Systems and methods for providing personalized audio replay on a plurality of consumer devices

Systems and methods for processing an audio signal are provided for server-mediated sound personalization on a plurality of consumer devices. A user hearing test is conducted on one of a plurality of audio output devices. Next, the hearing data of the user's hearing test is outputted to a server and stored on the server's database along with a unique user identifier. Next, a set of DSP parameters for a sound personalization algorithm are calculated from the user's hearing data. The DSP parameter set is then outputted to one of a plurality of audio output devices when the user logs in with their unique identifier on an application on the audio output device.

FIELD OF INVENTION

This invention relates generally to the field of digital signal processing (DSP), audio engineering and audiology—more specifically systems and methods for providing server-mediated sound personalization on a plurality of consumer devices based on user hearing test results.

BACKGROUND

Traditional DSP sound personalization methods often rely on administration of an audiogram to parameterize a frequency gain compensation function. Typically, a pure tone threshold (PTT) hearing test is employed to identify frequencies in which a user exhibits raised hearing thresholds and the frequency output is modulated accordingly. These gain parameters are stored locally on the user's device for subsequent audio processing.

However, this approach to augmenting the sound experience for the user is imprecise and inefficient. As hearing test results are stored locally on a single device, the resulting parameter calculations are inaccessible to a central server, as well as other devices. To this extent, separate hearing tests must be conducted on every device—potentially leading to locally incorrect results and inconsistent parameter values stored on different audio output devices. The ability to take hearing tests on multiple devices linked to a core account: 1) encourages users to take tests on whatever device pairing is most convenient at the time, 2) improves accuracy through the consolidation of multiple test results, and 3) enables the tracking of a user's hearing state over time. Additionally, in the instance of aberrant hearing test results, the user can be informed if he or she is using an improperly calibrated device and/or if the hearing test was conducted improperly.

The use of frequency compensation is further inadequate to the extent that solely applying a gain function to the audio signal does not sufficiently restore audibility. The gain may enable the user to recapture previously unheard frequencies, but the user may subsequently experience loudness discomfort. Listeners with sensorineural hearing loss typically have similar, or even reduced, discomfort thresholds when compared to normal hearing listeners, despite their hearing thresholds being raised. To this extent, their dynamic aperture is narrower and simply adding gain would be detrimental to their hearing health in the long run.

Although hearing loss typically begins at higher frequencies, listeners who are aware that they have hearing loss do not typically complain about the absence of high frequency sounds. Instead, they report difficulties listening in a noisy environment and in hearing out the details in a complex mixture of sounds, such as in an audio stream of a radio interview conducted in a busy street. In essence, off frequency sounds more readily mask information with energy in other frequencies for hearing-impaired (HI) individuals—music that was once clear and rich in detail becomes muddled. This is because music itself is highly self-masking, i.e. numerous sound sources have energy that overlaps in the frequency space, which can reduce outright detectability, or impede the users' ability to extract information from some of the sources.

As hearing deteriorates, the signal-conditioning capabilities of the ear begin to break down, and thus HI listeners need to expend more mental effort to make sense of sounds of interest in complex acoustic scenes (or miss the information entirely). A raised threshold in an audiogram is not merely a reduction in aural sensitivity, but a result of the malfunction of some deeper processes within the auditory system that have implications beyond the detection of faint sounds. To this extent, the addition of simple frequency gain provides an inadequate solution

Accordingly, it is an aspect of the present disclosure to provide systems and methods for providing personalized audio replay on a plurality of consumer devices through a server-empowered sound personalization account. By providing more accurate and portable parameter sets, a user may be able to enjoy sound personalization, and consequently, a healthier listening experience, across a universe of devices with one simple hearing test.

SUMMARY OF THE INVENTION

According to an aspect of the present disclosure, provided are systems and methods for providing personalized audio replay on a plurality of consumer devices. In some embodiments, the system and method include steps comprising: conducting a user hearing test on one of a plurality of audio output devices, outputting the hearing data from the user's hearing test to a server; storing the hearing data on the server's database with a user unique identifier; calculating a set of parameters from the user's hearing data for a sound personalization algorithm and storing the parameters alongside the unique user identifier on the database; outputting the set of parameters to the sound personalization algorithm on one of a plurality of audio output devices, wherein the parameters are outputted when the user inputs their unique identifier on one of the audio output devices; and processing an audio signal on one of the audio output devices using the sound personalization algorithm.

