System and method for sound quality enhancement with personalized sound equalizer control

Abstract

A computer-implemented method includes calculating a percentile value of a normalized gain for each of a plurality of equalizer bands, wherein the normalized gain is based on a statistical specification of a distribution of gain values for each of the plurality of equalizer bands. The computer-implemented method also includes receiving a user input that adjusts a gain value for at least one of the plurality of equalizer bands, where the user input is received from a graphical user interface that shows the adjustment value as the percentile value. The computer-implemented method also includes adjusting an audio output of an audio playback device based on the received user input, and causing the audio playback device to play the adjusted audio output.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to sound reproduction, and more particularly to systems and methods for enhancing sound quality with personalized sound equalizer control. BACKGROUND

[0002] The statements in this section merely provide background information related to the present disclosure and can not constitute prior art.

[0003] Personalization of sound is often provided using equalization tools to improve the listening experience for individual users (such as headphones). However, these equalization tools can be complex or difficult to understand when used, resulting in poor sound and user experience. For example, multi-band graphs and parametric equalizers often require expertise to use effectively. As a result, most users are unable to properly adjust equalizer settings to obtain a desired or improved sound quality or listening experience. SUMMARY

[0004] This section provides a general summary of the present disclosure and not a comprehensive disclosure of its full scope or all of its features.

[0005] The present disclosure provides a computer-implemented method comprising: calculating a percentile value of a normalized gain for each of a plurality of equalizer bands, wherein the normalized gain is based on statistical norms of a distribution of gain values for each of the plurality of equalizer bands; receiving a user input adjusting a gain value of at least one of the plurality of equalizer bands, wherein the user input is received from a graphical user interface that shows the adjustment value as the percentile value; adjusting an audio output of an audio playback device based on the received user input; and causing the audio playback device to play the adjusted audio output. In the computer-implemented method, the plurality of equalizer bands comprises a low frequency band, a mid frequency band, and a high frequency band, and the computer-implemented method further comprises determining a normalized level of the low frequency band, the mid frequency band, and the high frequency band by calculating an average level of a frequency response error curve for each of the low frequency band, the mid frequency band, and the high frequency band and configuring a corresponding filter; wherein the audio playback device comprises headphones, and the computer-implemented method further comprises calculating a difference or error in frequency response between two headphones to generate the frequency response error curve; wherein the graphical user interface comprises a virtual control corresponding to each of the low frequency band, the mid frequency band, and the high frequency band, wherein the virtual control is configured to allow adjustment of the gain value displayed as the percentile value; wherein the virtual control comprises three virtual rotary controls or three virtual sliders corresponding to each of the low frequency band, the mid frequency band, and the high frequency band, wherein the virtual control is color-coded with different colors to represent where the gain falls within the distribution of statistical norms.

[0006] The computer-implemented method further comprises storing a user’s individualized data to a database, wherein the individualized data defines the user’s personal sound profile and comprises a gain setting, a headphone model, a frequency response, a program, a playback SPL, a demographic / hearing profile, or a combination thereof; the computer-implemented method further comprises automatically individualizing the gain values for a plurality of equalizer bands based on a model, the model being generated by machine learning and configured to predict the user’s personal sound profile based on an individual profile, a demographic profile, a hearing profile, or a combination thereof; the computer-implemented method further comprises calculating and categorizing the personal sound profile according to a category and a percentile corresponding to the adjusted gain received from the user; the computer-implemented method further comprises calculating the value of the normalized gain for each of the plurality of equalizer bands as a percentile relative to a reference frequency response curve.

[0007] The computer-implemented method further includes: wherein the audio playback device comprises headphones, and computing the percentile value of the normalized gain for each of the plurality of equalizer bands comprises normalizing a frequency response of the headphones using a reference frequency response and determining a mean value for each of the plurality of equalizer bands using the normalized frequency response; wherein receiving the user input to adjust the gain value for at least one of the plurality of equalizer bands comprises receiving one or more adjustments corresponding to a standard test track; the computer-implemented method further includes, prior to receiving the user input, displaying, via the graphical user interface, the normalized gain value for each of the plurality of equalizer bands; and wherein the statistical specification is updated based on a plurality of listener’s preferred settings.

[0008] The present disclosure provides a system comprising an audio playback device; a graphical user interface; a processor; and a non-transitory computer-readable storage medium storing instructions that, when executed by the processor, cause the processor to: compute a percentile value of a normalized gain for each of a plurality of equalizer bands, wherein the normalized gain is based on a statistical specification of a distribution of gain values for each of the plurality of equalizer bands; receive a user input to adjust a gain value for at least one of the plurality of equalizer bands, wherein the user input is received from the graphical user interface that shows the adjustment value as the percentile value; adjust an audio output of the audio playback device based on the received user input; and cause the audio playback device to play the adjusted audio output. The system further includes: wherein the audio playback device comprises headphones, and the plurality of equalizer bands comprises a bass band, a midrange band, and a treble band, and the instructions further cause the processor to: determine a normalized level for the bass band, the midrange band, and the treble band by computing a mean level of a frequency response error curve for each of the bass band, the midrange band, and the treble band and configuring a corresponding filter; and compute a difference or error in frequency response between the headphones and another set of headphones to generate the frequency response error curve; and wherein the graphical user interface comprises a virtual control corresponding to each of the bass band, the midrange band, and the treble band, wherein the virtual control is configured to allow adjustment of the gain value displayed as the percentile value, wherein the virtual control comprises three virtual rotary controls or three virtual sliders corresponding to each of the bass band, the midrange band, and the treble band, and wherein the virtual control is color-coded with different colors to represent a position of the gain falling within the distribution of the statistical specification.

[0009] The system further includes, wherein the instructions also cause the processor to store user-specific data in a database, wherein the personal data defines the user's personal voice profile and includes gain settings, headphone model, frequency response, program, playback SPL, crowd / hearing profile, or a combination thereof, and wherein the personal voice profile is calculated and categorized according to a category and percentile corresponding to the adjusted gain received from the user; wherein the audio playback device includes headphones, and the instructions also cause the processor to calculate the percentile value of the normalized gain for each of a plurality of equalizer bands, including: normalizing the frequency response of the headphones using a reference frequency response, and using the normalized frequency response to determine the mean of each of the plurality of equalizer bands; and wherein the graphical user interface is configured to display the normalized gain value of each of the plurality of equalizer bands before receiving user input.

