Patent Description:
<CIT> discloses an out-of-head localization filter determination system according to an embodiment includes headphones, a microphone unit, an out-of-head localization device, and a server device.

<NPL>, disclose individualized prediction of the sound pressure at the eardrum for an earpiece with integrated receivers and microphones.

<NPL>, disclose prediction of the sound pressure at the ear drum in occluded human ears.

This application relates generally to ear-level electronic systems and devices, including hearing aids, personal amplification devices, and hearables. For example, an apparatus and method facilitate estimation of eardrum sound pressure based on secondary path measurement. The method involves determining secondary path measurements and associated acoustic transducer-to-eardrum responses obtained from a plurality of test subjects. Both a least squares estimate and a reduced dimensionality estimate are determined that both estimate a relative transfer function between the secondary path measurements and the associated acoustic transducer-to-eardrum responses. An individual secondary path measurement for a user is performed based on a test signal transmitted via a hearing device into an ear canal of the user. An individual cutoff frequency for the individual secondary path measurement is determined. A first acoustic transducer-to-eardrum response below the cutoff frequency is determined using the individual secondary path measurement and the least squares estimate. A second acoustic transducer-to-eardrum response above the cutoff frequency is determined using the individual secondary path measurement and the reduced dimensionality estimate. A sound pressure level at an eardrum of the user eardrum is predicted by using the first acoustic transducer-to-eardrum response and the second acoustic transducer-to-eardrum response.

The system includes an ear-wearable device and optionally an external device. The ear-wearable device includes: a first memory; an inward-facing microphone configured to receive internal sound inside of the ear canal; an acoustic transducer configured to produce amplified sound inside of the ear canal; a first communications device; and a first processor coupled to the first memory, the first communications device, the inward-facing microphone, and the acoustic transducer. The optional external device comprises: a second memory; a second communications device operable to communicate with the first communications device; and a second processor coupled to the second memory and the second communications device. One or both of the first memory and second memory store a least squares estimate and a reduced dimensionality estimate that that both estimate a relative transfer function between secondary path measurements and associated acoustic transducer-to-eardrum responses that were measured from a plurality of test subjects. The first processor, either alone or cooperatively with the second processor, is operable to: perform an individual secondary path measurement for the user based on a test signal transmitted into the ear canal via the acoustic transducer and measured via the inward facing microphone; determine a cutoff frequency for the individual secondary path measurement; determine a first acoustic transducer-to-eardrum response below the cutoff frequency using the individual secondary path measurement and the least squares estimate; and determine a second acoustic transducer-to-eardrum response above the cutoff frequency using the individual secondary path measurement and the reduced dimensionality estimate. The first processor is operable to predict a sound pressure level at an eardrum of the user using the first and second acoustic transducer-to-eardrum responses.

The above summary is not intended to describe each disclosed embodiment or every implementation of the present disclosure. The figures and the detailed description below more particularly exemplify illustrative embodiments.

The discussion below makes reference to the following figures.

Embodiments disclosed herein are directed to an ear-worn or ear-level electronic hearing device. Such a device may include cochlear implants and bone conduction devices, without departing from the scope of this disclosure. The devices depicted in the figures are intended to demonstrate the subject matter, but not in a limited, exhaustive, or exclusive sense. Ear-worn electronic devices (also referred to herein as "hearing aids," "hearing devices," and "ear-wearable devices"), such as hearables (e.g., wearable earphones, ear monitors, and earbuds), hearing aids, hearing instruments, and hearing assistance devices, typically include an enclosure, such as a housing or shell, within which internal components are disposed.

In recent years, hearing devices and hearables having been including both microphones and receivers in the ear canal. Inward-facing microphones and integrated receivers (e.g., loudspeakers) can provide the ability to predict the sound pressure at the eardrum. The integrated microphone and receiver can be used to better understand the acoustic transfer properties within the individual ear when the hearing devices are inserted. In this disclosure, devices, systems and methods are described that address the problem of individually predicting the sound pressure created by the receivers at the eardrum.