In another embodiment, the system and method include steps comprising: conducting a user hearing test on one of a plurality of audio output devices; calculating a set of parameters from the user's hearing data for a sound personalization algorithm on the audio output device; outputting the hearing data and calculated parameters to a server; storing the hearing data and calculated parameters on the server's database with a unique identifier; outputting the set of parameters to the sound personalization algorithm on one of a plurality of audio output devices, wherein the parameters are outputted when the user inputs their unique identifier to an application running on one of the audio output devices; and processing an audio signal on one of the audio output devices using the sound personalization algorithm.

In another embodiment, the system and method include steps comprising: conducting a user hearing test on one of a plurality of audio output devices; outputting the hearing data of the user's hearing test to a server; storing the hearing data on the server's database with a unique identifier; outputting the hearing data to one of a plurality of audio output devices, wherein the hearing data is outputted when the user inputs their unique identifier to an application running on one of the audio output devices; calculating a set of parameters from the user's hearing data for a sound personalization algorithm on the audio output device; and outputting the parameters to the sound personalization algorithm on the audio output device.

In some embodiments, the hearing test is one or more of a threshold test, a suprathreshold test, a psychophysical tuning curve test, a masked threshold test, a temporal fine structure test, a speech in noise test and a temporal masking curve test.

In some embodiments, the parameters are recalculated when the user conducts an additional hearing test on anyone of a plurality of audio output devices. The additional hearing test may reflect information from another critical band. The additional hearing test may be a different type of hearing test from previously conducted hearing tests or it may be a more recent version of a previously conducted hearing test. Additionally, the additional hearing test may replace the hearing data from a previously conducted hearing test with aberrant results.

In some embodiments, the set of parameters is calculated on demand on the server when the user inputs their unique identifier on one of the audio output devices.

In some embodiments, the hearing test measures masking threshold curves within a range of frequencies from 250 Hz to 12 kHz.

In some embodiments, the sound personalization algorithm operates on subband signals of the audio signal, i.e. the algorithm works frequency selectively. In a further embodiment, the parameters of the sound personalization algorithm comprise at least one of a gain value provided in each subband and a limiter value provided in each subband. In an optional embodiment, the sound personalization algorithm may be a multiband dynamic processing algorithm. The parameters of the multiband dynamics processor may optionally comprise at least one of a threshold value of a dynamic range compressor provided in each subband, a ratio value of a dynamic range compressor provided in each subband, and a gain value provided in each subband. In an alternate embodiment, the parameters of the multiband dynamics processor may optionally be determined from a hearing aid gain table, which specify the amount of gain for a given input level at each frequency. Typically these tables comprise columns for frequencies and rows for varying intensities of sound inputs (which also may be mapped to threshold, ratio and gain settings overall).

In some embodiments, the parameters are calculated indirectly. For instance, parameters may be calculated using a best fit of the user hearing data with previously inputted entries within the server's database, wherein the parameters associated with the best fitting, previously inputted hearing data are copied and inputted into the user's server database entry. Best fit may be determined, for instance, by measuring average Euclidean distance between the user's hearing data and hearing data in the database. Alternatively, root mean square distance or a similar best fit measurement may be used.

In some embodiments, the parameters may be calculated using the nearest fit of the user hearing data with at least two hearing data entries within the server's database, wherein the parameters associated with the nearest fitting, previously inputted hearing data are interpolated and inputted into the user's server database entry. For instance, parameters may be interpolated linearly between two parameter values. Alternately, parameters may be interpolated non-linearly, such as through a squared function. Alternately, the parameters may be calculated from a fitted mathematical function, such as a polynomial function, derived from plotting existing hearing and parameter set data entries within the server database.

In some embodiments, the parameters may be calculated by converting the user's hearing test results into a ‘hearing age’ value. Based upon predictable declines in hearing function, the user's hearing test results may be matched to the nearest representative hearing age. From this, hearing age parameter sets are then copied into the user's hearing profile.

In some embodiments, the parameters may be calculated directly. For instance, the parameters may be calculated by fitting a user masking contour curve to a target masking contour curve. Alternately, the parameters may be calculated through the optimization of perceptually relevant information (PRI). Alternately, the parameters may be calculated using commonly known prescriptive techniques in the art.

In some embodiments, the consumer electronic device is one of a mobile phone, a tablet, a television, a desktop computer, a laptop, a hearable, a smart speaker, a headphone and a speaker system.

The term “sound personalization algorithm”, as used herein, is defined as any digital signal processing (DSP) algorithm that processes an audio signal to enhance the clarity of the signal to a listener. The DSP algorithm may be, for example: an equalizer, an audio processing function that works on the subband level of an audio signal, a multiband compressive system, or a non-linear audio processing algorithm.

