System and method for predicting fitting preferences for in-ear headphones

Abstract

In at least one embodiment, a system for predicting a user's fitting preference for an in-ear headphone is provided. The system includes an image detection device and at least one controller. The image detection device is programmed to capture at least one image of a user's ear. The at least one controller is programmed to detect one or more anatomical features on the at least one image of the user's ear and provide at least one selected in-ear headphone to the user based at least on the one or more anatomical features.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to Indian Application Serial No. 202241050800 filed on September 6, 2022, the disclosure of which is hereby incorporated by reference in its entirety. Technical Field

[0003] The present invention relates generally to in-ear headphones, and more particularly to predicting a user's fit preference for in-ear headphones. Background Art

[0004] True Wireless Stereo (TWS) devices, including in-ear headphones (or earbuds), are becoming an increasingly popular method of presenting music and speech to end consumers. Earbuds are compact and come in a variety of shapes and sizes. The anatomy of the ear plays an important role in determining consumer preference for earbuds. Currently, there is no method that addresses the evaluation of end-user perception of earbud comfort other than trying the earbuds on. The present subject matter addresses this and other limitations by predicting preferences through an image-based algorithm. Summary of the invention

[0005] The disclosed systems and methods can provide, but are not limited to, a list of in-ear headphones or earbud types that are considered most suitable for a user. These and other aspects will be described in more detail below.

[0006] In at least one embodiment, a system for predicting a user's fit preference for an in-ear headphone is provided. The system includes an image detection device and at least one controller. The image detection device is programmed to capture at least one image of a user's ear. The at least one controller is programmed to detect one or more anatomical features on the at least one image of the user's ear, and provide at least one selected in-ear headphone to the user based at least on the one or more anatomical features.

[0007] In at least another embodiment, a method for predicting a user's fit preference for an in-ear headphone is provided. The method includes receiving at least one image of a user's ear and detecting one or more anatomical features on the at least one image of the user's ear. The method also includes providing at least one selected in-ear headphone to the user based at least on the one or more anatomical features.

[0008] In at least another embodiment, a computer program product embodied in a non-transitory computer readable medium is provided, the computer program product being executable by at least one controller to predict a user's fit preference for an in-ear headphone. The computer program product includes instructions for receiving at least one image of a user's ear and detecting one or more anatomical features on the at least one image of the user's ear. The method includes providing at least one selected in-ear headphone to the user based at least on the one or more anatomical features. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Embodiments of the present disclosure are particularly pointed out in the appended claims. However, other features of the various embodiments will become more apparent and will be best understood by reference to the following detailed description taken in conjunction with the accompanying drawings, in which:

[0010] Figures 1A to 1F Examples of various earplugs showing different shapes and sizes are generally depicted;

[0011] Figure 2 The overall depiction of multiple human ears, each with different characteristics from the other;

[0012] Figure 3 Generally depicted is a system for predicting fit preferences for in-ear headphones or earbuds, according to one embodiment;

[0013] Figure 4 depicts an ear of a user and a corresponding ear plane according to one embodiment;

[0014] Figure 5 Generally depicted is a method for predicting fit preferences for in-ear headphones or earbuds according to one embodiment;

[0015] Figure 6 A more detailed block diagram of a blur and distortion filter according to one embodiment is generally depicted.

[0016] Figure 7 depicts a screenshot showing instructions for performing video capture according to one embodiment;

[0017] Figure 8 depicts another screen shot providing a prompt to initiate recording according to one embodiment;

[0018] Fig. 9 depicts another screenshot showing a video capture of a user's right ear according to one embodiment;

[0019] Fig.10 depicts another screenshot showing a video capture of a user's ears according to one embodiment;

[0020] Fig.11 depicts another screenshot showing a recommendation of in-ear headphones to a user according to one embodiment;

[0021] Fig.12 depicts another screenshot showing various in-ear headphones being evaluated in conjunction with a user's ear measurements, according to one embodiment; and

[0022] Fig.13 Depicted is another screenshot showing a user feedback screen according to one embodiment. DETAILED DESCRIPTION

[0023] Reference will now be made in detail to the embodiments, examples of which are shown in the accompanying drawings. In the following detailed description, many specific details are set forth to provide a comprehensive understanding of the various described embodiments. However, it will be apparent to those skilled in the art that the various described embodiments may be practiced without these specific details. In other examples, known methods, processes, components, circuits and networks are not described in detail to avoid unnecessarily obscuring the various aspects of the embodiments.