In some embodiments described below, sound pressure can be predicted at the eardrum by finding an estimator (e.g., a linear estimator) that maps individually measured secondary path responses to a set of predefined receiver-to-eardrum responses. The estimator can be created via offline training on a set of previously measured secondary path and receiver-to-eardrum response pairs. Experimental results based on real-subject measurement data confirm the effectiveness of this approach, even for the case when the size of database for pre-training is limited.

In <FIG>, a diagram illustrates an example of an ear-wearable device <NUM> according to an example embodiment. The ear-wearable device <NUM> includes an in-ear portion <NUM> that fits into the ear canal <NUM> of a user/wearer. The ear-wearable device <NUM> may also include an external portion <NUM>, e.g., worn over the back of the outer ear <NUM>. The external portion <NUM> is electrically and/or acoustically coupled to the internal portion <NUM>. The in-ear portion <NUM> may include an acoustic transducer <NUM>, although in some embodiments the acoustic transducer may be in the external portion <NUM>, where it is acoustically coupled to the ear canal <NUM>, e.g., via a tube. The acoustic transducer <NUM> may be referred to herein as a "receiver," "loudspeaker," etc., however could include a bone conduction transducer. One or both portions <NUM>, <NUM> may include an external microphone, as indicated by respective microphones <NUM>, <NUM>.

The device <NUM> may also include an internal microphone <NUM> that detects sound inside the ear canal <NUM>. The internal microphone <NUM> may also be referred to as an inward-facing microphone or error microphone. For purposes of the following discussion, path <NUM> represents a secondary path, which is the physical propagation path from receiver <NUM> to the error microphone <NUM> within the ear canal <NUM>. Path <NUM> represents an acoustic coupling path between the receiver <NUM> and the eardrum <NUM> of the user. As discussed in greater detail below, the device <NUM> includes features that allow estimating the response of the path <NUM> using measurements of the secondary path <NUM> made using the receiver <NUM> and inward-facing microphone <NUM>.

Other components of hearing device <NUM> not shown in the figure may include a processor (e.g., a digital signal processor or DSP), memory circuitry, power management and charging circuitry, one or more communication devices (e.g., one or more radios, a near-field magnetic induction (NFMI) device), one or more antennas, buttons and/or switches, , for example. The hearing device <NUM> can incorporate a long-range communication device, such as a Bluetooth® transceiver or other type of radio frequency (RF) transceiver.

While <FIG> show one example of a hearing device, often referred to as a hearing aid (HA), the term hearing device of the present disclosure may refer to a wide variety of ear-level electronic devices that can aid a person with impaired hearing. This includes devices that can produce processed sound for persons with normal hearing. Hearing devices include, but are not limited to, behind-the-ear (BTE), in-the-ear (ITE), in-the-canal (ITC), invisible-in-canal (IIC), receiver-in-canal (RIC), receiver-in-the-ear (RITE) or completely-in-the-canal (CIC) type hearing devices or some combination of the above. Throughout this disclosure, reference is made to a "hearing device" or "ear-wearable device," which is understood to refer to a system comprising a single left ear device, a single right ear device, or a combination of a left ear device and a right ear device.

The sound pressure at the eardrum due to a stimulus signal being played out via the integrated receiver, indicates the acoustic transfer properties within the individual ear when the hearing devices being inserted. It facilitates to derive control strategies to achieve individualized drum pressure equalization as well as potential self-fitting, active feedback, noise, and occlusion control. Conventionally, the sound pressure at the eardrum can be measured directly using probe-tube microphones. However, positioning a probe tube tip in the vicinity of the eardrum is a delicate task, which makes it cumbersome to be conducted in practice. Also, this technique may be subject to significant inter-subject variations due to ear-canal acoustics and re-insertions.