The term “audio output device”, as used herein, is defined as any device that outputs audio, including, but not limited to: mobile phones, computers, televisions, hearing aids, headphones, smart speakers, hearables, and/or speaker systems.

The term “headphone”, as used herein, is any earpiece bearing a transducer that outputs soundwaves into the ear. The earphone may be a wireless hearable, a corded or wireless headphone, a hearable device, or any pair of earbuds.

The term “hearing test”, as used herein, is any test that evaluates a user's hearing health, more specifically a hearing test administered using any transducer that outputs a sound wave. The test may be a threshold test or a suprathreshold test, including, but not limited to, a psychophysical tuning curve (PTC) test, a masked threshold (MT) test, a temporal fine structure test (TFS), temporal masking curve test and a speech in noise test.

The term “server”, as used herein, generally refers to a computer program or device that provides functionalities for other programs or devices.

DETAILED DESCRIPTION

Various example embodiments of the disclosure are discussed in detail below. While specific implementations are discussed, it should be understood that this is done for illustration purposes only. A person skilled in the relevant art will recognize that other components and configurations may be used without departing from the spirit and scope of the present disclosure.

The systems and methods according to aspects of the present disclosure address the problems of accurately and effectively personalizing the audio output of a plurality of audio output devices, and particularly the problem of doing so in a consistent manner. By enabling server-mediated sound personalization based on user hearing test results, aspects of the present disclosure provide for seamless sound personalization between various audio output devices. To this extent, increased user adoption of sound personalization and/or augmentation will not only enable a richer and crisper listening experience for the user, but also lead to healthier user behavior while listening to audio.

As illustrated inFIG. 1, a user may take a hearing test, for instance, on a mobile phone101, laptop computer102or in front of a television109, the results of which may then be outputted to a server103. The resulting DSP parameters may then be calculated and stored on the server103. This DSP parameter calculation may be done on the initial device (e.g.,101,102,103) on which the hearing test is conducted, on the server103, or some combination of the above. The resulting DSP parameters may then be outputted to a plurality of end user devices, which include, but are not limited to, a media player104, mobile phone105, smart speaker106, laptop computer107, and a television set108, etc. The calculated DSP parameters are subsequently used to provide seamless and consistent sound personalization across each of the various end user devices104-108. In some embodiments, a user's hearing test data is outputted to one or more of the end user devices104-108, such that the DSP parameter calculation may be performed on the end user device. In some embodiments, one or more of the end user devices104-108can be employed to perform the user hearing test, i.e. the initial device101-103and the end user device104-108can be provided as a single device.

FIGS. 2A-Bunderscore the importance of sound personalization, illustrating the deterioration of a listener's hearing ability over time. Starting at the age of 20 years old, humans begin to lose their ability to hear higher frequencies—FIG. 2A(albeit above the spectrum of human speech). This loss steadily becomes worse with age, as noticeable declines within the speech frequency spectrum are apparent around the age of 50 or 60. However, these pure tone audiometry findings mask a more complex problem as the human ability to understand speech may decline much earlier. Although hearing loss typically begins at higher frequencies, listeners who are aware that they have hearing loss do not typically complain about the absence of high frequency sounds. Instead, they report difficulties listening in a noisy environment and in hearing out the details in a complex mixture of sounds, such as in a telephone call. In essence, off-frequency sounds more readily mask a frequency of interest for hearing impaired individuals—conversation that was once clear and rich in detail becomes muddled. As hearing deteriorates, the signal-conditioning capabilities of the ear begin to break down, and thus hearing impaired listeners need to expend more mental effort to make sense of sounds of interest in complex acoustic scenes (or miss the information entirely). A raised threshold in an audiogram is not merely a reduction in aural sensitivity, but a result of the malfunction of some deeper processes within the auditory system that have implications beyond the detection of faint sounds.

To this extent,FIG. 2Billustrates key, discernable age trends in suprathreshold hearing tests. The psychophysical tuning curve (PTC) test is a suprathreshold test that measures an individual's ability to discern a probe tone (or pulsed signal tone) against a sweeping masker noise of variable frequency and amplitude. For example, the psychophysical tuning curve test may be measured for signal tones between frequencies of 500 Hz and 4 kHz, and at a probe level of between 20 dB SL and 40 dB SL, in the presence of a masking signal for the signal tone that sweeps from 50% of the signal tone frequency to 150% of the signal tone frequency. Additionally, while the sound level of the probe tone may be between 20 dB SL and 40 dB SL (although various other ranges, such as 5 dB SL-40 dB SL, are also possible), it is noted that the sound level of the sweeping masking signal can exceed, and even significantly exceed, the sound level range associated with the probe tone. Through the collection of large datasets, key age trends as seen on the rightmost vertical axis inFIG. 2Bcan be ascertained, allowing for the accurate parameterization of personalization DSP algorithms. In a multiband compressive system, for example, the threshold and ratio values of each subband signal dynamic range compressor (DRC) can be modified to reduce problematic areas of frequency masking, while post-compression subband signal gain can be further applied in the relevant areas. In the context ofFIGS. 2A-B, masked threshold curves represent a similar paradigm for measuring masked threshold. A narrow band of noise, in this instance around 4 kHz, is fixed while a probe tone sweeps from 50% of the noise band center frequency to 150% of the noise band center frequency. Again, key age trends can be ascertained from the collection of large MT datasets.

