U.S. patent application number 15/133896 was filed with the patent office on 2017-10-26 for neural network-driven feedback cancellation.
The applicant listed for this patent is Starkey Laboratories, Inc.. Invention is credited to Kelly Fitz, Carlos Renato Calcada Nakagawa, Tao Zhang.
Application Number | 20170311095 15/133896 |
Document ID | / |
Family ID | 58579097 |
Filed Date | 2017-10-26 |
United States Patent
Application |
20170311095 |
Kind Code |
A1 |
Fitz; Kelly ; et
al. |
October 26, 2017 |
NEURAL NETWORK-DRIVEN FEEDBACK CANCELLATION
Abstract
Disclosed herein, among other things, are apparatus and methods
for neural network-driven feedback cancellation for hearing
assistance devices. Various embodiments include a method of signal
processing an input signal in a hearing assistance device to
mitigate entrainment, the hearing assistance device including a
receiver and a microphone. The method includes performing neural
network processing to identify acoustic features in a plurality of
audio signals and predict target outputs for the plurality of audio
signals, and using the trained neural network to control acoustic
feedback cancellation of the input signal.
Inventors: |
Fitz; Kelly; (Eden Prairie,
MN) ; Nakagawa; Carlos Renato Calcada; (Eden Prairie,
MN) ; Zhang; Tao; (Eden Prairie, MN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Starkey Laboratories, Inc. |
Eden Prairie |
MN |
US |
|
|
Family ID: |
58579097 |
Appl. No.: |
15/133896 |
Filed: |
April 20, 2016 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04R 25/507 20130101;
H04R 25/558 20130101; H04R 3/005 20130101; H04R 25/453 20130101;
H04R 2225/023 20130101 |
International
Class: |
H04R 25/00 20060101
H04R025/00; H04R 3/00 20060101 H04R003/00; H04R 25/00 20060101
H04R025/00; H04R 25/00 20060101 H04R025/00 |
Claims
1. A method of signal processing an input signal in a hearing
device including a receiver and a microphone, the method
comprising: training a neural network to identify acoustic features
in a plurality of audio signals and predict target outputs for the
plurality of audio signals; and using the trained network to
control acoustic feedback cancellation on the input signal,
including using the neural network to pre-process signals for
adaptation of a feedback canceller to mitigate entrainment.
2. The method of claim 1, wherein training the neural network to
identify acoustic features in a plurality of audio signals and
predict target outputs for the plurality of audio signals includes
performing training offline from data collected during normal use
of the hearing device.
3. The method of claim 1, wherein the training is performed on an
external device.
4. The method of claim 3, wherein the training is performed based
on data collected from wearers stored on a server connected to the
hearing device by a communication network.
5. The method of claim 4, wherein neural network processing runs on
the server and updates parameters of feedback cancellation on the
hearing device.
6. The method of claim 3, wherein the training is performed on a
mobile device.
7. The method of claim 6, wherein neural network processing runs on
the mobile device and updates parameters of feedback cancellation
on the hearing device.
8. The method of claim 1, wherein using the trained network to
control acoustic feedback cancellation on the input signal includes
using the trained network to control subband acoustic feedback
cancellation of the input signal.
9. The method of claim 1, comprising training the network to
manipulate parameters of adaptive feedback cancellation.
10. The method of claim 9, comprising training the network to
select optimal values for parameters that control a rate at which a
feedback canceller adapts.
11. The method of claim 9, comprising training the network to
control depth or rate of phase modulation.
12. The method of claim 9, comprising training the network to
control an adaptation gradient for adaptive feedback cancellation
of the input signal.
13. The method of claim 1, comprising training the network to
predict or control adaptive feedback cancellation filter
coefficients.
14. The method of claim 1, comprising training the network to
produce an estimated feedback signal.
15. The method of claim 1, comprising training the network to
produce an estimated feedback-free input signal.
16. The method of claim 1, wherein using the trained network to
control acoustic feedback cancellation of the input signal includes
using the trained network to control acoustic feedback cancellation
of the input signal during conditions in which the input signal is
a tonal or pitched signal.
17. A hearing device, comprising: a microphone configured to
receive audio signals; and a processor configured to process the
audio signals to correct for a hearing impairment of a wearer, the
processor further configured to: train a neural network processing
to identify acoustic features in a plurality of audio signals and
predict target outputs for the plurality of audio signals; and
control acoustic feedback cancellation on the input signal using
the results of neural network processing, including using the
neural network to pre-process signals for adaptation of a feedback
canceller to mitigate entrainment.