It is expected a large number of hearing devices will integrate both a receiver (or other acoustic transducer) and an additional inward-facing microphone in the ear canal. Apart from being used for active noise cancellation (ANC) and active occlusion cancellation (AOC) features, the inward-facing microphone also enables the possibility to predict the sound pressure at the eardrum using the integrated receiver and inward-facing microphone. Note that hearing device <NUM> may include a silicone-molded bud <NUM> that provides an effective sealing of the ear when the device <NUM> is inserted. Embodiments described herein address the problem of individually predicting the sound pressure created by the receiver at the eardrum when the hearing device <NUM> is inserted and properly fitted into the ear. More specifically, the transfer functions of the sound pressure at the eardrum <NUM> relative to the sound pressure measured by the inward-facing microphone <NUM> will be estimated individually.

In <FIG> and <FIG>, graphs illustrate frequency responses obtained from a plurality of test subjects that can be used in hearing device according to an example embodiment. These graphs show acoustic measurements on ten subjects with the same hearing device. Each curve in <FIG> is a secondary path (SP) response that is paired with one of the eardrum response curves in <FIG>. These figures represent <NUM> pairs of secondary path responses and associated eardrum responses. Each response pair was used to derive a relative transfer function (RTF), the RTF curves being shown in <FIG>. The bold curve in <FIG> represents an average of the <NUM> calculated RTF.

Although probe-tube measurements are widely used to measure eardrum sound pressure, unwanted artifacts are known to appear in these measurements. For example, the measured responses may include quarter-wavelength notches related to standing waves, e.g., due to backward reflections. It can be difficult to enforce the measurements with fixed distance to the eardrum among different subjects, which leads to random presence of spectrum minimas at high frequencies (> <NUM>). An example of this is shown by spectrum minimum <NUM> in <FIG>, which is approximately at <NUM>. Other responses show similar minimas in this region at or above <NUM>.

In one embodiment, the probe-tube measurements can be adjusted to compensate for these random artifacts. For example, as described in "<NPL>), a minimum at the measurement position can be compensated for by a modeled pressure transfer function from the measurement position to the eardrum. The pressure transfer function can use a lossless cylinder model, for example, and can be used to correct the probe-tube measurement data and improve the estimation performance and consistency at higher frequencies.

Embodiments described herein include an estimator for the individual acoustic transducer -to-eardrum (e.g., receiver-to-eardrum) response based on a measurement of the individual secondary path. The individual secondary path measurement is made in the ear of the target user using the user's own personal hearing device. The estimator is based on offline pre-training on a set of previously measured secondary path and receiver-to-eardrum response pairs, such as shown in <FIG>. Three such estimators have been investigated. The first is an average receiver-to-eardrum response, which is intuitive but not mathematically optimal. The second estimator is a least square estimator that may be globally optimized. The third estimator is a reduced dimensionality estimator such as Principal Component Analysis (PCA) based estimator. The second and third estimators are discussed in more detail below.

The least squares optimization is formulated by minimizing the cost function in Expression (<NUM>) below, where DSP is a diagonal matrix containing the discrete Fourier transform (DFT) coefficients of all SP responses and DREAR is stacked vectors containing the DFT coefficient of all receiver-to-eardrum responses. The variable ggls is the gain vector of the RTF and µ is a regularization multiplier to prevent the derived gain vector from being over-amplified, which may be set to a value << <NUM>. The optimal least-square solution is derived as shown in Equation (<NUM>), where I is an identity matrix, (·)H is the Hermitian transpose, and µ is selected as <NUM>, for example. <MAT> <MAT>.

The PCA approach converts frequency response pairs into principal components domain and finds a map (e.g., a linear map) that projects the secondary path gain vectors onto the receiver-to-eardrum gain vectors in a minimum mean square error (MMSE) sense. In <FIG>, a graph shows normalized eigenvalues of the singular value decomposition of both SP and REAR responses during the PCA decay for this example. The curve in <FIG> implies that it is reasonable to reduce the order of components. In <FIG>, a graph shows the estimation error for the gain vector for this example. For this data set, the order number for the PCA analysis was chosen to be <NUM>, which means that a 12x12 linear mapping in the PC domain is used. The PCA-based estimator benefits from numerical robustness and efficiency due to the dimensionality reduction of the PCA.