FIGS. 3A-Billustrate graphs showing an example method in which a PTC test301or MT test305may be conducted. A psychophysical tuning curve (PTC), consisting of a frequency selectivity contour304, extracted via behavioral testing, provides useful data to determine an individual's masking contours. In one embodiment of the test, a masking band of noise302is gradually swept across frequency, from below the probe frequency303to above the probe frequency303. The user then responds when they can hear the probe and stops responding when they no longer hear the probe. This gives a jagged trace that can then be interpolated to estimate the underlying characteristics of the auditory filter through a masking contour curve plot. Other methodologies known in the prior art may be employed to attain user masking contour curves. For instance, an inverse paradigm may be used in which a probe tone306is swept across frequency while a masking band of noise307is fixed at a center frequency (known as a “masked threshold test” or “MT test”).

FIG. 4illustrates an exemplary embodiment of the present disclosure in which personalized audio replay is carried out on a plurality of audio output devices. First, a hearing test is conducted407on one of a plurality of audio output devices. The hearing test may be provided by any one of a plurality of hearing test options, including but not limited to: a masked threshold test (MT test)401, a pure tone threshold test (PTT test)402, a psychophysical tuning curve test (PTC test)403, a temporal fine structure test (TFS test)404, a speech in noise test405, or other suprathreshold test(s)406.

Next, hearing test results are outputted408to a server along with one or more of a timestamp and a unique user identifier. DSP parameters for a sound personalization algorithm are then calculated and stored409in the server database. The calculated DSP parameters for a given sound personalization algorithm may include, but are not limited to: ratio, threshold and gain values within a multiband dynamic processor, gain and limiter values for equalization DSPs, and/or parameter values common to other sound personalization DSPs (see, e.g., commonly owned U.S. Pat. No. 10,199,047 and U.S. patent application Ser. No. 16/244,727, the contents of which are incorporated by reference in their entirety).

One or more of the DSP parameter calculations may be performed directly or indirectly, as is explained below. When a user inputs410their unique identifier on one of a plurality of audio output devices, the user's DSP parameters are retrieved411from the server database and outputted412to the audio output device's sound personalization DSP. The user's unique identifier may be entered into a standalone application on the user's device—or alternatively or additionally, may be entered into an existing application with a plugin sign-in functionality that mediates server connectivity. After the user's unique identifier has been received and the corresponding DSP parameter's retrieved from the server database, audio signals are then locally processed413at the given audio output device using the parameterized sound personalization DSP.

FIG. 5illustrates an alternative embodiment to the method illustrated inFIG. 4. As depicted inFIG. 5, a hearing test (e.g. selected from the group of hearing tests501-506, which in some embodiments can be the same as the group of hearing tests401-406) is first conducted507on one of a plurality of audio output devices. After conducting the hearing test, the audio output device then calculates508the DSP parameters itself and outputs509these locally calculated DSP parameter values and the user's hearing data to the server database. This output is then stored509at the server database alongside the user's unique identifier.

In contrast to the embodiment ofFIG. 4, where the server received the user's hearing test data and then performed a server-side calculation of the DSP parameters, the embodiment ofFIG. 5performs local calculation of the DSP parameters (i.e. on the same audio output device where the hearing test was performed/measured). However, because the audio output device transmits both the locally calculated DSP parameters and the underlying hearing test data to the server, it is still possible for a server-side calculation of the DSP parameters to be performed. For example, such a server-side calculation might be used to verify or otherwise augment the local calculation, or as a backup measure.

Subsequently, in a similar fashion to the embodiment ofFIG. 4, when a user inputs510their unique identifier in an application on one of a plurality of audio output devices, their DSP parameters are retrieved511from the server database and outputted512to the audio output device's sound personalization DSP. Audio signals are then processed513accordingly.