18. The hearing device of claim 17, wherein the hearing device is a
completely-in-the-canal (CIC) hearing aid.
19. The hearing device of claim 17, wherein the hearing device is a
receiver-in-canal (RFC) hearing aid.
20. The hearing device of claim 18, further comprising multiple
microphones configured to receive audio signals.
Description
TECHNICAL FIELD
[0001] This document relates generally to hearing systems and more
particularly to neural network-driven feedback cancellation for
hearing devices.
BACKGROUND
[0002] Hearing devices provide sound for the wearer. Some examples
of hearing devices are headsets, hearing aids, speakers, cochlear
implants, bone conduction devices, and personal listening devices.
Hearing aids provide amplification to compensate for hearing loss
by transmitting amplified sounds to their ear canals. In various
examples, a hearing aid is worn in and/or around a patient's
ear.
[0003] Adaptive feedback cancellation is used in many modern
hearing aids. Adaptive feedback cancellation algorithms may suffer
in the presence of strongly self-correlated input signals, such as
pitched speech and music. The performance degradation results in
lower added stable gain, and audible artifacts, referred to as
entrainment. Signal processing systems that reduce entrainment by
processing the output of the hearing aid can restore added stable
gain, but introduce additional audible sound quality artifacts.
These artifacts may occur during voiced speech, but are most
egregious for music signals, in which persistent tones aggravate
the entraining behavior and magnify the sound quality
artifacts.
[0004] There is a need in the art for improved feedback
cancellation to mitigate unwanted adaptive feedback cancellation
artifacts, such as those from entrainment, in hearing devices.
SUMMARY
[0005] Disclosed herein, among other things, are apparatus and
methods for neural network-driven feedback cancellation for hearing
devices. Various embodiments include a method of processing an
input signal in a hearing device to mitigate entrainment, the
hearing device including a receiver and a microphone. The method
includes performing neural network training to identify acoustic
features in a plurality of audio signals and predict target outputs
for the plurality of audio signals, and using the trained network
in a processor to control acoustic feedback cancellation of the
input signal.
[0006] Various aspects of the present subject matter include a
hearing device having a microphone configured to receive audio
signals, and a processor configured to process the audio signals to
correct for a hearing impairment of a wearer. The processor is
further configured to train a neural network to identify acoustic
features in a plurality of audio signals and predict target outputs
for the plurality of audio signals, and to control acoustic
feedback cancellation of the input signal using the results of the
neural network processing. In various embodiments, the network is
pre-trained offline and loaded onto the hearing device processor,
where it is used to control feedback cancellation and/or phase
modulation.
[0007] This summary is an overview of some of the teachings of the
present application and not intended to be an exclusive or
exhaustive treatment of the present subject matter. Further details
about the present subject matter are found in the detailed
description and appended claims. The scope of the present invention
is defined by the appended claims and their legal equivalents.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] Various embodiments are illustrated by way of example in the
figures of the accompanying drawings. Such embodiments are
demonstrative and not intended to be exhaustive or exclusive
embodiments of the present subject matter.
[0009] FIG. 1 is a diagram demonstrating, for example, an acoustic
feedback path for one application of the present system relating to
an in-the-ear hearing aid application, according to one application
of the present system.
[0010] FIG. 2 illustrates an acoustic system with an adaptive
feedback cancellation filter according to one embodiment of the
present subject matter.
DETAILED DESCRIPTION
[0011] The following detailed description of the present subject
matter refers to subject matter in the accompanying drawings which
show, by way of illustration, specific aspects and embodiments in
which the present subject matter may be practiced. These
embodiments are described in sufficient detail to enable those
skilled in the art to practice the present subject matter.
References to "an", "one", or "various" embodiments in this
disclosure are not necessarily to the same embodiment, and such
references contemplate more than one embodiment. The following
detailed description is demonstrative and not to be taken in a
limiting sense. The scope of the present subject matter is defined
by the appended claims, along with the full scope of legal
equivalents to which such claims are entitled.
[0012] The present system may be employed in a variety of hardware
devices, including hearing devices. The present detailed
description describes hearing devices using hearing aids as an
example. However, it is understood by those of skill in the art
upon reading and understanding the present subject matter that
hearing aids are only one type of hearing device. Other hearing
devices include, but are not limited to, those described in this
document.