[0010] This disclosure provides one or more non-transitory computer-readable media storing processor-executable instructions, which, when executed by at least one processor, cause the at least one processor to: calculate a percentile value of normalized gain for each of a plurality of equalizer bands, wherein the normalized gain is based on a statistical specification of the distribution of gain values ​​for each of the plurality of equalizer bands; receive user input adjusting the gain value of at least one of the plurality of equalizer bands, wherein the user input is received from a graphical user interface that displays the adjusted value as the percentile value; adjust the audio output of an audio playback device based on the received user input; and cause the audio playback device to play the adjusted audio output.

[0011] As will be apparent from the description provided herein, further applicability will be readily apparent. It should be understood that the descriptions and specific examples are for illustrative purposes only and are not intended to limit the scope of this disclosure. Attached Figure Description

[0012] To better understand this disclosure, various forms of the disclosure, given by way of example, will now be described with reference to the accompanying drawings, in which:

[0013] Figure 1 This is a block diagram illustrating a tone controller according to one or more embodiments;

[0014] Figure 2 It is a graph showing the normalized gain distribution according to one or more implementation schemes;

[0015] Figure 3 It is a graph showing different equalizer frequency bands according to one or more implementation schemes;

[0016] Figure 4 is a graph illustrating filter band parameters according to one or more embodiments;

[0017] Figure 5 is a table illustrating filter band parameters according to one or more embodiments;

[0018] Figure 6 is a graph illustrating a user interface according to one or more embodiments;

[0019] Figure 7 is a graph illustrating another user interface according to one or more embodiments;

[0020] Figure 8 is a graph illustrating a sound profile according to one or more embodiments;

[0021] Figure 9 is a flowchart illustrating an equalizer process according to one or more embodiments;

[0022] Figure 10 is a graph illustrating a frequency response according to one or more embodiments;

[0023] Figure 11 is a table illustrating a distribution of gain values according to one or more embodiments;

[0024] Figure 12 is a graph illustrating a user interface with normalized percentile values according to one or more embodiments;

[0025] Figure 13 is a block diagram illustrating a database arrangement according to one or more embodiments;

[0026] Figure 14 is a graph illustrating a personal sound profile according to one or more embodiments; and

[0027] Figure 15 is a block diagram of a computing device according to one or more embodiments.

[0028] The accompanying drawings described herein are for purposes of illustration only and are not intended to limit the scope of the present disclosure in any way. DETAILED DESCRIPTION

[0029] The following description is merely exemplary in nature and is not intended to limit the present disclosure, application, or uses. It should be understood that throughout the drawings, corresponding reference numerals indicate like or corresponding parts and features.

[0030] One or more embodiments of the present disclosure provide systems and methods for sound enhancement and / or personalization. In some examples, headphones are equalized to accommodate individual preferences in frequency response (FR), commonly referred to as sound personalization. The personalization process in one or more embodiments is simplified by using three fixed filters that address the three frequency regions that vary the most in preference: bass, midrange, and treble regions.

[0031] In one or more examples, personalization is achieved by adjusting the gain (dB) for each frequency band by a value expressed in percentiles (1 to 100) based on statistical norms established in a controlled study. For example, the study measures a large sample of listeners representing different demographic groups (e.g., age, gender, listening experience, hearing loss, etc.) with preference adjustments to bass / midrange / treble levels. The data from this study provides a distribution of responses from which the median, quartiles, and percentiles of preferred gain values can be known. Further cluster analysis can identify different classes or groups of listeners based on similarities in their sound profile preferences and potential demographic factors that can predict sound profile preferences. Thus, when there is a demographic profile match, one or more embodiments allow a listener to be customized with a set of statistical norms based on that group.

[0032] In one or more embodiments, the simplified user interface is provided with only three adjustments that focus on the three most common frequency regions, as described in greater detail herein, resulting in a faster and improved user experience. The user interface in one or more examples provides adjustments in percentiles rather than decibels (which can not be as meaningful to most listeners), thereby increasing the context and meaning of the settings. In one or more examples, feedback is also provided to the user when adjustments are far outside of the normal range, thereby providing a warning that the response can be an outlier from an error that needs further examination.

[0033] Reference Figure 1 is shown a tone controller 100 in accordance with one or more embodiments of the present disclosure. The tone controller 100 allows for sound personalization, as described in greater detail herein. For example, the tone controller 100 is configured to provide sound equalization based on statistical norms 102 to personalize sound output from, for example, headphones. That is, in one or more embodiments, the tone controller 100 uses the statistical norms 102 to allow for adjustments to tone settings corresponding to different groups of listeners that can have different listening “tastes” than the average listener.

[0034] The tonality controller 100 is configured to receive one or more percentile adjustments 104 as input, which is easier or more intuitive for a user to adjust different tonal properties corresponding to audio output settings 106, which in one or more embodiments include treble, midrange, and bass frequency settings. In the illustrated example, the output control is simplified to include only these three frequency settings (e.g., filter bands), namely a setting value in the high frequency range (e.g., above 4 kilohertz (kHz)), a midrange frequency range (e.g., 300 Hz to 4 kHz), and a bass frequency range (e.g., 20 Hz and 300 Hz). However, it should be understood that other frequency settings and ranges are also contemplated. Thus, in one or more embodiments, the tonality controller 100 is configured to receive adjustments by a user in percentiles or standard deviations rather than decibels (which a user can not understand in terms of subjectivity or tonal quality).

[0035] In one or more examples, the tonality controller 100 is configured to provide gain adjustments based on the statistical specification 102, as Figure 2 shown in the graph 200. That is, in one or more embodiments, the adjustable settings for treble, midrange, and bass frequencies are defined by a percentile or standard deviation range 202, as shown in the graph 200. In the illustrated example, the preferred gain falls within a two standard deviation (+ / - 2SD) range of the average gain setting. Thus, in some examples, the gain of an equalizer (EQ) filter (e.g., in an audio earphone) is controlled by the audio output settings 106 from the tonality controller 100 based on the percentile adjustments 104. Thus, a more intuitive approach is provided in one or more examples that represents a user’s gain adjustments in percentiles or standard deviations based on prior knowledge that the user’s adjustments fall in a position in a distribution of gain adjustments by a large sample of listeners in a controlled study of each of the three filter bands.

[0036] In one or more examples, the tonality controller 100 allows for implementation of a simplified sound equalizer. For example, as Figure 3 and Figure 4 shown in the graphs 300 and 400, a simple three-band equalizer is configured to control three primary frequency regions 302, which correspond to the three “important” frequency ranges of the frequency bands that need to be adjusted. In one or more embodiments, the tonality controller 100 is configured to only allow gain adjustments using values defined and / or illustrated based on the statistical specification 102, which is shown as a curve 402 of percentile values defining filter band parameters. For example, Figure 5The illustrated table 500 shows parameters corresponding to bass control 502, mid control 504, and treble control 506 using the statistical specification 102. Control attributes or parameters can be defined based on, for example, type, frequency, Q-factor, and gain, among others. However, other attributes or parameters can also be used.