FIG. 6illustrates a further alternative embodiment to the methods illustrated inFIGS. 4 and 5. Here, a hearing test is conducted on one of a plurality of audio output devices607and subsequently, the hearing test data is stored608on a server database with a unique user identifier. In the embodiment ofFIG. 6, DSP parameters for the hearing test data are not necessarily calculated in response to the hearing test data being received at the server or server database. Notably, DSP parameters can instead be calculated611in an on-demand or just-in-time fashion, either on the end user device, the server, or some combination of the two, wherein the calculation611is based on hearing data that is retrieved610from the server database when a user inputs609their unique identifier on the end user device. The calculated DSP parameters may optionally be outputted to the server for storage alongside the user's hearing data.

Importantly, the previously discussed methods illustrated inFIGS. 4-6commonly feature a central server which mediates the exchange of data necessary for sound personalization across a plurality of audio output devices, independent of when and where the calculation of the DSP parameter values is performed.

FIG. 7illustrates a further example embodiment demonstrating the manner in which a user's calculated DSP parameters may change over time, e.g., in this case when a user takes multiple hearing tests. As depicted inFIG. 7, at t1a user takes an MT test with a 4 kHz noise probe701and DSP parameters are then calculated and stored based on the hearing data from the MT test, for example, according to any of the methods illustrated inFIGS. 4-6. Next, at t2, the user takes an MT test with a 1 kHz noise probe702, representing hearing data within another auditory filter region of the individual. Taken together with data collected at t1, the combination of both data sets may provide a more comprehensive picture of the user's hearing health, thus enabling a more accurate calculation of DSP parameters. To this extent, DSP parameters are updated in view of the t1and t2data. The user may then take further tests, such as a pure tone threshold test703or an MT test with a 2 kHz noise probe704to further refine their hearing profile. Additionally, the user may retake a test705at a later time point t5, which may reveal that an earlier test result was aberrant and/or that the user's hearing ability has degraded since the previous test—both scenarios resulting in a parameter update based on the new results. Altogether, the method illustrated inFIG. 7provides for an evolving and accurate hearing profile that informs updated calculations for sound personalization algorithms.

FIG. 8further illustrates an example embodiment for personalizing audio replay according to aspects of the present disclosure. Hearing test data801is obtained from a user and is utilized to calculate sound personalization DSP parameters803, in this instance, for a multiband dynamics processor with at least parameter values ratio (r), threshold (t) and gain (g) for each subband 1 through x. Here, the hearing test data801is provided as MT and PTT data, although other types of hearing test data can be utilized without departing from the scope of the present disclosure. Within the server, hearing data may be stored as exemplary entry [(u_id), t, MT, PTT]802, wherein u_id is a unique user ID, t is a timestamp, MT is hearing data related to an MT test, and PTT is hearing data related to a PTT test. Additionally, when DSP parameter sets are calculated for a given sound personalization algorithm, they may then be added to the entry as [DSPq-param]804. When a user subsequently inputs their u_id to log in to a given application on their audio output device805, [DSPq-param]806corresponding to the u_id are then outputted to the audio output device807. InFIG. 8, parameter set values803encompass at least ratio and threshold values for a dynamic range compressor as well as gain values per subband signal from 1 to x in a multiband dynamics processor sound personalization algorithm.

In some embodiments, DSP parameter sets may be calculated directly from a user's hearing data or calculated indirectly based on preexisting entries or anchor points in the server database. An anchor point comprises a typical hearing profile constructed based at least in part on demographic information, such as age and sex, in which DSP parameter sets are calculated and stored on the server to serve as reference markers. Indirect calculation of DSP parameter sets bypasses direct parameter sets calculation by finding the closest matching hearing profile(s) and importing (or interpolating) those values for the user.

FIG. 9illustrates three conceptual user masked threshold (MT) curves for users x, y, and z. The MT curves are centered at frequencies a-d, each with curve width d, which may be used to as a metric to measure the similarity between user hearing data. For instance, a root mean square difference calculation may be used to determine if user y's hearing data is more similar to user x's or user z's, e.g. by calculating:
(√{square root over ((d5a−d1a)2+(d6b−d2b)2. . . )}<√{square root over ((d5a−d9a)2+(d6b−d10b)2. . . )}

FIG. 10illustrates three conceptual audiograms of users x, y and z, each with pure tone threshold values 1-5. Similar to above, a root mean square difference measurement may also be used to determine, for example, if user y's hearing data is more similar to user x's than user z's, e.g., by calculating:
(√{square root over ((y1−x1)2+(y2−x2)2. . . )}<√{square root over ((y1−z1)2+(y2−z2)2. . . )})

As would be appreciated by one of ordinary skill in the art, other methods may be used to quantify similarity amongst user hearing profile graphs, where the other methods can include, but are not limited to, methods such as a Euclidean distance measurements, e.g. ((y1−x1)+(y2−x2) . . . >(y1−x1)+(y2−x2)) . . . or other statistical methods known in the art. For indirect DSP parameter set calculation, then, the closest matching hearing profile(s) between a user and other preexisting database entries or anchor points can then be used.