[0013] Digital hearing aids with an adaptive feedback canceller
usually suffer from artifacts when the input audio signal to the
microphone is strongly self-correlated. The feedback canceller may
use an adaptive technique that exploits the correlation between the
feedback signal at the microphone and the receiver signal, to
update a feedback canceller filter to model the external acoustic
feedback. A self-correlated input signal results in an additional
correlation between the receiver and the microphone signals. The
adaptive feedback canceller cannot differentiate this undesired
correlation from correlation due to the external acoustic feedback
and borrows characteristics of the self-correlated input signal in
trying to trace this undesired correlation. This results in
artifacts, called entrainment artifacts, due to non-optimal
feedback cancellation. The entrainment-causing self-correlated
input signal and the affected feedback canceller filter are called
the entraining signal and the entrained filter, respectively.
[0014] Entrainment artifacts in audio systems include whistle-like
sounds that contain harmonics of the self-correlated input audio
signal and can be very bothersome, and occur with day-to-day sounds
such as telephone rings, dial tones, microwave beeps, and
instrumental music to name a few. These artifacts, in addition to
being annoying, can result in reduced output signal quality. Most
previous solutions attempt to address the problem of entrainment
and poor adaptive behavior in the presence of tonal and
self-correlated signals by distorting the signals, such that they
no longer have the properties that trigger these problems. The
consequence of such an approach is that the hearing aid output is
distorted or corrupted in some way. Thus, there is a need in the
art for method and apparatus to reduce the occurrence of these
artifacts and provide improved quality and performance.
[0015] Disclosed herein, among other things, are apparatus and
methods for neural network-driven feedback cancellation for hearing
devices. Various embodiments include a method of processing an
input signal in a hearing device to mitigate entrainment, the
hearing device including a receiver and a microphone. The method
includes training a neural network to identify acoustic features in
a plurality of audio signals and predict target outputs for the
plurality of audio signals, and using the trained neural network on
a processor to control acoustic feedback cancellation of the input
signal. The present subject matter mitigates entrainment in
adaptive feedback cancellation without altering hearing device
output, thereby improving sound quality for tonal inputs such as
speech and music.
[0016] In various embodiments, the present subject matter
manipulates parameters of the feedback cancellation algorithm
according to properties of the signals, to render the feedback
canceller less sensitive to entrainment and improper adaptation.
Thus, the present subject matter provides a much more powerful
mechanism for identifying relevant signal properties and
appropriate parameter manipulations, by leveraging machine learning
algorithms rather than relying on heuristics, to infer the optimal
relationship between signal properties and parameter adjustments
rather than prescribing the known or putative relationship.
[0017] A trained neural network is provided in the hearing device
to govern the adaptive behavior of the adaptive feedback canceller.
Neural networks are used to learn automatically the relationship
between data available in the online operation of the hearing
device and optimal configuration of runtime state and/or parameters
of the adaptive feedback canceller, to improve the ability of the
system to accurately model the true feedback path under adverse
conditions. Adverse conditions for an adaptive feedback canceller
include conditions in which the feedback in the system is weak
relative to the input signal, and conditions in which the input
signal, and therefore output signal, is strongly self-correlated.
Self-correlated signals are self-similar over a short time span,
that is, delayed and attenuated versions of the signal are similar
to each other. If the signal is similar to a delayed and attenuated
version of itself, then at the hearing device input the feedback
canceller cannot distinguish new signal from feedback. The simplest
case of this self-similarity is a tonal, or pitched signal. A
periodic signal is identical to versions of itself delayed by
multiples of the pitch period, and thus tonal signals, like music,
which are approximately periodic, are troublesome for adaptive
feedback cancellers.
[0018] Feedback cancellation performance degradation manifests
itself in the form of reduced accuracy in modeling the feedback
path, or misalignment, which results in lower added stable gain and
degraded sound quality. In the extreme case of signal
self-correlation, the system begins to cancel the signal itself
rather than the feedback signal, introducing audible artifacts and
distortion referred to as entrainment. Entrainment artifacts may
occur during voiced speech, but are most egregious for music
signals, in which persistent tones aggravate the entraining
behavior and magnify the artifacts. Output-processing systems break
down the problematic correlation, restoring the modeling accuracy
and reducing misalignment, at the expense of degrading the sound
quality of the output and introducing artifacts of their own. Like
entrainment itself, these artifacts are most egregious for music
signals and some voiced speech.