Note that pressure transform function described above to adjust measured eardrum responses can be used as a pre-processing stage for the PCA-based estimator, e.g., to pre-correct the spectrum notches that are presented in the probe-tube measurement data. This pre-processing can provide a better estimate of targeted eardrum response with a smooth spectrum. This pre-processing can also improve PCA-based estimator accuracy at high frequencies, e.g., above <NUM>.

In <FIG>, a graph showing frequency domain normalized estimation error 10log((P'rear- Prear)<NUM>)- 10log((Prear)<NUM>) for an example selected from this data set. A repetitive leave-one-out cross-validation approach was conducted for the <NUM> pairs of SP and REAR response pairs to obtain this type of data for the entire set. As seen in <FIG>, there is a noticeably improved estimation performance in this example with the PCA based estimator at higher frequency ranges (e.g., up to <NUM> in this example) compared to the least squares estimator. The PCA-based estimator is not as good as the least-square based method at lower frequencies (e.g., below around <NUM>) due to that the transfer functions at low frequency regions are less affected by deterministic changes between two responses.

In <FIG>, a graph shows an example of the application of both the least squares estimator and PCA estimator to an SP response from the data set. This is shown in comparison to the actual measured eardrum response, REAR. By analyzing these results, it was found that a PCA-based estimator is not as good as the least-square based method at low frequency regions due to the transfer functions being less affected by deterministic changes between two responses (SP and REAR). Therefore, in some embodiments a cut-off frequency is defined that separates the two estimation schemes (e.g., PCA-based estimator and least-square based method) for high/low frequency ranges and it varies among different subjects based on the individualized SP measurements.

The cutoff frequency may be dependent on the subject (e.g., the individual user and device) and can be determined based on a fitting of the device, e.g., a self-fitting. In one embodiment, determining the cut-off frequency fcutoff for each of subject may involve selecting the frequency of the first peak of measured SP gain between <NUM> and <NUM> (<NUM>/<NUM> octave band segmentation). An example method of determining the fcutoff using this process is shown in the pseudo-code listing of <FIG>. Generally, the pseudo-code involves stepping through each gain value of the DFT starting at <NUM>. If for a selected frequency fi the gain gi is greater than or equal to the largest of the next two values minus a small offset (max(gi+<NUM>,gi+<NUM>) - <NUM> in this example), then gi is the first peak of the gain curve and the selected frequency fi is set as the cutoff. If the maximum frequency <NUM> is encountered without finding a peak, then <NUM> is set as the cutoff.

It will be understood that other procedures may be used to determine the cutoff frequency. For example, instead of looking at the next two values of the gain curve, more or fewer next values may be considered. In other embodiments, the maximum value in the frequency range (e.g., <NUM> to <NUM> in this example) may be selected instead of the first peak. In some embodiments, the cutoff frequency could be later changed, e.g., based on a startup process in which SP is subsequently re-measured, etc., to account for variations in fit of the device within the ear over time.

A separate training process will performed for each hearing device type/model that will utilize the REAR estimation feature. The number of test subjects can be relatively small, e.g., <NUM>-<NUM>. In <FIG>, a flowchart shows a method for training data according to an example embodiment. Generally, for each test subjects, one or more SP response measurements <NUM> are made with an associated measurement of the eardrum sound pressure response, REAR. Frequency regions of Sj, Rj are extracted <NUM> with respective rectangular frequency domain window Q<NUM>(z) and Q<NUM>(z), examples of which are shown in <FIG>. Note that <FIG> assume that fcutoff is <NUM>, however these curves could change if a different fcutoff is used.