FIG. 11illustrates an exemplary embodiment for calculating sound personalization parameter sets for a given algorithm based on preexisting entries and/or anchor points. Here, server database entries1102are surveyed to find the best fit(s) with user hearing data input1101, represented as MT200and PTT200for (u_id)200. This may be performed by the statistical techniques illustrated inFIGS. 9 and 10. In the example ofFIG. 11, (u_id)200hearing data best matches MT3and PTT3data603. To this extent, (u_id)3associated parameter sets, [DSPq-param 3], are then used for the (u_id)200parameter set entry, illustrated here as [(u_id)200, t200, MT200, PTT200, DSPq-param 3].

FIG. 12illustrates an exemplary embodiment for calculating sound personalization parameter sets for a given algorithm based on preexisting entries or anchor points, according to aspects of the present disclosure. Here, server database entries1202are employed to interpolate1204between two nearest fits1200with user hearing data input1201MT300and PT300for (u_id)300. In this example, the (u_id)300hearing data fits nearest between: MT5≲MT200≳MT3and PTT5≲PTT200≳PTT3703. To this extent, (u_id)3and (u_id)5parameter sets are interpolated to generate a new set of parameters for the (u_id)300parameter set entry, represented here as [(u_id)200, t200, MT200, PTT200, DSPq-param3/5]705. In a further embodiment, interpolation may be performed across multiple data entries to calculate sound personalization parameters, e.g/

DSP parameter sets may be interpolated linearly, e.g., a DRC ratio value of 0.7 for user 5 (u_id)5and 0.8 for user 3 (u_id)3would be interpolated as 0.75 for user 200 (u_id)200in the example ofFIG. 12, assuming user 200's hearing data was halfway in-between that of users 3 and 5. In some embodiments, DSP parameter sets may also be interpolated non-linearly, for instance using a squared function, e.g. a DRC ratio value of 0.6 for user 5 and 0.8 for user 3 would be non-linearly interpolated as 0.75 for user 200 in the example ofFIG. 12.

FIGS. 13, 14 and 15illustrate various exemplary methods of directly calculating parameter sets based on user hearing data according to one or more aspects of the present disclosure. In one embodiment, this may done using a hearing aid gain table prescriptive formulas. In another embodiment, ratio and threshold values for a compressor, as well as gain, in a given multiband dynamic processor signal subband may be calculated by comparing user threshold and suprathreshold information for a listener with that of a normal hearing individual, i.e. reference audiograms and PTC/MT curves. For instance, masking contour curve data, such as PTC or MT, may be used to calculate ratio and threshold parameters for a given frequency subband, while audiogram data may be used to calculate gain within a given frequency subband.

FIGS. 13 and 14demonstrate one way of configuring the ratio and threshold parameters for a frequency band in a multi-band compression system (see, e.g., commonly owned applications EP18200368.1 and U.S. Ser. No. 16/201,839, the contents of which are herein incorporated by reference). Briefly, a user's masking contour curve is received1301, a target masking curve is determined1302, and is subsequently compared with the user masking contour curve1301in order to determine and output user-calculated DSP parameter sets1304.

FIG. 14combines the visualization of a user masking contour curve1406for a listener (listener) and a target masking contour curve1407of a probe tone1450(with the x-axis1401being frequency, and the y-axis1402being the sound level in dB SPL or HL) with an input/output graph of a compressor showing the input level1403versus the output level1404of a sound signal, in decibels relative to full scale (dB FS). The bisecting line in the input/output graph represents a 1:1 (unprocessed) output of the input signal with gain 1.

The parameters of the multi-band compression system in a frequency band are threshold1411and gain1412. These two parameters are determined from the user masking contour curve1406for the listener and target masking contour curve1407. The threshold1411and ratio1412must satisfy the condition that the signal-to-noise ratio1421(SNR) of the user masking contour curve1406at a given frequency1409is greater than the SNR1422of the target masking contour curve1407at the same given frequency1409. Note that the SNR is herein defined as the level of the signal tone compared to the level of the masker noise. The broader the curve will be, the greater the SNR. The given frequency1409at which the SNRs1421and1422are calculated may be arbitrarily chosen, for example, to be beyond a minimum distance from the probe tone frequency1408.

