method of claim 13.

Description

BRIEF DESCRIPTION OF DRAWINGS

[0114] 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:

[0115] FIG. 1 shows a state-of-the-art feedback control system using an adaptive filter,

[0116] FIG. 2 shows examples of state-of-the-art forward path processing for feedback control purposes,

[0117] FIG. 3 shows a block diagram of a hearing device comprising a feedback control system according to the present disclosure,

[0118] FIG. 4 shows a block diagram of a hearing device comprising a machine learning based feedback control system according to the present disclosure,

[0119] FIG. 5 shows a block diagram of a hearing device comprising a machine learning based feedback control system according to the present disclosure, wherein information from the hearing device processing unit is used as inputs to the machine learning based feedback control system, and acoustic information from the learning model are provided to the hearing aid processing unit,

[0120] FIG. 6 shows a block diagram of a hearing device comprising a machine learning based feedback control system according to the present disclosure, wherein a simpler model comprising that the output signal u(n) is the input to the model is used,

[0121] FIG. 7A schematically illustrates a first example of input and output vectors for a machine learning model according to the present disclosure;

[0122] FIG. 7B schematically illustrates a second example of input and output vectors for a machine learning model according to the present disclosure;

[0123] FIG. 7C schematically illustrates an example of historic context of an input vector for a machine learning model according to the present disclosure; and

[0124] FIG. 7D schematically illustrates an example of historic content of an input vector formed as concatenated individual vectors comprising data for a number of different input signals for a machine learning model according to the present disclosure and a corresponding output vector comprising concatenated individual vectors comprising data for a number of different output signals, and

[0125] FIG. 8A shows a first embodiment of a flow diagram of a method of training a machine learning model for use in a feedback control system of a hearing aid; and

[0126] FIG. 8B shows a second embodiment of a flow diagram of a method of training a machine learning model for use in a feedback control system of a hearing aid.

[0127] 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.

[0128] 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

[0129] 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.

[0130] 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.

[0131] The present application relates to the field of hearing devices, e.g. hearing aids, in particular to feedback control in such devices.

[0132] Modern machine learning techniques provide a new tool, which may deliver a completely new generation of feedback control systems, which can solve the biased estimation problem without compromising sound quality, and it can better handle critical feedback situations and hence better ensure that the optimal gain can be provided to the users.

[0133] FIG. 1 shows a simplified block diagram of a hearing aid comprising a state-of-the-art feedback control system. The hearing aid is adapted to be located at or in an ear of a user. The hearing aid may be configured to compensate for a hearing loss of the user. The hearing aid comprises a forward path for processing an input signal representing sound in the environment (x(n), v(n), n representing time). The forward path comprises at least one input transducer (e.g. one or more microphones, here one microphone (M)) for picking up sound from the environment of the hearing aid and providing an electric input signal (y(n)). The forward path further comprises an audio signal processor (Processing) for processing a feedback corrected version (e(n)) of the electric input signal (y(n)) and providing a processed signal (u(n)) based thereon. The forward path further comprises an output transducer (SPK, e.g. a loudspeaker or a vibrator) for generating stimuli perceivable by the user as sound based on the processed signal (u(n)). The hearing aid further comprises a feedback control system for feedback control (e.g. attenuation or removal). The feedback control system comprises a feedback estimation unit (embodied as an adaptive filter) for estimating a current feedback path (Feedback Path h(n)) from the output transducer (SPK) to input transducer (M) (cf. acoustic input signal v(n) to the microphone (M)) and providing an estimate (v′(n)) thereof. The adaptive filter comprises an algorithm part (Adaptive algorithm) and variable filter part (Time Varying Filter h′(n)). The algorithm part comprises an adaptive algorithm for providing update filter coefficients to the algorithm part in dependence of the feedback corrected version (e(n)) of the electric input signal (y(n)) and the output signal (u(n)). Based on the updated filter coefficients, the variable filter part provides the estimate (v′(n)) of the feedback path signal (v(n)) by filtering the output signal (u(n)). A further component of the feedback control system shown in FIG. 1 is a combination unit (her a summation unit, ‘+’) for combining the electric input signal (y(n)) and the estimated feedback signal (v′(n)) provided by the adaptive filter (specifically by the filter part (Time Varying Filter h′(n))). The feedback path estimate (v′ (n)) is (here subtracted from input signal (y(n)) in summation unit (+), to provide the feedback corrected signal (e(n)).