[0019] In the present subject matter, neural network-based signal
processing is used to immunize the adaptive feedback canceller
against the effects of self-correlated inputs without degrading the
hearing device output, by modifying the adaptive behavior of the
system, rather than modifying the signal sent to the hearing device
receiver. In various embodiments, neural network-based processing
generalizes and infers the optimal relationship from a large number
of examples, referred to as a training set. Elements of the
training set comprise an example of network input and the desired
target network output. During the training process, which can be
performed offline, the network configuration is adapted gradually
to optimize its ability to correctly predict the target output for
each input in the training set. Given the training set, the network
learns to extract the salient acoustic features in noisy speech
signals, those that best predict the desired output from noisy
input, and to optimally and efficiently combine those features to
produce the desired output from the input. During a training phase,
example system inputs are provided to the algorithm along with
corresponding desired outputs, and over many such input-output
pairs, the learning algorithms adapt their internal states to
improve their ability to predict the output that should be produced
for a given input. For a well-chosen training set, the algorithm
will be able to learn to generalize and predict outputs for inputs
that are not part of the training set. This contrasts with
traditional signal processing methods, in which an algorithm
designer knows and specifies, a priori, the relationship between
input features and desired outputs. Most of the computational
burden in machine learning algorithms (of which neural networks are
an example) is loaded on the training phase. The process of
adapting the internal state of a neural network from individual
training examples is not costly, but for effective learning, very
large training sets are required. In various embodiments, learning
takes place during an offline training phase, which is done in
product development or research, but not in the field. Neural
network training can be performed online, in other embodiments.
[0020] A number of different neural network inputs can be used, in
various embodiments. In one approach, the network is provided with
the lowest-level features such as time-domain samples or complex
spectra, allowing the network to learn from the greatest possible
breadth of information. An alternative approach is to provide
higher-level, or more abstract features as input, guiding the
network towards interpretations of the data that are known to be
useful. In various embodiments, a combination of high-and low-level
features may be used. In the application to subband adaptive
feedback cancellation in hearing devices, the primary low-level
features available are the complex subband coefficients at the
hearing device input, at the hearing device output (including the
output delayed by the bulk delay), at the output of the FBC
adaptive filter (the estimated feedback signal), and the
feedback-cancelled error signal coefficients (equal to the
difference between the hearing device input and the adaptive filter
output). Higher-level features of interest derived from these
include the subband signal log-powers (log squared magnitudes),
auto-correlation coefficients, periodicity strength, etc. Any
combination of high-and/or low-level acoustic features for use as
neural network inputs is within the scope of this disclosure.
[0021] A number of different neural network outputs can be used, in
various embodiments, and span a similar range from high to low
level. At the highest level of abstraction, the neural network can
be trained to select optimal values for the parameters that control
the rate at which the feedback cancellation adapts, such as
adaptation step size or other parameters governing the behavior of
the adaptive algorithm, and closely associated with this, the
amount of signal distortion introduced by the feedback cancellation
or entrainment mitigation algorithms (such as phase modulation). An
example of a mitigation algorithm includes output phase modulation
(OPM), such as described in the following commonly assigned U.S.
Patent Applications which are herein incorporated by reference in
their entirety: "Output Phase Modulation Entrainment Containment
for Digital Filters," Ser. No. 11/276,763, filed on Mar. 13, 2006,
now issued as U.S. Pat. No. 8,116,473; and "Output Phase Modulation
Entrainment Containment for Digital Filters," Ser. No. 12/336,460,
filed on Dec. 16, 2008, now issued as U.S. Pat. No. 8,553,899. In
various embodiments, an acoustic feature is used to recognize
acoustic situations dominated by tonal signals like music, and
configures the feedback cancellation accordingly by reducing
adaptation rate. Alternatively, the neural network can be
responsive to the state of the feedback cancelation system itself,
for example modulating adaptation rates according to an estimate of
the misalignment, which is the difference between estimated and
true feedback paths. The misalignment can be explicitly estimated,
such as with a high-level input feature, or implicitly estimated by
the network.