The sound level1430(in dB) of the target masking contour curve1407at a given frequency corresponds (see bent arrow1431inFIG. 14) to an input sound level1441entering the compression system. The objective is that the sound level1442outputted by the compression system will match the user masking contour curve1406, i.e., that this sound level1442is substantially equal to the sound level (in dB) of the user masking contour curve1406at the given frequency1409. This condition allows the derivation of the threshold1411(which has to be below the input sound level1441) and the ratio1412. In other words, input sound level1441and output sound level1442determine a reference point of the compression curve. As noted above, threshold1411must be selected to be lower than input sound level1441—if it is not, there will be no change, as below the threshold of the compressor, the system is linear). Once the threshold1411is selected, the ratio1412can be determined from the threshold and the reference point of the compression curve.

In the context of the present invention, a masking contour curve is obtained from a user hearing test. A target masking contour curve1407is interpolated from at least the user masking contour curve1406and a reference masking contour curve, representing the curve of a normal hearing individual. The target masking contour curve1407is preferred over a reference curve because fitting an audio signal to a reference curve is not necessarily optimal. Depending on the initial hearing ability of the listener, fitting the processing according to a reference curve may cause an excess of processing to spoil the quality of the signal. The objective is to process the signal in order to obtain a good balance between an objective benefit and a good sound quality.

The given frequency1409is then chosen. It may be chosen arbitrarily, e.g., at a certain distance from the tone frequency1408. The corresponding sound levels of the listener and target masking contour curves are determined at this given frequency1409. The value of these sound levels may be determined graphically on the y-axis1402.

The right panel inFIG. 14(see the contiguous graph) illustrates a hard knee DRC, with a threshold1411and a ratio1412as parameters that need to be determined. An input sound signal having a sound level1430/1441at a given frequency1409enters the compression system (see bent arrow1431indicating correspondence between1430/1441). The sound signal should be processed by the DRC in such a way that the outputted sound level is the sound level of the user masking contour curve1406at the given frequency1409. The threshold1411should not exceed the input sound level1441, otherwise compression will not occur. Multiple sets of threshold and ratio parameters are possible. Preferred sets can be selected depending on a fitting algorithm and/or objective fitting data that have proven to show the most benefit in terms of sound quality. For example, either one of the threshold1411and ratio1412may be chosen to have a default value, and the respective other one of the parameters can then be determined by imposing the above-described condition.

For calculating gain within a subband signal, the results of an audiogram may be used. For instance, raised thresholds may be compensated for by a corresponding frequency gain.

In one embodiment of the present disclosure, as shown inFIG. 15, DSP parameters in a multiband dynamic processor may be calculated by optimizing perceptually relevant information (e.g. perceptual entropy) through parameterization using user threshold and suprathreshold hearing data (see commonly owned applications U.S. Ser. No. 16/206,376 and EP18208020.0). Briefly, in order to optimally parameterize a multiband dynamic processor through perceptually relevant information, an audio sample1501, or body of audio samples, is first processed by a parameterized multiband dynamics processor1502and the perceptual entropy of the file is calculated1503according to user threshold and suprathreshold hearing data1507. After calculation, the multiband dynamic processor is re-parameterized1511according to a given set of parameter heuristics, derived from optimization, and from this—the audio sample(s) is reprocessed1502and the PRI calculated1503. In other words, the multiband dynamics processor is configured to process the audio sample so that it has a higher PRI value for the particular listener, taking into account the individual listener's threshold and suprathreshold information1507. To this end, parameterization of the multiband dynamics processor is adapted to increase the PRI of the processed audio sample over the unprocessed audio sample. The parameters of the multiband dynamics processor are determined by an optimization process that uses PRI as its optimization criteria.

PRI can be calculated according to a variety of methods found. One such method, also called perceptual entropy, was developed by James D. Johnston at Bell Labs, generally comprising: transforming a sampled window of audio signal into the frequency domain, obtaining masking thresholds using psychoacoustic rules by performing critical band analysis, determining noise-like or tone-like regions of the audio signal, applying thresholding rules for the signal and then accounting for absolute hearing thresholds. Following this, the number of bits required to quantize the spectrum without introducing perceptible quantization error is determined. For instance, Painter & Spanias disclose a formulation for perceptual entropy in units of bits/s, which is closely related to ISO/IEC MPEG-1 psychoacoustic model 2 [Painter & Spanias, Perceptual Coding of Digital Audio, Proc. Of IEEE, Vol. 88, No. 4 (2000); see also generally Moving Picture Expert Group standards https://mpeg.chiariglione.org/standards; both documents included by reference].