[0037] In one or more examples, there is provided a user interface 600 and 700 as illustrated in Figure 6 and 7 The user interfaces 600 and 700 are configured to receive percentile-based adjustment settings or changes, as can be seen. That is, gain adjustments are expressed in percentiles rather than decibels. For example, the user interface 600 includes three rotary controls 602 (e.g., virtual knobs or dials) corresponding to bass control 502, mid control 504, and treble control 506. The user interface 700 includes three slider controls 702 (e.g., virtual sliding members or elements) corresponding to bass control 502, mid control 504, and treble control 506. Thus, gain adjustments are defined and controlled based on percentiles, which is a more intuitive and easier to control arrangement. It will be appreciated that other non-decibel settings controls can be used, such as controls defined based on standard deviation, as described in more detail herein.

[0038] In operation, one or more embodiments allow equalization of, for example, headphones to accommodate individual preferences in their FRs, thereby providing improved sound personalization. For example, the simplified user interfaces 600, 700 having only three adjustments with focus on three frequency regions provide a faster and improved user experience. In one or more examples, the audience is segmented based on headphone preferences, such as but not limited to, based on preferred headphone FRs, the audience is divided into at least three different audience groups: (1) audience preference ratings for different headphone models, with a majority of the audience (64%) preferring headphones with FRs that closely match the headphone manufacturer’s target FRs; (2) a certain group (21%) preferring headphones with more bass and treble than the manufacturer’s target FRs; (3) a smallest group (15%) preferring headphones with more bass and treble than the manufacturer’s target FRs. Thus, in one or more embodiments, the audience can be divided into, for example, three different groups based on headphone preferences.

[0039] For example, the graph 800 shows plots 802, 804, 806 corresponding to the average FR of the top five preferred headphones in a set of thirty headphones, which have been normalized for each listening class / group’s target response (e.g., the Harman target). It can be seen that the taste differences (e.g., sound listening preferences) of different groups are mostly related to the relative bass and treble levels, with less difference in the midrange. Thus, in one or more embodiments, individual taste can be accommodated by using two to three fixed filters focused on bass, midrange, and treble, and adjusting the gains, as described in more detail herein.

[0040] In one or more examples, headphone preference groups or classes are predicted based on a listener’s experience, age, gender, hearing loss, or a combination thereof. It will be appreciated that a listener can be predicted to be included in one of the three classes based on a demographic profile (e.g., age, gender, etc.) as well as hearing experience and hearing loss. With these factors, in one or more embodiments, a model is generated using these factors that can be used to automatically personalize a listener’s headphone class, such as described in U.S. Patent Application Publication No. 2021 / 0195328. However, other sound enhancement and personalization can also be used.

[0041] As some examples of sound preferences, it has been found that the effect of age-related hearing loss on preferred high FRs for in-ear headphones suggests that as hearing loss increases, listeners prefer higher frequencies above 6 kHz. Noise-induced hearing loss tends to produce a sharp drop in the audiogram at 3-4 kHz, which is a frequency region most sensitive to natural resonances produced by the ear canal and concha. Thus, an EQ filter centered on this region can help compensate for noise-induced hearing loss.

[0042] In addition, the ear canal and head / ear / torso variations also affect personalization. For example, it has been found that due to anthropometric differences in the shape and geometry of the individual listener's ear canal and pinna, the individual listener's preference for mid-high frequencies (e.g., 2kHz-6kHz) can vary, which can result in up to 10dB differences when measured at the eardrum or at the entrance of the open ear canal. These features are known to affect the perceived sound quality (timbre) as well as spatial dimensions such as externalization and localization. Therefore, most of the personal preferences related to earphone FR are grouped into three populations, and these preferences are accommodated in one or more embodiments using three equalization filters for bass, midrange (ear canal region), and treble, as described in more detail herein, rather than using a complex and difficult to use without expertise multi-band graphic equalizer or parametric equalizer; or rather than having the listener complete a series of trials in which the EQ is incrementally changed and the settings are adjusted based on the listener's corresponding, until the settings converge to a solution, which the user can not have the patience to perform to personalize their earphones.

[0043] In one or more embodiments, all three frequency regions are processed, which allows the listener to make adjustments directly without having to complete a series of test trials, increasing the likelihood that the features are used. The direct approach combined with the reduction to three fixed filters with gain adjustments results in a faster and more intuitive personalization method. For example, converting the gain adjustments from dB to percentiles can provide the user with more context about the user's preferred adjustments in relation to the tastes of other listeners. In one or more examples, the adjustment controls (such as shown in FIGS. 1-3) display percentiles and color coding to indicate, for example, typical gain values (green: 40-60 percentiles), less common values (e.g., yellow: 20-40 and 60-80 percentiles), and extreme values (e.g., red: 0-20 and 80-100). It is noted that extreme gain values beyond 1 and 99 percentiles would alert the listener that the adjustments can be outliers, and the user can want to reconsider the settings. Figure 6 and Figure 7 It is noted that extreme gain values beyond 1 and 99 percentiles would alert the listener that the adjustments can be outliers, and the user can want to reconsider the settings.

[0044] In one or more embodiments, the selection of music tracks used for the control study contains full bandwidth spectral content and is balanced or neutral, with no significant emphasis / boost or cut in any frequency region. For example, ideal tracks can be found through expert listening and spectral analysis, then used for the control study and shared during the subsequent personalization process. Furthermore, the sensitivity of human hearing is nonlinear in both level and frequency dependence, meaning that the perceived loudness and timbre of a sound changes depending on the sound pressure level (SPL) of the playback level. This psychoacoustic phenomenon is known as the loudness contour and describes the precise level adjustments needed for pure tones of different frequencies and SPLs to sound equally loud. In one or more examples, the overall playback levels used in the control study and personalization process are calibrated accordingly. For example, the selected levels are close to each other and at a typical level of comfort (e.g., 78-80 dB (B-weighted)). It is noted that if the music track, sensitivity, and volume settings of the earphone are known, the playback SPL of the earphone can be established. Earphones with a feedback microphone for sound pressure level (ANC) purposes can be used to measure the SPL inside the ear.