[0022] At a lower level of abstraction, the neural network can be
trained to manipulate the internal state of the adaptive system,
becoming an integral component of the adaptation algorithm. For
example, the adaptation gradient (or gradient direction) can be
improved by predicting the gradient (or gradient angle) error using
the neural network. Adaptive feedback cancellation iteratively
estimates the error in its approximation of the true feedback path,
and adapts the filter coefficients in a direction that reduces the
error most. The present subject matter can use the neural network
to learn to better estimate that error-reducing adaptation
direction, in various embodiments. At an even lower level of
abstraction, a neural network can be trained to predict the
adaptive feedback cancellation filter coefficients directly,
replacing the current adaptive algorithm altogether. In further
embodiments, the neural network produces the estimated feedback
signal directly, or feedback-free input signal, replacing both
adaptation and filtering.
[0023] In various embodiments, other supervised machine learning
algorithms can be employed in place of neural networks. The neural
network can also be implemented on a device other than the hearing
aid, for example, on a smart phone. In one example, applications
that govern adaptation speed or step size, which change more
slowly, can be implemented externally to the hearing device. In
certain embodiments, the neural network training, or some part of
it, can be performed online. For example, based on data collected
from the hearing aid wearer's experience, the neural network can be
retrained (or refined through additional training) on a smart
phone, which can then download the updated network weights and/or
configuration to the hearing aid. Based on data collected from a
group of hearing aid wearers' experiences, such as collected on a
server in the cloud, the neural network can be retrained in the
cloud, connected through the smart phone, which can then download
the updated network weights and/or configuration to the hearing aid
in further embodiments. In one embodiment, neural networks can be
employed to pre-process the signals that drive the adaptation in
the feedback canceller, to improve that algorithm's performance or
make it less sensitive to entrainment.
[0024] FIG. 1 is a diagram demonstrating, for example, an acoustic
feedback path for one application of the present system relating to
an in-the-ear hearing aid application, according to one embodiment
of the present system. In this example, a hearing aid 100 includes
a microphone 104 and a receiver 106. The sounds picked up by
microphone 104 are processed and transmitted as audio signals by
receiver 106. The hearing aid has an acoustic feedback path 109
which provides audio from the receiver 106 to the microphone 104.
Various embodiments include multiple microphones, and the
microphones can be used for monaural or binaural embodiments of the
present subject matter.
[0025] FIG. 2 illustrates an acoustic system 200 with an adaptive
feedback cancellation filter 225 according to one embodiment of the
present subject matter. The embodiment of FIG. 2 also includes a
input device 204, such as a microphone, an output device 206, such
as a speaker, processing electronics 208 for processing and
amplifying a compensated input signal e.sub.n 212, and an acoustic
feedback path 209 with acoustic feedback path signal y.sub.n 210.
In various embodiments, the adaptive feedback cancellation filter
225 mirrors the feedback path 209 transfer function and signal
y.sub.n 210 to produce a feedback cancellation signal y.sub.n 211.
When y.sub.n 211 is subtracted from the input signal x.sub.n, 205,
the resulting compensated input signal e.sub.n 212 contains
minimal, if any, feedback signal y.sub.n 210 components. In various
embodiments, the feedback cancellation filter 225 includes an
adaptive filter 202 and an adaptation module 201. The adaptation
module 201 adjusts the coefficients of the adaptive filter 202 to
minimize the error signal e.sub.n 212, that is, the difference
between the input signal x.sub.n 205 and the adaptive filter output
y.sub.n 211. In one embodiment, a processor 203 is used to monitor
the input signal x.sub.n 205, the adaptive filter output y.sub.n
211, and/or the output signal u.sub.n 207 for indication of
entrainment or filter misadjustment, or conditions likely to result
in entrainment or filter misadjustment. In various embodiments, the
processor 203 is further configured to train a neural network to
identify acoustic features in a plurality of audio signals and
predict target outputs for the plurality of audio signals, and to
control acoustic feedback cancellation on the input signal using
the results of the neural network processing. In various
embodiments, the processor 203 is further configured to control
output phase modulation (OPM) 230 using the results of the neural
network processing.
[0026] In various embodiments, the training is performed on the
hearing device processor. In further embodiments, the training is
performed an external device, for example on a server in a cloud or
on a smart phone, where neural network training runs on the server
or smart phone and a signal is sent to the hearing device to update
parameters of feedback cancellation on the hearing assistance
device. In some embodiments, a neural network is trained, or
refined by means of additional or ongoing training, using data
collected from many hearing aid wearers. In some embodiments, the
data is collected from many hearing aid wearers while they are
wearing their hearing aids in the course of normal use and
transmitted to the server in the cloud using a smartphone. Various
embodiments use the trained neural network to control subband
acoustic feedback cancellation on the input signal, such as by
manipulating parameters of adaptive feedback cancellation. In some
embodiments, the neural network is trained to select optimal values
for parameters that control a rate at which a feedback canceller
adapts, to control depth or rate of phase modulation, to control an
adaptation gradient for adaptive feedback cancellation of the input
signal, to control gradient angle for feedback cancellation of the
input signal, to predict adaptive feedback cancellation filter
coefficients, to produce an estimated feedback signal, and/or to
produce an estimated feedback-free input signal.