The windowed frequency domain vectors with Q<NUM>(z) are <MAT> and the windowed frequency domain vectors with Q<NUM>(z) are <MAT>. The transition frequency for Q<NUM>(z) is fcutoff and the pass band for Q<NUM>(z) is fcutoff ~<NUM>. A least-square solution ggls (e.g., global least square solution) is derived <NUM> that maps SP <MAT> to receiver-to-eardrum responses <MAT> at low frequency region based on the least squares method in Expressions (<NUM>)-(<NUM>). The ensemble average <MAT> of is calculated <NUM> to get <MAT> respectively.

The first n-principal components are extracted <NUM> from the windowed frequency domain vectors <MAT> by PCA to get Us and Ur respectively. In the above example, n=<NUM> principle components are extracted, although other values may be used. The principal component gain vectors Gr,j are calculated <NUM> according to <MAT> and <MAT>. The ensemble average of gs,j, gr,j are respectively calculated <NUM> to get gs' , <MAT>, and the map a is found <NUM> in the principal component domain according to Equation (<NUM>) below.

In <FIG>, a flowchart shows a method of estimating the individual receiver-to-eardrum response. Blocks <NUM>-<NUM> describe measuring the individual secondary path response, which involves inserting <NUM> the hearing device into the user's ear and playback <NUM> of a stimulus signal (e.g. swept-sine chirp signal) via the integrated receiver. A measured secondary path response SM can be derived <NUM> based on the response data from the inward-facing microphone. As indicated by block <NUM>, the cutoff frequency fcutoff may optionally be determined, e.g., as shown in <FIG>. Otherwise, a predetermined fcutoff may be chosen, e.g., <NUM>.

The frequency regions of SM are extracted <NUM> with respective rectangular frequency domain window Q<NUM>(z) and Q<NUM>(z) in the z-domain. The windowed frequency domain vectors with Q<NUM>(z) are <MAT> and the windowed frequency domain vectors with Q<NUM>(z) are <MAT>. The estimated eardrum response at low frequencies (at or below fcutoff)is derived <NUM> based on least squares solution by <MAT>, where ggls is obtained from previously determined training data.

Blocks <NUM>-<NUM> relate to the PCA-based estimate of the eardrum response at high frequencies (above fcutoff). This involves obtaining <NUM> the complex gain vectors in PC domain for the measured SP: <MAT>, where <MAT> and <MAT> are obtained from the previously determined training data. The estimate of gain vectors in the PC domain for the eardrum response is obtained <NUM> as ĝr = g ' r + aĝs, where g ' r and a are obtained from the previously determined training data. The PCA-based estimate of eardrum response in the frequency domain vector is obtained as R̂PCA = R ' <NUM> + Urĝr, where R ' <NUM> and Ur are obtained from the previously determined training data.

Based on these operations, the final estimate of eardrum response in frequency domain R̂, is obtained <NUM> as R̂ = R̂GLS, when frequency ≤ fcutoff, and R̂ = R̂PCA, when frequency > fcutoff. These estimations can be used during operation of the hearing device, e.g., for example, one or more of insertion gain calculation, active noise cancellation, and occlusion control. The previously determined training data may be accessible by the hearing device for at least the operations in blocks <NUM>-<NUM>, e.g., stored in local memory or stored in an external device that is coupled to the hearing device, e.g., a smartphone. In some embodiments, operations in some or all of blocks <NUM>-<NUM> may be performed by the external device and the results transferred to the hearing device.

Note that the PCA-based estimator is just one example of a reduced dimensionality estimator. A reduced dimensionality estimate may be alternatively determined by a deep encoder estimator (also sometimes referred to as an "autoencoder"), which reduces the dimensionality based on a machine learning structure such as a deep neural network. Replacement of the PCA-based estimator with a deep encoder estimator may change some aspects described above, such as the selection of the cutoff frequency. Generally, the deep encoder estimator data transferred from the training process will be a neural network that can take the windowed frequency domain vector <MAT> as input.

In <FIG>, a flowchart shows a method according to another example embodiment. The method involves determining <NUM> secondary path measurements and associated acoustic transducer -to-eardrum responses obtained from a plurality of test subjects. The method also involves determining <NUM> both a) a least squares estimate and b) a reduced dimensionality estimate that both estimate a relative transfer function between the secondary path measurements and the associated acoustic transducer-to-eardrum responses.