[0045] It is also noted that in one or more examples, the FR of the earphone used for personalization is selected to ideally match the FR of the reference earphone used in the control study, as this is the underlying assumption when using statistical norms in the personalization process. In one or more embodiments, the earphone is designed to match the FR of the reference earphone used in the control (e.g., the Harman target curve). The bass / mid / treble of the consumer’s earphone is then customized to meet the personalization needs. In one or more other examples, prior to personalization, the earphone is equalized by the onboard DSP so that the FR of the earphone matches the FR of the reference earphone. The FR of the earphone can be measured during production and stored in the earphone or in the personalization application to enable this feature. Alternatively, data in one or more databases storing measurements for hundreds of earphone models can be downloaded. In one or more other examples, the earphone is calibrated so that the difference in FR is known and taken into account during the personalization process. If the FR of both the reference earphone and the consumer’s earphone are known, calibration is performed to calculate the relative levels of each EQ band used during the personalization process.

[0046] Figure 9is a block diagram of performing the EQ process 900 according to one or more embodiments. The EQ process 900 can be implemented using, for example, an application on a computing device (e.g., a smartphone, a tablet, an embedded audio device / controller, a box) that includes an audio DSP processor on the device or the earphone itself that performs the processing used in the personalization process. In one or more examples, the application includes a graphical user interface (GUI) on the application (or a screen on the charging box) for the user to adjust and provide feedback. In one or more examples, the user can use voice commands to the voice assistant agent (e.g., bass: 75%, midrange: 50%, treble: 50%) for adjustments. For further example, the user can also say “increase bass” and the voice agent feedbacks the current level in percentiles (e.g., “current bass level is at 75 percentile”). It is noted that, in one or more examples, the communication from the application to the earphone and to the cloud / internet to access data (e.g., music, earphone FR data, latest data of statistical norms) is provided via Bluetooth and WiFi or other wireless (or wired) communication links. As described in more detail herein, the filters used in the personalization process are shown in Figure 3 and Figure 4 and examples of the GUI are shown in Figure 6 and Figure 7 showing knobs or sliders displaying key information such as: current gain values as percentiles, color coding representing the increase values from the media, and optionally the position of said values within the normal distribution of preferred values.

[0047] Referring now particularly to Figure 9 and the EQ process, at 902, the FR of the target earphone is determined. For example, the FR of the target earphone is obtained to calibrate and normalize the response of the earphone to a known reference FR (e.g., the Harman target curve) on which the statistical norms of preferred levels are based. For example, as shown in the graph 1000 of Figure 10 the FR of the earphone represented by curve 1002 is normalized to the reference FR represented by curve 1004 by subtracting the two curves 1002, 1004 to obtain curve 1006 (e.g., a difference or error curve). The mean value of each of the frequency bands can then be calculated.

[0048] The target headphone's FR (Frequency Rate) can be stored, for example, in the headphone's hardware or software application (such as during manufacturing), downloaded from the manufacturer, or retrieved from a public database of headphone measurements. That is, the target headphone is normalized relative to a reference target FR. For example, the current bass, midrange, and treble levels of the target headphone are determined and normalized relative to a reference. Thus, in one or more examples, the target headphone FR is divided by the target's FR, representing the difference or error curve 1006. The level for each frequency band of the target is then determined. For example, the normalized bass, midrange, and treble levels of the target headphone are determined by calculating the average level of the error curves within the frequency bands of the three filters. It should be understood that the greater the deviation from 0 dB, the larger the resulting value.

[0049] The statistical specification of the gain is determined at 904. For example, as... Figure 11 As shown in Table 1100, a distribution of gain values ​​for each EQ 1102 (e.g., bass, midrange, and treble) based on statistical specifications is obtained, including dB values, corresponding percentiles, and percentage values ​​in dB. It is important to note that this operation utilizes prior knowledge of the distribution of gain values ​​based on studies of listeners adjusting the three frequency bands under controlled testing conditions. In one or more examples, controlled studies using the three specified filters described herein are conducted using, for example, a large sample of listeners including a range of ages, listening experiences, genders, and hearing losses. The percentile value of the current normalized gain for each frequency band is calculated. For example, standard statistical analysis applied to the dataset of the controlled studies can be used to calculate the gain value of each filter as a percentile. The sample mean / median gain values ​​represent the 50th percentile, ±1 standard deviation (SD) relative to the mean represents the 16th and 84th percentiles, ±2 SD represents the 2.5th and 97.5th percentiles, and ±3 SD represents the 0.3th and 99.7th percentiles. It should be noted that any percentile between these deviations can be estimated and applied to the user's adjustment during the personalization process.

[0050] At 904, the headphones are calibrated based on FR. In this calibration, the difference between the FR of the headphones used in the control study and the personalization process is considered. For example, if the FRs do not match, the difference is considered, such as equalizing the headphones to the same FR as the headphones used in the control study (e.g., a Harman target), and then allowing users to personalize the FR according to their desired listening style. In one or more examples, without performing a matching, the FR difference or error between the two headphones is first calculated, represented by the FR error curve 1006 (see...). Figure 10). According to curve 1006, the average error level in each filter band is calculated. For example, if there is no error (0 dB), then the percentile value for each band will be 50% before adjustment is made. A positive error in gain produces a percentile higher than 50%, while a negative error in gain produces a percentile lower than 50%.

[0051] At 906, the listener adjusts the gain in each EQ band as needed, e.g., while listening to a music track and using UI 1200, as shown. For example, the listener can adjust the gain in each filter band based on taste. In one or more embodiments, the user can make the adjustments using a GUI (see Figure 12 and Figure 6 and Figure 7 ), where a knob (e.g., rotary control 602) or slider (e.g., slider control 702) is used to increase / decrease the level in each band. The adjustment is shown in different colors of the percentile to represent where the gain falls within the distribution of the statistical norm, providing the user with context, meaning, and guidance as to how the user’s adjustments relate to other listeners, as described in more detail herein. As shown, the user can adjust three bands using three independent controls 1202 (corresponding to the headphone EQ), and the adjustment is represented in the form of a percentile. That is, the normalized value for each band relative to the reference curve 1004 (see Figure 12 ) is calculated as a percentile, as can be seen in table 1210 (e.g., bass, mid, and treble gain values of 29%, 50%, and 33%). In one or more examples, as the gain adjustment further deviates from the median value (50%), the scale of each of controls 1202 takes on a color coding (e.g., green / yellow / red). In the example shown, section 1204 represents the 33 to 67 percentile, and sections 1206, 1208 are the upper and lower sextiles. In one or more embodiments, the color coding makes the user aware of how atypical the user’s adjustments are relative to other listeners, and reduces or avoids the likelihood of the user using outlier values. Figure 10

[0052] It is noted that, as described herein, the user can choose to make the adjustments using a standard test track. For example, in one or more examples, the user uses a standardized music test track used in the control study to eliminate interference variable procedures and playback levels, which are also adjusted to match the levels used in the control study. It is noted that without controlling for interference variables, the statistical norm used to determine the user’s percentile gain can be less accurate. In one or more examples, the user optionally uses one or more standard reference tracks played at a reference SPL to perform the process.