[0134] For the feedback control purpose, the processing unit in the forward path typically consists of a decorrelation block, a gain control block, and optionally a fast feedback reduction block. This is illustrated in FIG. 2.

[0135] FIG. 2 shows examples of state-of-the-art forward path processing for feedback control purposes. In FIG. 2, the decorrelation method is implemented by an introduction of a frequency shift (cf. unit FS). Further a fast feedback reduction block (STM proc.) provides fast feedback reduction in case a risk of feedback is detected (cf. e.g. EP3139636A1, EP3291581A2). A further gain control block (Gain Ctrl.) may provide gain reduction in case a risk of feedback is detected. Together with the adaptive filter h′(n), they may form a state-of-the-art feedback control system.

[0136] In our envisioned future machine learning based feedback control system, we can in principle replace all these blocks with a machine learning block, as shown in FIG. 3.

[0137] FIG. 3 shows a block diagram of a hearing device comprising a feedback control system according to the present disclosure. The hatched blocks will be replaced by a machine learning based system. This new system can be redrawn into FIG. 4.

[0138] FIG. 4 shows a block diagram of a hearing device comprising a machine learning based feedback control system according to the present disclosure. This system shown in FIG. 4 is in principle capable of providing all the possible feedback control related opportunities as shown in the state-of-the-art system in FIG. 2, if we train the system in FIG. 4 using the real data captured from the system in FIG. 1. However, by doing that, the machine learning based system would also “learn” the disadvantages of the current state-of-the-art system.

[0139] As an alternative, it is proposed to train the machine learning based feedback control system with synthetic data v(n), x(n), y(n), e(n), p(n), and u(n), that would only be presented if there were a perfect feedback control system, i.e., there is no need for decorrelation, the feedback system would react to feedback path changes instantly without the need of a convergence period known from the adaptive filters.

[0140] Furthermore, it is proposed to provide more information from the hearing aid processing unit to the machine learning based feedback control system, to gain better performance, as illustrated in FIG. 5. This may e.g. be information about the noise reduction (NR), the compression including its gain, knee point, compression ratio, etc. On the other hand, it is also possible that the machine learning based feedback control system provides acoustic related information to the hearing aid processing, such as loop gain, current sound environment etc. which can all be trained together with the feedback control system.

[0141] FIG. 5 shows a block diagram of a hearing device comprising a machine learning based feedback control system according to the present disclosure, wherein information from the hearing device processing unit is used as inputs to the machine learning based feedback control system, and acoustic information from the learning model are provided to the hearing aid processing unit.

[0142] A simpler model is shown in FIG. 6, in which the machine learning should only provide a feedback-free signal e(n).

[0143] FIG. 6 shows a block diagram of a hearing device comprising a machine learning based feedback control system according to the present disclosure, wherein a simpler model comprising that the output signal u(n) is the input to the model is used.

[0144] In this model, although it is possible (indirectly by modifying e(n)), it is not the intention that the model can modify the output signal u(n), as was the case in the previous model.

[0145] More details on how to train the machine learning model (as shown in FIG. 5.) are presented below.

[0146] We consider some standard algorithms for the machine learning training, such as the supervised learning method, one specific way of training such a model is backpropagation.

[0147] In this case, we would provide training signals generated from realistic computer simulations. In simulations, we have access to all signals, including the feedback signal v(n) and the incoming signal x(n), which are not observable in practice. We can then create the microphone signals y(n)=x(n)+v(n), and the desired processing signal p(n) depends on the chosen (and known) hearing aid processing, both y(n) and p(n) would be available to use (for us or our machine learning model) during the normal hearing aid operation.

[0148] Furthermore, we can generate the desired feedback compensated signal e(n) (ideally e(n)=x(n)), and the desired output signal u(n) (ideally u(n)=p(n)), both e(n) and u(n) are used as the reference signals (labeled data) for the training.