Various optimization methods are possible to maximize the PRI of audio samples, depending on the type of the applied audio processing function such as the above mentioned multiband dynamics processor. For example, a subband dynamic compressor may be parameterized by compression threshold, attack time, gain and compression ratio for each subband, and these parameters may be determined by the optimization process. In some cases, the effect of the multiband dynamics processor on the audio signal is nonlinear and an appropriate optimization technique such as gradient descend is required. The number of parameters that need to be determined may become large, e.g. if the audio signal is processed in many subbands and a plurality of parameters needs to be determined for each subband. In such cases, it may not be practicable to optimize all parameters simultaneously and a sequential approach for parameter optimization may be applied. Although sequential optimization procedures do not necessarily result in the optimum parameters, the obtained parameter values result in increased PRI over the unprocessed audio sample, thereby improving the listener's listening experience.

Other parameterization processes commonly known in the art may be used to calculate parameters based off user-generated threshold and suprathreshold information. For instance, common prescription techniques for linear and non-linear DSP may be employed. Well known procedures for linear hearing aid algorithms include POGO, NAL, and DSL. See, e.g., H. Dillon, Hearing Aids, 2ndEdition, Boomerang Press, 2012.

Fine tuning of any of the above mentioned techniques may be estimated from manual fitting data. For instance, it is common in the art to fit a multiband dynamic processor according to series of tests given to a patient in which parameters are adjusted according to a patient's responses, e.g. a series of A/B tests/decision tree paradigm in which the patient is asked which set of parameters subjectively sounds better. This testing ultimately guides the optimal parameterization of the DSP. In the instance of the present invention, manually-fit DSP parameters results of a given hearing profile can be categorized and inputted into the server database. Subsequently, a user's parameters may be calculated based on the approaches delineated inFIGS. 6 and/or 7.

FIG. 16shows an example of computing system1600, which can be for example any computing device making up (e.g., mobile device100, server, etc.) or any component thereof in which the components of the system are in communication with each other using connection1605. Connection1605can be a physical connection via a bus, or a direct connection into processor1610, such as in a chipset architecture. Connection1605can also be a virtual connection, networked connection, or logical connection.

Example system1600includes at least one processing unit (CPU or processor)1610and connection1605that couples various system components including system memory1615, such as read only memory (ROM)1620and random access memory (RAM)1625to processor1610. Computing system1600can include a cache of high-speed memory1612connected directly with, in close proximity to, or integrated as part of processor1610.

Processor1610can include any general purpose processor and a hardware service or software service, such as services1632,1634, and1636stored in storage device1630, configured to control processor1610as well as a special-purpose processor where software instructions are incorporated into the actual processor design. Processor1610may essentially be a completely self-contained computing system, containing multiple cores or processors, a bus, memory controller, cache, etc. A multi-core processor may be symmetric or asymmetric.

To enable user interaction, computing system1600includes an input device1645, which can represent any number of input mechanisms, such as a microphone for speech, a touch-sensitive screen for gesture or graphical input, keyboard, mouse, motion input, speech, etc. Computing system1600can also include output device1635, which can be one or more of a number of output mechanisms known to those of skill in the art. In some instances, multimodal systems can enable a user to provide multiple types of input/output to communicate with computing system1600. Computing system1600can include communications interface1640, which can generally govern and manage the user input and system output. There is no restriction on operating on any particular hardware arrangement and therefore the basic features here may easily be substituted for improved hardware or firmware arrangements as they are developed.

The storage device1630can include software services, servers, services, etc., that when the code that defines such software is executed by the processor1610, it causes the system to perform a function. In some embodiments, a hardware service that performs a particular function can include the software component stored in a computer-readable medium in connection with the necessary hardware components, such as processor1610, connection1605, output device1635, etc., to carry out the function.

The presented technology offers an efficient and accurate way to personalize audio replay on a plurality of consumer electronic devices through server-mediated sound personalization. It is to be understood that the present disclosure contemplates numerous variations, options, and alternatives. For clarity of explanation, in some instances the present technology may be presented as including individual functional blocks including functional blocks comprising devices, device components, steps or routines in a method embodied in software, or combinations of hardware and software.

Devices implementing methods according to these disclosures can comprise hardware, firmware and/or software, and can take any of a variety of form factors. Typical examples of such form factors include laptops, smart phones, small form factor personal computers, personal digital assistants, rackmount devices, standalone devices, and so on. Functionality described herein also can be embodied in peripherals or add-in cards. Such functionality can also be implemented on a circuit board among different chips or different processes executing in a single device, by way of further example. The instructions, media for conveying such instructions, computing resources for executing them, and other structures for supporting such computing resources are means for providing the functions described in these disclosures.