[0053] ​At 908, the EQ settings can optionally be stored, such as by uploading to a storage device (e.g., as...). Figure 13 Database 1300 in cloud storage (as shown) is used to improve the accuracy of population-based statistical profiles. For example, user population profiles (e.g., age, gender, hearing loss, listening experience, etc.) and other information 1302 (such as music tracks, headphone models, SPL, etc.) are also stored (e.g., uploaded to database 1300). For example, user personalized data (e.g., gain settings, headphone model, FR, program, playback SPL, population / hearing profile) is uploaded to database 1300 in the cloud (and can be shared with the user's other audio devices) for further analysis along with other user data in one or more examples, then providing updated statistical profiles 1304 for the listener population. Overall, in one or more examples, user personalized data provides insights into the relationships between personalized profiles, music and playback levels, and the user's population / hearing profile.

[0054] It is important to note that in one or more examples, a larger dataset and machine learning and / or artificial intelligence (AI) are used to generate models that predict a user's personalized configuration based on their personality / group / hearing profile and automatically personalize headphones without user intervention. By incorporating headphones, programs, and playback SPL into the analysis, compensation for one or more of these factors can be automatically adjusted. For example, if the bias effect of programs and playback levels on a user's personalized profile is determined through analysis, this bias can be considered in further analysis and models. Furthermore, in one or more examples, with accurate predictive models, EQ compensation can be automatically applied to specific programs and playback SPL to match the user's personalized profile.

[0055] Using the EQ process 900, it is possible to generate, for example Figure 14 The personal voice profile 1400 is shown. For example, after EQ adjustments, a user's voice profile 1400 can be calculated based on a large sample of listeners and categorized according to the user's category and percentile, as shown in curve 1402.

[0056] In one or more examples, database 1300 stores personalized data for a user on a specific headphone model. However, it should be noted that the personal sound profile 1400 can be applied to any headphone model with a known headphone frequency response. This is possible in one or more examples because the personal sound profile 1400 is applied after calculating a normalized level based on the error correction / difference frequency response, and this normalized level can be calculated for any headphone with a known frequency response.

[0057] One or more examples can be implemented with a computing device 1500 as shown in Figure 15 The computing device 1500 can be any type of device capable of executing an application, including but not limited to instructions associated with the FR application 1501, the FR filter 1502, the personalized FR filter 1503, the filter generator 1504, the personalized filter generator 1505, the GUI 1506, and / or the hardware identification (ID) application 1507. For example, but not by way of limitation, the computing device 1500 can be an electronic tablet, a smartphone, a laptop computer, an infotainment system incorporated into a vehicle, a home entertainment system, etc. Alternatively, the computing device 1500 can be implemented as a standalone chip, such as a microprocessor, or as part of a more comprehensive solution implemented as an application-specific integrated circuit (ASIC), a system-on-chip (SoC), etc. It should be noted that the computing device 1500 described herein is illustrative, and that any other technically feasible configuration falls within the scope of the present disclosure.

[0058] As shown, the computing device 1500 includes, without limitation, an interconnect (bus) 1510 connecting a processor 1512, an input / output (I / O) device interface 1514 coupled to I / O devices 1520, a memory 1530, and a network interface 1516. The processor 1512 can be any suitable processor, such as a central processing unit (CPU), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), any other type of processing unit, or a combination of different processing units, implemented as a CPU configured to operate in conjunction with a digital signal processor (DSP), for example. In one or more embodiments, the processor 1512 includes a CPU and a DSP, for example. Generally, the processor 1512 can be any technically feasible hardware unit capable of processing data and / or executing instructions to facilitate the operation of the computing device 1500 as described herein. Moreover, in the context of the present disclosure, the computing elements shown in the computing device 1500 can correspond to a physical computing system (e.g., a system in a data center), or can be a virtual computing instance executing within a computing cloud. Figure 15

[0059] The I / O devices 1520 can include devices capable of providing input, such as a keyboard, a mouse, a touchscreen, a microphone 1522, etc., and devices capable of providing output, such as any type of audio playback device (including a speaker 1524 (of headphones)) and a display screen. The display screen can be a computer monitor, a video display screen, a display device incorporated into a handheld device, or any other technically feasible display screen. Particular examples of the speaker 1524 can include one or more speakers that are elements of an audio system.

[0060] ​I / O devices 1520 can include additional devices that can both receive input and provide output, such as a touchscreen, a universal serial bus (USB) port, and the like. Such I / O devices 1520 can be configured to receive various types of input from an end user of computing device 1500 and also be configured to provide various types of output to an end user of computing device 1500, such as a displayed digital image (e.g., a user interface or virtual user interface element). In one or more embodiments, one or more of I / O devices 1520 are configured to couple computing device 1500 to communication network 1540.

[0061] I / O interface 1514 enables communication between I / O devices 1520 and processor 1512. I / O interface 1514 typically includes logic to interpret addresses generated by processor 1512 corresponding to I / O devices 1520. I / O interface 1514 can also be configured to implement signal exchanges between processor 1512 and I / O devices 1520, and / or generate interrupts associated with I / O devices 1520. I / O interface 1514 can be implemented as any technically feasible CPU, ASIC, FPGA, any other type of processing unit or device.

[0062] Network interface 1516 is a computer hardware component that connects processor 1512 to communication network 1540. Network interface 1516 can be implemented in computing device 1500 as a standalone card, processor, or other hardware device. In one or more embodiments in which communication network 1540 includes a WiFi network or a WPAN, network interface 1516 includes a suitable wireless transceiver. Alternatively or additionally, network interface 1516 can be configured with cellular communication capabilities, satellite phone communication capabilities, wireless WAN communication capabilities, or other types of communication capabilities that allow for communication with communication network 1540 and other computing devices external to computing device 1500.

[0063] Memory 1530 can include a random access memory (RAM) module, a flash memory unit, or any other type of memory unit or combination thereof. Processor 1512, I / O device interface 1514, and network interface 1516 are configured to read data from and write data to memory 1530. Memory 1530 includes various software programs executable by processor 1512, as well as application data associated with the software programs, including FR application 1501, FR filter 1502, personalized FR filter 1503, filter generator 1504, personalized filter generator 1505, and / or GUI 1506, and / or hardware ID application 1507.