An individual secondary path measurement is performed <NUM> for a user based on a test signal transmitted via a hearing device into an ear canal of the user. An individual cutoff frequency is determined <NUM> for the individual secondary path measurement. The cutoff frequency may be predetermined (e.g., a fixed value based on the training data) or selected based on the individual secondary path measurement.

A first acoustic transducer-to-eardrum response below the cutoff frequency is determined <NUM> using the individual secondary path measurement and the least squares estimate. A second acoustic transducer-to-eardrum response above the cutoff frequency is determined <NUM> using the individual secondary path measurement and the reduced dimensionality estimate. A sound pressure level is predicted at the user's eardrum using the first and second acoustic transducer-to-eardrum responses.

In <FIG>, a block diagram illustrates a system and ear-worn hearing device <NUM> in accordance with any of the embodiments disclosed herein. The hearing device <NUM> includes a housing <NUM> configured to be worn in, on, or about an ear of a wearer. The hearing device <NUM> shown in <FIG> can represent a single hearing device configured for monaural or single-ear operation or one of a pair of hearing devices configured for binaural or dual-ear operation. The hearing device <NUM> shown in <FIG> includes a housing <NUM> within or on which various components are situated or supported. The housing <NUM> can be configured for deployment on a wearer's ear (e.g., a behind-the-ear device housing), within an ear canal of the wearer's ear (e.g., an in-the-ear, in-the-canal, invisible-in-canal, or completely-in-the-canal device housing) or both on and in a wearer's ear (e.g., a receiver-in-canal or receiver-in-the-ear device housing).

The hearing device <NUM> includes a processor <NUM> operatively coupled to a main memory <NUM> and a non-volatile memory <NUM>. The processor <NUM> can be implemented as one or more of a multi-core processor, a digital signal processor (DSP), a microprocessor, a programmable controller, a general-purpose computer, a special-purpose computer, a hardware controller, a software controller, a combined hardware and software device, such as a programmable logic controller, and a programmable logic device (e.g., FPGA, ASIC). The processor <NUM> can include or be operatively coupled to main memory <NUM>, such as RAM (e.g., DRAM, SRAM). The processor <NUM> can include or be operatively coupled to non-volatile (persistent) memory <NUM>, such as ROM, EPROM, EEPROM or flash memory. As will be described in detail hereinbelow, the non-volatile memory <NUM> is configured to store instructions that facilitate using estimators for eardrum sound pressure based on SP measurements.

The hearing device <NUM> includes an audio processing facility operably coupled to, or incorporating, the processor <NUM>. The audio processing facility includes audio signal processing circuitry (e.g., analog front-end, analog-to-digital converter, digital-to-analog converter, DSP, and various analog and digital filters), a microphone arrangement <NUM>, and an acoustic transducer <NUM> (e.g., loudspeaker, receiver, bone conduction transducer). The microphone arrangement <NUM> can include one or more discrete microphones or a microphone array(s) (e.g., configured for microphone array beamforming). Each of the microphones of the microphone arrangement <NUM> can be situated at different locations of the housing <NUM>. It is understood that the term microphone used herein can refer to a single microphone or multiple microphones unless specified otherwise.

At least one of the microphones <NUM> may be configured as a reference microphone producing a reference signal in response to external sound outside an ear canal of a user. Another of the microphones1530 may be configured as an error microphone producing an error signal in response to sound inside of the ear canal. A physical propagation path between the reference microphone and the error microphone defines a primary path of the hearing device <NUM>. The acoustic transducer <NUM> produces amplified sound inside of the ear canal. The amplified sound propagates over a secondary path to combine with direct noise at the ear canal, the summation of which is sensed by the error microphone.

The hearing device <NUM> may also include a user interface with a user control interface <NUM> operatively coupled to the processor <NUM>. The user control interface <NUM> is configured to receive an input from the wearer of the hearing device <NUM>. The input from the wearer can be any type of user input, such as a touch input, a gesture input, or a voice input. The user control interface <NUM> may be configured to receive an input from the wearer of the hearing device <NUM>.