[0149] We would need to create many sets of the signals v(n), x(n), y(n) and p(n), e(n) and u(n) to train the network, under different conditions (signal type of x(n), dynamics of v(n), the processing of hearing aid to mention the most important ones).

[0150] We expect that the input signals to the machine learning network are the time-frequency units Y(k,l) and P(k,l) after the transformation (e.g., STFT) of the time domain signals y(n), p(n), where l and k are the time-frequency domain time and frequency indices.

[0151] Furthermore, we would have the time-domain signals e(n) and u(n), or their time-frequency transformed signals E(k,l) and U(k,l) as labeled data for our training.

[0152] In one setup, we would arrange Y(k,l′), P(k,l′) as a K-element (column) vector Y.sub.j′ and P.sub.j′ for each time index m, where k=0, . . . , K−1. This is illustrated in FIG. 7A, 7B (with different output vectors).

[0153] In another setup, we would concatenate more K-element (column) vectors of Y.sub.j′, P.sub.j′ over several time indices l′ into matrices Y.sub.j′=[Y.sub.l′Y.sub.l′-1 . . . ] and P.sub.l′=[P.sub.l′P.sub.l′-1 . . . ]. This is illustrated in FIGS. 7C and 7D.

[0154] FIG. 7A schematically illustrates a first example of input and output vectors for a machine learning model (MLM (FBC)) according to the present disclosure. FIG. 7 is an example of input and output vectors that may be used in the embodiment of a hearing aid shown in FIG. 6. The input vector comprises concatenated column vectors of a single frame of the electric input signal (y(n)) (from microphone M) and of the processed signal (p(n)) (from audio signal processor (Processing)). Both input signals are converted to a time-frequency representation (Y(k,l), P(k,l), e.g. using respective analysis filter banks, e.g. applying Fourier transform algorithms (e.g. STFT) to the respective time domain signals (y(n), p(n)). Concatenated column vectors (Y(k,l), P(k,l) for a given time index l′ are used as input vector to the machine learning model (MLM (FBC)), which provides a time frame E(k,l′) of the feedback corrected signal (e(n)).

[0155] FIG. 7B schematically illustrates a second example of input and output vectors for a machine learning model according to the present disclosure. FIG. 7B is similar to FIG. 7A, but the output vector for the machine learning model additionally comprises a frame (U(k,l′) representing the output signal (u(n)), which is fed to an output transducer (SPK) of the hearing device (see e.g. embodiments of a hearing devices of FIGS. 4 and 5).

[0156] FIG. 7C schematically illustrates an example of historic context of an input vector for a machine learning model according to the present disclosure. FIG. 7C illustrates a part of a time-frequency ‘map’ for a given signal X represented by magnitudes (|X(k,l)|) of the signal X in each time frequency unit (k,l). The hearing device may comprise a context unit for providing an appropriate input vector Z(k,l′) to the machine learning model (MLM (FBC)) to be trained, l′ corresponding to a specific point in time (denoted ‘now’ in FIG. 7C). The context is illustrated in in FIG. 7C by hatched part of time-frequency map denoted ‘Context’. These L time frames are included in the input vector for a given input signal (denoted X in FIG. 7C) to the model (input vector denoted Z(k,l′)). The number of frames L may e.g. be fixed in advance of the training procedure, e.g. related to the timing of feedback howl build-up.

[0157] The (synthetic) training data preferably comprises a larger number of data sets leading to feedback howl (for a given hearing device, e.g. a specific hearing aid style), wherein the input and output and intermediate signals are known as described above.

[0158] FIG. 7D schematically illustrates an example of historic content of an input vector formed as concatenated individual vectors comprising data for a number of different input signals (X1, . . . , XN, N being the number of input signals) for a machine learning model (MLM (FBC)) according to the present disclosure and a corresponding output vector comprising concatenated individual vectors comprising data for a number of different output signals (O1, . . . , OP, P being the number of output signals from the model).