[0064] Memory 1530 can include a non-transitory computer-readable medium, such as a non-volatile storage device. In one or more embodiments, memory 1530 includes a database 1532 of frequency response curves for various earpieces, speakers, or other devices, as well as other data described in greater detail herein. Additionally or alternatively, in one or more embodiments, database 1532 includes filter response presets, device-specific FR filters, and the like, such as personal sound profile 1400. Alternatively, in one or more embodiments, database 1532 can reside at a remote location from computing device 1500, for example in a cloud computing environment with database 1300.

[0065] FR application 1501 is configured to implement one or more aspects of one or more embodiments described herein. For example, in one or more embodiments, FR application 1501 enables an audio system to generate audio output with different FRs. Further, in one or more embodiments, FR application 501 enables a user to modify audio output generated by an audio system (e.g., an earpiece) to match a personalized sound profile 1400. In one or more embodiments, the functionality of one or more of FR filter 1502, personalized FR filter 1503, filter generator 1504, personalized filter generator 1505, GUI 1506, and / or hardware ID application 1507 can be incorporated into FR application 1501.

[0066] FR filter 1502 is configured to modify an audio input signal according to a particular frequency response curve, such as bass, midrange, and treble described in greater detail herein. For example, in one or more embodiments, FR filter 1502 is a user-specific FR filter configured to modify an audio input based on user personalization. Alternatively, in one or more embodiments, the frequency response of FR filter 1502 is selected such that an audio input signal processed by FR filter 1502 is played back by a particular audio system and produces an audio output that approximates an audio output generated by an audio system having a target frequency response curve.

[0067] The individualized FR filter 1503 is configured to modify the audio input signal according to a particular user-selected frequency response curve, such as determined by the personal sound profile 1400. In one or more embodiments, the particular frequency response curve is selected by the user, as described in greater detail herein. For example, the individualized FR filter 1503 can be configured to implement certain equalization parameters that effectively equalize the original sound content in a user-selected manner. When the audio input signal is processed by the FR filter 1502 and the individualized FR filter 1503, and then played back by an audio system that selected the FR filter 1502, the resulting audio output generated by the audio system closely approximates the audio output that would be generated by an audio system having the user-selected frequency response curve, as described in greater detail herein.

[0068] Based on the foregoing, the following provides an overall summary of the present disclosure, but is not a comprehensive summary. In a first one or more embodiments Al, a computer-implemented method includes: calculating a percentile value of a normalized gain for each of a plurality of equalizer bands, wherein the normalized gain is based on a statistical specification of a distribution of gain values for each of the plurality of equalizer bands; receiving a user input that adjusts a gain value of at least one of the plurality of equalizer bands, wherein the user input is received from a graphical user interface that shows the adjustment value as the percentile value; adjusting an audio output of an audio playback device based on the received user input; and causing the audio playback device to play the adjusted audio output.

[0069] In a second one or more embodiments A2, which can include the first one or more embodiments Al, the plurality of equalizer bands includes a low frequency band, a mid frequency band, and a high frequency band, and the computer-implemented method further includes determining the normalized level of the low frequency band, the mid frequency band, and the high frequency band by calculating an average level of a frequency response error curve for each of the low frequency band, the mid frequency band, and the high frequency band and configuring a corresponding filter. In a third one or more embodiments A3, which can include one or more embodiments Al-A2, the audio playback device includes headphones, and the computer-implemented method further includes calculating a difference or error in frequency response between two headphones to generate the frequency response error curve. In a fourth one or more embodiments A4, which can include one or more embodiments Al-A3, the graphical user interface includes a virtual control corresponding to each of the low frequency band, the mid frequency band, and the high frequency band, wherein the virtual control is configured to allow adjustment of the gain value displayed as the percentile value.

[0070] In a fifth one or more embodiments A5, which can include one or more of the embodiments Al-A4, the virtual controls include three virtual rotary controls or three virtual sliders corresponding to each of the bass frequency range, the midrange frequency range, and the treble frequency range, wherein the virtual controls are color coded with different colors to represent where the gain falls within the distribution of the statistical norm. In a sixth one or more embodiments A6, which can include one or more of the embodiments Al-A5, further comprising storing user’s personalized data to a database, wherein the personalized data defines the user’s personal sound profile and includes gain settings, earphone model, frequency response, program, playback SPL, demographic / hearing profile, or a combination thereof. In a seventh one or more embodiments A7, which can include one or more of the embodiments Al-A6, further comprising automatically personalizing the gain values for a plurality of equalizer frequency ranges based on a model, the model generated by machine learning and configured to predict the user’s personal sound profile based on a personal profile, a demographic profile, a hearing profile, or a combination thereof. In an eighth one or more embodiments A8, which can include one or more of the embodiments Al-A7, further comprising computing and categorizing the personal sound profile according to categories and percentiles corresponding to adjusted gains received from the user.

[0071] In a ninth one or more embodiments A9, which can include one or more of the embodiments Al-A8, further comprising computing the value of the normalized gain for each of the plurality of equalizer frequency ranges as a percentile relative to a reference frequency response curve. In a tenth one or more embodiments A10, which can include one or more of the embodiments Al-A9, the audio playback device includes an earphone, and computing the percentile value of the normalized gain for each of the plurality of equalizer frequency ranges includes normalizing a frequency response of the earphone using a reference frequency response and determining a mean value for each of the plurality of equalizer frequency ranges using the normalized frequency response. In an eleventh one or more embodiments Al l, which can include one or more of the embodiments Al-A10, wherein receiving the user input adjusting the gain value of at least one of the plurality of equalizer frequency ranges includes receiving one or more adjustments corresponding to a standard test track.

[0072] In a twelfth one or more embodiments A12, which can include one or more of the embodiments Al-A11, further comprising displaying, via the graphical user interface, the normalized gain value for each of the plurality of equalizer frequency ranges prior to receiving the user input. In a thirteenth one or more embodiments A13, which can include one or more of the embodiments Al-A12, wherein the statistical norm is updated based on preferred settings of a plurality of listeners.

[0073] In a fourteenth one or more embodiments A14, which can include one or more of the embodiments Al-A13, a system includes an audio playback device; a graphical user interface; a processor; and a non-transitory computer-readable storage medium storing instructions that, when executed by the processor, cause the processor to: compute a percentile value of a normalized gain for each of a plurality of equalizer bands, wherein the normalized gain is based on a statistical norm of a distribution of gain values for each of the plurality of equalizer bands; receive a user input adjusting a gain value of at least one of the plurality of equalizer bands, wherein the user input is received from the graphical user interface, which shows the adjustment value as the percentile value; adjust an audio output of the audio playback device based on the received user input; and cause the audio playback device to play the adjusted audio output.