[0024] However, it should be understood that the disclosed embodiments are merely exemplary, and that various and alternative forms are possible. The drawings are not necessarily to scale; some features may be exaggerated or minimized to show details of particular components. Therefore, the specific structural details and functional details disclosed herein should not be interpreted as limiting, but merely as a representative basis for teaching those skilled in the art to adopt the embodiments according to the present disclosure in various ways.

[0025] “One or more” and / or “at least one” includes functions being performed by one element, functions being performed by more than one element (e.g., in a distributed manner), several functions being performed by one element, several functions being performed by several elements, or any combination of the above.

[0026] It should also be understood that, although the terms first, second, etc. are used in some examples herein to describe various elements, these elements should not be limited to these terms. These terms are only used to distinguish one element from another element. For example, without departing from the scope of the various described embodiments, the first contact may be referred to as the second contact, and similarly, the second contact may be referred to as the first contact. Both the first contact and the second contact are contacts, but not the same contact.

[0027] The terms used in the description of the various embodiments are only used for the purpose of describing specific embodiments and are not intended to be limiting. As used in the description of the various embodiments and the appended claims, unless the context clearly indicates otherwise, the singular forms "one", "a kind of" and "said" are also intended to include plural forms. It should also be understood that the term "and / or" as used herein refers to and covers any and all possible combinations of one or more of the associated listed items. It should also be understood that the terms "including" and / or "comprising" are used in this specification to specify the presence of the features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0028] As used herein, the term "if" is optionally interpreted to mean "at the time of," "after," or "in response to determining," or "in response to detecting," depending on the context. Similarly, the term "if it is determined" or "if [the condition or event] is detected" is optionally interpreted to mean "after determining," or "in response to determining," or "after detecting [the condition or event]," or "in response to detecting [the condition or event]," depending on the context.

[0029] Figures 1A to 1F Examples of various earplugs 100a-100f exhibiting different characteristics are generally depicted respectively. The various earplugs 100a-100f exhibit different characteristics, such as size, shape, and material, which may affect the overall comfort of the user when any of the earplugs 100a-100f is inserted into the user's ear. In general, the earplugs 100a-100f correspond to various examples of earplugs with different shapes and size specifications. It should be appreciated that the embodiments disclosed herein may recommend a specific earplug for any of the corresponding earplugs 100a-100f. In addition, the embodiments disclosed herein may recommend earplugs 100a-100f with specific size specifications, such as earplugs that are completely in the ear like a TWS device, such as but not limited to earplug 100c.

[0030] Users may have various preferences for the desired characteristics when wearing earplugs. Such preferences may include the ability of earplugs to stay in the ear canal for a long time, particularly when the user is exercising and sweating. Similarly, user preferences may include wearing earplugs comfortably for a long time. In many cases, the overall size and / or material of any one of the earplugs 100a-100f inserted into the ear canal and adjacent to the concha of the user's ear determines the user's comfort level. In addition, the overall size and / or material of any one of the earplugs 100a-100f determines the way in which the earplugs 100a-100f can remain fixed in the ear, particularly when the user is sweating during exercise or other activities. Additional non-restrictive preferences may also include fit, for example, earplugs do not feel pain when placed in the user's ears at the same time. As described above, earplugs are preferably not too loose, so as not to lack stability. Wearing inappropriately may also have a negative impact on the wearer's auditory experience.

[0031] Figure 2 A plurality of human ears 120a-120f are generally depicted, each ear having different features or variations from one another. In general, one or more of the earplugs 100a-100f can be inserted into the respective ear canals 122a-122f of the ears 120a-120f. The overall size and / or shape of the ear canals 122 of the respective ears 120a-120f (or the size of the opening defined by the walls of the ear canals 122) may vary from user to user. Similarly, each ear 120a-120f defines a concha 124, which includes a depression located in the inner ear, the depression leading to the initial opening of the ear canal 122. The size and shape of the concha 124 may also vary from user to user. When any one of the earplugs 100a-100f is inserted into any one of the ears 120a-120f, the fit or retention of any one of the earplugs 100a-100f in the concha 124 may also affect the comfort of the user. As can be readily seen, each earplug 100a-100f has various characteristics (eg, size, shape, and material) that may affect the comfort of a user when inserted into the corresponding ear 120a-120f.