[0027] Hearing devices typically include at least one enclosure or
housing, a microphone, hearing device electronics including
processing electronics, and a speaker or "receiver." Hearing
devices can include a power source, such as a battery. In various
embodiments, the battery is rechargeable. In various embodiments
multiple energy sources are employed. It is understood that
variations in communications protocols, antenna configurations, and
combinations of components can be employed without departing from
the scope of the present subject matter. Antenna configurations can
vary and can be included within an enclosure for the electronics or
be external to an enclosure for the electronics. Thus, the examples
set forth herein are intended to be demonstrative and not a
limiting or exhaustive depiction of variations.
[0028] It is understood that digital hearing assistance devices
include a processor. In digital hearing assistance devices with a
processor, programmable gains can be employed to adjust the hearing
assistance device output to a wearer's particular hearing
impairment. The processor can be a digital signal processor (DSP),
microprocessor, microcontroller, other digital logic, or
combinations thereof. The processing can be done by a single
processor, or can be distributed over different devices. The
processing of signals referenced in this application can be
performed using the processor or over different devices. Processing
can be done in the digital domain, the analog domain, or
combinations thereof. Processing can be done using subband
processing techniques or other transform-domain techniques.
Processing can be done using frequency domain or time domain
approaches. Some processing can involve both frequency and time
domain aspects. For brevity, in some examples drawings can omit
certain blocks that perform frequency synthesis, frequency
analysis, analog-to-digital conversion, digital-to-analog
conversion, amplification, buffering, and certain types of
filtering and processing. In various embodiments of the present
subject matter the processor is adapted to perform instructions
stored in one or more memories, which can or cannot be explicitly
shown. Various types of memory can be used, including volatile and
nonvolatile forms of memory. In various embodiments, the processor
or other processing devices execute instructions to perform a
number of signal processing tasks. Such embodiments can include
analog components in communication with the processor to perform
signal processing tasks, such as sound reception by a microphone,
or playing of sound using a receiver (i.e., in applications where
such transducers are used). In various embodiments of the present
subject matter, different realizations of the block diagrams,
circuits, and processes set forth herein can be created by one of
skill in the art without departing from the scope of the present
subject matter.
[0029] It is further understood that different hearing devices can
embody the present subject matter without departing from the scope
of the present disclosure. The devices depicted in the figures are
intended to demonstrate the subject matter, but not necessarily in
a limited, exhaustive, or exclusive sense. It is also understood
that the present subject matter can be used with a device designed
for use in the right ear or the left ear or both ears of the
wearer.
[0030] The present subject matter is demonstrated for hearing
devices, such as hearing aids, including but not limited to,
behind-the-ear (BTE), in-the-ear (ITE), in-the-canal (ITC),
receiver-in-canal (RIC), invisible-in-canal (IIC) or
completely-in-the-canal (CIC) type hearing aids. It is understood
that behind-the-ear type hearing devices can include devices that
reside substantially behind the ear or over the ear. Such devices
can include hearing devices with receivers associated with the
electronics portion of the behind-the-ear device, or hearing
devices of the type having receivers in the ear canal of the user,
including but not limited to receiver-in-canal (RIC) or
receiver-in-the-ear (RITE) hearing aid designs. The present subject
matter can also be used in hearing devices generally, such as
cochlear implant type hearing devices and blue-tooth headsets. The
present subject matter can also be used in deep insertion devices
having a transducer, such as a receiver or microphone. The present
subject matter can be used in devices whether such devices are
standard or custom fit and whether they provide an open or an
occlusive design. It is understood that other hearing assistance
devices not expressly stated herein can be used in conjunction with
the present subject matter.
[0031] This application is intended to cover adaptations or
variations of the present subject matter. It is to be understood
that the above description is intended to be illustrative, and not
restrictive. The scope of the present subject matter should be
determined with reference to the appended claims, along with the
full scope of legal equivalents to which such claims are
entitled.
* * * * *