[0074] In a fifteenth one or more embodiments A15, which can include one or more of the embodiments Al-A14, wherein the audio playback device includes headphones, and the plurality of equalizer bands includes a low frequency band, a mid frequency band, and a high frequency band, and the instructions further cause the processor to: determine the normalized levels of the low frequency band, the mid frequency band, and the high frequency band by computing an average level of a frequency response error curve for each of the low frequency band, the mid frequency band, and the high frequency band and configuring a corresponding filter; and compute a difference or error in frequency response between the headphones and another set of headphones to generate the frequency response error curve. In a sixteenth one or more embodiments A16, which can include one or more of the embodiments Al-A15, wherein the graphical user interface includes a virtual control corresponding to each of the low frequency band, the mid frequency band, and the high frequency band, wherein the virtual control is configured to allow adjustment of the gain value displayed as the percentile value, wherein the virtual control includes three virtual rotary controls or three virtual sliders corresponding to each of the low frequency band, the mid frequency band, and the high frequency band, and wherein the virtual control is color coded with different colors to represent where the gain falls within the distribution of the statistical norm.

[0075] In a seventeenth one or more embodiments A17, which can include one or more of the embodiments A1-A16, wherein the instructions further cause the processor to store individualized data of a user to a database, wherein the individualized data defines a personal sound profile of the user and includes a gain setting, a headphone model, a frequency response, a program, a playback SPL, a demographic / hearing profile, or a combination thereof, and wherein the personal sound profile is computed and categorized according to a category and percentile corresponding to an adjusted gain received from the user. In an eighteenth one or more embodiments A18, which can include one or more of the embodiments A1-A17, wherein the audio playback device includes a headphone, and the instructions further cause the processor to compute the percentile value of the normalized gain for each of a plurality of equalizer bands, including: normalizing a frequency response of the headphone using a reference frequency response, and determining a mean value for each of the plurality of equalizer bands using the normalized frequency response. In a nineteenth one or more embodiments A19, which can include one or more of the embodiments A1-A18, wherein the graphical user interface is configured to display the normalized gain value for each of the plurality of equalizer bands prior to receiving the user input.

[0076] In a twentieth one or more embodiments A20, which can include one or more of the embodiments A1-A19, one or more non-transitory computer-readable media storing processor-executable instructions that, when executed by at least one processor, cause the at least one processor to: compute a percentile value of a normalized gain for each of a plurality of equalizer bands, wherein the normalized gain is based on a statistical specification of a distribution of gain values for each of the plurality of equalizer bands; receive a user input adjusting a gain value for at least one of the plurality of equalizer bands, wherein the user input is received from a graphical user interface that shows an adjustment value as the percentile value; adjust an audio output of an audio playback device based on the received user input; and cause the audio playback device to play the adjusted audio output.

[0077] Unless specifically stated otherwise as apparent from the foregoing disclosure, all measurements, values, ratings, positions, and / or other specifications that are not otherwise qualified are stated to be approximate, unless otherwise specified. The approximate nature of some aspects and / or parameters should be considered in the context of the state of the art and the common general knowledge.

[0078] As used herein, the phrase at least one of A, B, and C should be interpreted to mean the use of the inclusive logical "or" of non-exclusive logic (A or B or C), and should not be interpreted to mean "at least one of A, at least one of B, and at least one of C."

[0079] In this application, the terms "controller" and / or "module" can refer to, be part of, or include: an Application Specific Integrated Circuit (ASIC); a digital, analog, or mixed analog / digital discrete circuit; a digital, analog, or mixed analog / digital integrated circuit; a combinational logic circuit; a field programmable gate array (FPGA); a processor circuit (shared, dedicated, or group) that executes code; a memory circuit (shared, dedicated, or group) that stores code for execution by the processor circuit; other suitable hardware components that provide the described functionality (e.g., operational amplifier circuit integrator as part of a heat flux data module); or a combination of some or all of the above, as well as any combination of implementations of the technology described herein.

[0080] Embodiments of the disclosure are described in the general context of computer- executable instructions, such as program modules, being executed by one or more computers or other devices, software, firmware, hardware, or a combination thereof. In one example, the computer-executable instructions are organized into one or more computer-executable components or modules. Generally, program modules include, but are not limited to, routines, programs, objects, components, and data structures that perform particular tasks or implement particular abstract data types. In one example, aspects of the disclosure are implemented with any number and organization of such components or modules. For example, aspects of the disclosure are not limited to the specific computer-executable instructions or the specific components or modules illustrated in the figures and described herein. Other examples of the disclosure include different computer-executable instructions or components having a more or less similar functionality as compared with the computer-executable instructions or components illustrated and described herein. In embodiments involving a general-purpose computer, aspects of the disclosure transform the general-purpose computer into a special-purpose computing device when configured to execute the instructions described herein.

[0081] The term memory is a subset of the term computer-readable medium. As used herein, the term "computer-readable medium" does not encompass transitory propagating signals per se (such as waves, waves or other propagating electromagnetic signals — whether over wired or wireless networks); the term computer-readable medium thus is considered tangible and non-transitory. Non-limiting examples of non-transitory, tangible computer-readable media are nonvolatile memory circuits (such as flash memory circuits, erasable programmable read-only memory (EPROM) circuits, or mask read-only circuits), volatile memory circuits (such as static random access memory (SRAM) circuits or dynamic random access memory (DRAM) circuits), magnetic storage media (such as analog or digital magnetic tape or a hard disk drive), and optical storage media (such as CD, DVD, or Blu-ray discs).

[0082] The apparatus and methods described in this application can be partially or entirely implemented by special purpose computers created by configuring general purpose computers to execute one or more particular functions embodied in the computer program. The aforementioned functions, flowchart components, and other elements can be used by skilled programmers or programmers to create software programs that accomplish the desired functions of the disclosed apparatus.

[0083] Although described in connection with processor 214, examples of the present disclosure are capable of being implemented by many other general purpose or special purpose computing system environments, configurations or arrangements. Implementations of well-known computing systems, environments, and / or configurations suitable for use with aspects of the present disclosure include, but are not limited to, smartphones, mobile tablets, mobile computing devices, personal computers, server computers, hand-held or laptop devices, multiprocessor systems, game consoles, microprocessor-based systems, set top boxes, programmable consumer electronics, mobile telephones, mobile computing and / or communication devices in wearable or accessory form factors (e.g., watches, glasses, headsets, or earbuds), network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, VR devices, holographic devices, and the like. Such systems or devices accept input from users in any way, including from input devices such as keyboards or pointing devices, via gesture input, proximity input (such as hovering), and / or via voice input.