[0159] Similarly, we can arrange the information from the hearing aid processing (dotted line in FIG. 5) as vectors (with elements containing information over frequencies). Some examples of relevant and useful information can be the amount of noise reduction N(k,l) applied and the information about the applied gain G(k,l′), input signal level L(k,l′), etc., over different frequencies k at a given time l′. These values can also be concatenate to vectors and/or matrices.

[0160] An output (dotted line in FIG. 5) from the machine leaning model can be acoustic information, such as loop gain over frequencies, current sound environments etc. These can be trained as part of the supervised learning training.

[0161] Different types of networks may be used to train the machine learning model, such as a dense neural network, convolutional neural network, and recurrent neural network, e.g. a gated recurrent unit (GRU), or combinations thereof.

[0162] Another way of training the network may be to use the reinforcement learning method.

[0163] A method of training for machine learning model (e.g. implemented by a neural network) for use in a feedback control system of a hearing device, e.g. hearing aid, is proposed. This is illustrated in FIG. 8A. The training is performed by synthetic signals provided by a computer simulation of the hearing device, e.g. a hearing aid.

[0164] The synthetic input data may represent [0165] an external part of an electric input signal provided by an input transducer; [0166] a feedback part of the electric input signal propagated from an output transducer to the input transducer; and [0167] a processed signal provided by processing of a feedback corrected version of the electric input signal; and

[0168] The synthetic output data may represent [0169] the feedback corrected version of the at least one electric input signal.

[0170] The synthetic output data may further represent an output signal provided to the output transducer for being presented to a user.

[0171] The synthetic input data may further represent information about one or more processing algorithms applied to feedback corrected version of the at least one electric input signal.

[0172] The synthetic output data may further represent parameters providing inputs to the one or more processing algorithms.

[0173] The training procedure may involve the use of one or more “loss functions” (or “cost functions”), i.e. functions to be optimized (e g minimized or maximized) during the training of the network. Many such functions could be envisioned, including: [0174] Mean-Squared Error (MSE) between complex STFTs of network compensated signal and ideal signal. [0175] MSEs of transformed STFTs, e.g., log-magnitude-STFTs. [0176] More perceptually oriented loss functions (e.g. speech intelligibility measures, e.g. Short-Time Objective Intelligibility (STOI), Speech Intelligibility Index (SII), Hearing Aid Speech Perception Index (HASPI), etc.).

[0177] FIG. 8B illustrates another embodiment of a method of training a machine learning model for use in a feedback control system of a hearing aid.

[0178] The hearing aid comprises [0179] at least one input transducer for converting input sound in an environment around the user to at least one electric input signal representing said input sound; [0180] an output transducer for converting an output signal provided in dependence of said at least one electric input signal to stimuli perceivable to the user as sound;
wherein said input sound comprises an external sound and a feedback sound generated by said output transducer and leaked to said input transducer via feedback path, and wherein said at least one electric input signal likewise comprises an external part originating from said external sound and a feedback part originating from said feedback sound; [0181] a feedback control system for minimizing said feedback part of said at least one electric input signal and at least providing a feedback corrected version of said at least one electric input signal; and [0182] an audio signal processor configured to apply one or more processing algorithms to said feedback corrected version of said at least one electric input signal and to provide a processed signal in dependence thereof.

[0183] The method comprises that the machine learning model is trained with synthetic input data at least representing [0184] said external part of said at least one electric input signal; [0185] said feedback part of said at least one electric input signal; and [0186] said processed signal; and
and with synthetic output data at least representing [0187] said feedback corrected version of the at least one electric input signal.

[0188] The synthetic output data may further represent an output signal provided to the output transducer for being presented to a user.

[0189] The synthetic input data may further represent information about one or more processing algorithms applied to feedback corrected version of the at least one electric input signal.

[0190] The synthetic output data may further represent parameters providing inputs to the one or more processing algorithms.

[0191] 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.

[0192] Embodiments of the disclosure may e.g. be useful in electronic appliances, where acoustic feedback can be expected.

[0193] 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.

[0194] 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.

[0195] 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

[0196] EP3139636A1 (Oticon, Bernafon) 8 Mar. 2017 [0197] EP3291581A2 (Oticon) 7 Mar. 2018 [0198] EP3236675A1 (Starkey) 25 Oct. 2017