The hearing device <NUM> also includes an eardrum response estimator <NUM> operably coupled to the processor <NUM>. The eardrum response estimator <NUM> can be implemented in software, hardware, or a combination of hardware and software. The eardrum response estimator <NUM> can be a component of, or integral to, the processor <NUM> or another processor coupled to the processor <NUM>. The eardrum response estimator <NUM> is operable to perform an initial setup as shown in blocks <NUM>-<NUM> of <FIG>, and may also be operable to perform calculations in blocks <NUM>-<NUM>. During operation of the hearing device <NUM>, the eardrum response estimator <NUM> can be used to apply the eardrum response estimates over different frequency ranges as described above.

The hearing device <NUM> can include one or more communication devices <NUM>. For example, the one or more communication devices <NUM> can include one or more radios coupled to one or more antenna arrangements that conform to an IEEE <NUM> (e.g., Wi-Fi®) or Bluetooth® (e.g., BLE, Bluetooth® <NUM>. <NUM>, <NUM>, <NUM>, <NUM> or later) specification, for example. In addition, or alternatively, the hearing device <NUM> can include a near-field magnetic induction (NFMI) sensor (e.g., an NFMI transceiver coupled to a magnetic antenna) for effecting short-range communications (e.g., ear-to-ear communications, ear-to-kiosk communications). The communications device <NUM> may also include wired communications, e.g., universal serial bus (USB) and the like.

The communication device <NUM> is operable to allow the hearing device <NUM> to communicate with an external computing device <NUM>, e.g., a smartphone, laptop computer, etc. The external computing device <NUM> includes a communications device <NUM> that is compatible with the communications device <NUM> for point-to-point or network communications. The external computing device <NUM> includes its own processor <NUM> and memory <NUM>, the latter which may encompass both volatile and non-volatile memory. The external computing device <NUM> includes an eardrum response estimator <NUM> that may operate in cooperation with the eardrum response estimator <NUM> of the hearing device <NUM> to perform some or all of the operations described for the eardrum response estimator <NUM>. The estimators <NUM>, <NUM> may adopt a protocol for the exchange of data, initiation of operations (e.g., playing of test signals via the acoustic transducer <NUM>), and communication of status to the user, e.g., via user interface <NUM> of the external computing device <NUM>. Also, some portions of the data used in the estimations (e.g., least squares and reduced dimensionality estimates from secondary path measurements and associated receiver-to-eardrum responses that were measured from a plurality of test subjects) may be stored in one or both of the memories <NUM>, <NUM>, and <NUM> of the devices <NUM>, <NUM> during the estimation process.

The hearing device <NUM> also includes a power source, which can be a conventional battery, a rechargeable battery (e.g., a lithium-ion battery), or a power source comprising a supercapacitor. In the embodiment shown in <FIG>, the hearing device <NUM> includes a rechargeable power source <NUM> which is operably coupled to power management circuitry for supplying power to various components of the hearing device <NUM>. The rechargeable power source <NUM> is coupled to charging circuity <NUM>. The charging circuitry <NUM> is electrically coupled to charging contacts on the housing <NUM> which are configured to electrically couple to corresponding charging contacts of a charging unit when the hearing device <NUM> is placed in the charging unit.

In <FIG>, a block diagram shows an audio signal processing path according to an example embodiment. An external microphone <NUM> receives external audio <NUM> which is converted to an audio signal <NUM>. A hearing assistance (HA) sound processor <NUM> which processes the audio signal <NUM> which is output to an acoustic transducer <NUM>, which produces audio <NUM> within the ear canal. The HA sound processor <NUM> may perform, among other things, digital-to-analog conversion, analog-to-digital conversion, amplification, noise reduction, feedback suppression, voice enhancement, equalization, etc. An inward-facing microphone <NUM> receives acoustic output <NUM> of the acoustic transducer <NUM> via a secondary path <NUM>, which includes physical properties of the acoustic transducer <NUM>, microphone <NUM>, housing structures in the ear, the shape and characteristics of the ear canal, etc..