[0084] The description of the present disclosure is merely exemplary in nature and, thus, variations that do not depart from the essence of the present disclosure are intended to be within the scope of the present disclosure. Such variations are not to be regarded as a departure from the spirit and scope of the present disclosure.

Claims

1. A computer-implemented method comprising: computing percentile values for normalized gains of each of a plurality of equalizer bands, wherein the normalized gains are based on a statistical norm of a distribution of gain values for each of the plurality of equalizer bands; receiving user input to adjust a gain value of at least one of the plurality of equalizer bands, wherein the user input is received from a graphical user interface that shows adjustment values as the percentile values; adjusting an audio output of an audio playback device based on the received user input; and causing the audio playback device to play the adjusted audio output.

2. The computer-implemented method of claim 1, wherein the plurality of equalizer bands comprises a low frequency band, a mid frequency band, and a high frequency band, and the computer-implemented method further comprises determining normalized levels for the low frequency band, the mid frequency band, and the high frequency band by computing an average level of a frequency response error curve for each of the low frequency band, the mid frequency band, and the high frequency band and configuring a corresponding filter.

3. The computer-implemented method of claim 2, wherein the audio playback device comprises headphones, and the computer-implemented method further comprises computing a difference or error in frequency response between two headphones to generate the frequency response error curve.

4. The computer-implemented method of claim 2, wherein the graphical user interface comprises virtual controls corresponding to each of the low frequency band, the mid frequency band, and the high frequency band, wherein the virtual controls are configured to allow adjustment of the gain values displayed as the percentile values.

5. The computer-implemented method of claim 4, wherein the virtual controls comprise three virtual rotary controls or three virtual sliders corresponding to each of the low frequency band, the mid frequency band, and the high frequency band, wherein the virtual controls are color-coded with different colors to represent where the gains fall within the distribution of the statistical norm.

6. The computer-implemented method of claim 1, further comprising storing individualized data of a user to a database, wherein the individualized data defines a personal sound profile of the user and comprises gain settings, headphone models, frequency responses, programs, playback SPLs, demographic / hearing profiles, or combinations thereof.

7. The computer-implemented method of claim 6, further comprising automatically individualizing the gain values for a plurality of equalizer bands based on a model, the model generated by machine learning and configured to predict the personal sound profile of the user based on an individual profile, a demographic profile, a hearing profile, or combinations thereof.

8. The computer-implemented method of claim 6, further comprising computing and categorizing the personal sound profile according to a class and percentile corresponding to an adjusted gain received from the user.

9. The computer-implemented method of claim 1, further comprising calculating the value of the normalized gain for each of the plurality of equalizer bands as a percentile relative to a reference frequency response curve.

10. The computer-implemented method of claim 1, wherein the audio playback device comprises headphones, and computing the percentile value of the normalized gain for each of a plurality of equalizer bands comprises: normalizing the frequency response of the earphone using the reference frequency response and determining the normalized frequency response to determine the mean value of each of the plurality of equalizer bands.

11. The computer-implemented method of claim 1, wherein receiving the user input to adjust the gain value of at least one of the plurality of equalizer bands comprises receiving one or more adjustments corresponding to a standard test track.

12. The computer-implemented method of claim 1, further comprising, prior to receiving the user input, displaying, via the graphical user interface, the normalized gain value for each of the plurality of equalizer bands.

13. The computer-implemented method of claim 1, wherein the statistical specification is updated based on preferred settings of a plurality of listeners.

14. A system comprising: an audio playback device; a graphical user interface; a processor; and a non-transitory computer-readable storage medium storing instructions that, when executed by the processor, cause the processor to: calculate a percentile value of a normalized gain for each of a plurality of equalizer bands, wherein the normalized gain is based on a statistical specification of a distribution of gain values for each of the plurality of equalizer bands; receive user input to adjust a gain value of at least one of the plurality of equalizer bands, wherein the user input is received from the graphical user interface that shows the adjustment value as the percentile value; adjust an audio output of the audio playback device based on the received user input; and cause the audio playback device to play the adjusted audio output.

15. The system of claim 14, wherein the audio playback device comprises an earphone and the plurality of equalizer bands comprises a bass band, a midrange band, and a treble band, and the instructions further cause the processor to: determine normalized levels of the bass band, the midrange band, and the treble band by calculating a mean level of a frequency response error curve for each of the bass band, the midrange band, and the treble band and configuring a corresponding filter; and calculate a difference or error in frequency response between the earphone and another set of earphones to generate the frequency response error curve.

16. The system of claim 15, wherein the graphical user interface comprises a virtual control corresponding to each of the bass band, the midrange band, and the treble band, wherein the virtual control is configured to allow adjustment of the gain value displayed as the percentile value, wherein the virtual control comprises three virtual rotary controls or three virtual sliders corresponding to each of the bass band, the midrange band, and the treble band, and wherein the virtual control is color-coded with different colors to represent where the gain falls within the distribution of the statistical specification. ​ 17. The system of claim 14, wherein the instructions further cause the processor to store individualized data of a user to a database, wherein the individualized data defines a personal sound profile of the user and includes a gain setting, a headphone model, a frequency response, a program, a playback SPL, a crowd / hearing profile, or a combination thereof, and wherein the personal sound profile is calculated and categorized according to a category and percentile corresponding to an adjusted gain received from the user.

18. The system of claim 14, wherein the audio playback device comprises headphones, and the instructions further cause the processor to calculate the percentile value of the normalized gain for each of a plurality of equalizer bands, comprising: normalizing the frequency response of the headphone using a reference frequency response, and determining a mean value for each of the plurality of equalizer bands using the normalized frequency response.

19. The system of claim 14, wherein the graphical user interface is configured to display a normalized gain value for each of the plurality of equalizer bands prior to receiving the user input.

20. One or more non-transitory computer-readable media storing processor-executable instructions that, when executed by at least one processor, cause the at least one processor to: calculate a percentile value of a normalized gain for each of a plurality of equalizer bands, wherein the normalized gain is based on a statistical norm of a distribution of gain values for each of the plurality of equalizer bands; receive a user input adjusting a gain value of at least one of the plurality of equalizer bands, wherein the user input is received from a graphical user interface that shows an adjustment value as the percentile value; adjust an audio output of an audio playback device based on the received user input; and cause the audio playback device to play the adjusted audio output.

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