The inward-facing microphone <NUM> provides an audio signal <NUM> that may be used by the HA processor <NUM>, which includes or is coupled to an eardrum response estimator <NUM>, which may operate locally (on the hearing device) or remotely (on a mobile device with a data link to the hearing device). The eardrum response estimator <NUM> used to provide data <NUM> to the HA sound processor <NUM>, such as a transfer function that can be used to determine an eardrum sound pressure level based on the audio signal <NUM>. Generally, the eardrum response estimator <NUM> utilizes stored data <NUM> that includes a cutoff frequency and data used to make a least squares estimate and a reduced dimensionality estimate as described above. This data <NUM> is specific to an individual user, and may be determined during an initial fitting, and may also be subsequently measured for validation/update, e.g., the estimated eardrum pressure can be periodically updated or updated upon request by the user based on current measurements of the secondary path.

The eardrum response estimator <NUM> may also perform setup routines <NUM> that are used to derive the data <NUM> based on a test signal transmitted through the acoustic transducer <NUM> and training data <NUM>. Note that the training data <NUM> need not be stored on the apparatus long-term, e.g., may be transferred in whole or in part for purposes of deriving the data <NUM>, or the processing may occur on another device, with just the derived individual data <NUM> being transferred to the apparatus.

The data <NUM> provided by the eardrum response estimator <NUM> may be used by one or more functional modules of the HA processor <NUM>. An example of these modules is a pressure equalizer <NUM>, which can be used to determine eardrum pressure equalization for self-fitting of a hearing device. An occlusion control module <NUM> can shape the output audio to help sound to be reproduced more accurately. An insertion gain module <NUM> can be used to more accurately predict the actual gain of input sound <NUM> to output sound <NUM> as the latter is perceived at the eardrum. An active noise cancellation module <NUM> can be used to reduce unwanted sounds (e.g., background noise) so that desired sounds (e.g., speech) can be more easily perceived by the user.

In summary, systems, methods, and apparatuses are described that estimate an individual receiver-to-eardrum response based on a measurement of the individual secondary path. The estimator features a combination of two different estimation schemes at low- and high- band frequencies. The cut-off frequency that separates the two estimations schemes for high/low frequency ranges is selected and it may vary among different subjects based on the individualized secondary path measurements. At low frequencies where the deterministic changes between secondary path and receiver-to-eardrum responses are not manifest, the estimated eardrum response is based on the global least-squares estimator that optimizes across a training dataset. At high frequencies, the estimated eardrum response is based on reduced dimensionality estimator that benefits from numerical robustness and reduced processing resources.

Although reference is made herein to the accompanying set of drawings that form part of this disclosure, one of at least ordinary skill in the art will appreciate that various adaptations and modifications of the embodiments described herein are within, or do not depart from, the scope of this disclosure.

Claim 1:
A method comprising:
determining (<NUM>) secondary path measurements and associated acoustic transducer-to-eardrum responses obtained from a plurality of test subjects;
determining (<NUM>) both a least squares estimate and a reduced dimensionality estimate that both estimate a relative transfer function between the secondary path measurements and the associated acoustic transducer-to-eardrum responses;
performing (<NUM>) an individual secondary path measurement for a user based on a test signal transmitted via a hearing device into an ear canal of the user;
determining (<NUM>) an individual cutoff frequency for the individual secondary path measurement;
determining (<NUM>) a first acoustic transducer-to-eardrum response below the cutoff frequency using the individual secondary path measurement and the least squares estimate;
determining (<NUM>) a second acoustic transducer-to-eardrum response above the cutoff frequency using the individual secondary path measurement and the reduced dimensionality estimate; and
predicting (<NUM>) a sound pressure level at an eardrum of the user eardrum using the first and second acoustic transducer-to-eardrum response