[0032] As shown in Figure 1 and Figure 2As shown, the overall fit and comfort of any of the earplugs 100a-100f when inserted into any of the ears 120a-120f can be determined by the size, shape and material of the individual earplugs 100a-100f and / or the size and shape of the ear canal 122 and / or the concha 124. Although consumers may have many choices for earplugs 100a-100f, consumers may not have a mechanism to help identify which earplug is most suitable. Therefore, the present disclosure provides a system that can recommend the best earplug that is most favorable for each of their own preferences for their corresponding ear anatomy with a high probability. For hygiene reasons, if the user is not satisfied with the feel and fit of the earplug 100a-100f after purchase, the manufacturer of the earplug 100a-100f generally does not allow or does not want the user to return the earplug 100a-100f after purchase. Therefore, it may be advantageous to develop a system that determines or provides a user with a set of recommended earplugs based on anatomical aspects of the user's ear and further based on the size, shape, and material of the earplugs 100a-100f so as to provide the user with the best fit and comfort before the user purchases a specific earplug 100a-120f.

[0033] Figure 3 A system 200 for predicting or determining optimal earplugs 100a-100f for a user is generally depicted according to one embodiment. The system 200 generally includes an ear detection block 202, an ear region extraction block 204, a pose detection block 206, an image preprocessing block 208, a blur and distortion filter 210, a recommendation model block 212, an ear landmark detection block 213, and a recommendation refinement block 214. The system 200 can be implemented on an electronic device 201, such as a mobile device, a laptop, a tablet, or any other device including an image detection device (camera) 220 for capturing images of the user's ears 120a-120f. The system 200 includes at least one controller 230 (hereinafter referred to as "controller 230") located on the mobile device, laptop, tablet, etc. to perform any of the noted operations described herein in conjunction with the system 200. The system 200 can utilize, for example (but not limited to), a Fitchecker algorithm to determine or confirm a set of optimal earphones 100a-100f. At least one memory device 232 (hereinafter referred to as “memory device 232”) is coupled to the controller 230. At least one camera 234 (hereinafter referred to as “camera 234”) may be located on the electronic device 201 to capture images of objects outside the electronic device 201.

[0034] The ear detection block 202 receives images of the user's face and ears (i.e., left and right ears) from the camera 234. The ear detection block 202 utilizes a deep learning model to identify images of the right and left ears to create a bounding box around the left ear and a bounding box around the right ear. In one example, the bounding box typically corresponds to the coordinates of a rectangular boundary of a digital image that completely surrounds the left and right ears. The ear region extraction block 204 crops the image outside the bounding box to reduce the processing load, thereby processing a smaller image. Since multiple images of the left and right ears can be provided to the system 200, the posture detection block 206 identifies the best image of the ear (e.g., left and right ears). The system 200 selects the best image based on the area of ​​the bounding box and the area of ​​the ear located in the bounding box.

[0035] Figure 4 The anatomical structure of the user's ear 233 and the corresponding contours that produce the best image are generally shown. In general, the best image of the ear 233 generally corresponds to the image captured when the camera 234 is located in front of the ear 233 and generally perpendicular to the ear plane 235 on the side of the user's head. In this case, the controller 230 is able to fully capture and characterize the anatomical structure of the user's ear 233. The ear plane 235 is generally positioned parallel to the user's ear 233 and is positioned axially spaced from the ear 233 and forms an angle α relative to the user's nose tip, as shown in FIG. Figure 4 Generally speaking, the angle α may correspond to an angle greater than 90 degrees and less than 180 degrees.

[0036] Reference again Figure 3 , the gesture detection block 206 distinguishes between the left ear and the right ear, and monitors the aspect ratio of the bounding box of the left ear and the bounding box of the right ear. The aspect ratio of the bounding box of the left ear and the bounding box of the right ear can be used as a reliable or advantageous metric to evaluate the effectiveness of utilizing such an image before providing a given image as an input to the recommendation model block 212. The aspect ratio generally corresponds to the ratio of the width to the height of the image. As described above, an ear image taken at a certain angle will produce a perspective tilt. This may cause the bounding box to be too wide or too long. Therefore, in this regard, the aspect ratio of the bounding box within an acceptable range (or a predetermined range) of the ear anatomy can be considered optimal. In other words, an aspect ratio within a bounded range indicates an acceptable image. In addition, consider the case where the user's face is turned from the front to the side to make the ear more visible. In this case, the aspect ratio also increases, thereby producing a peak. Therefore, by monitoring the aspect ratio, the controller 230 can be used to infer (or determine) whether the ear area of ​​the target person is captured from the front or the side of the face.

[0037] The image preprocessing block 208 performs image normalization to compensate for noise, lighting, and other artifacts present in the bounding box of the left ear and the bounding box of the right ear. Image normalization generally includes a process of changing the range of pixel intensity values. For example, the image preprocessing block 208 may perform contrast normalization and brightness correction. The linear normalization of a grayscale digital image is generally defined by the following formula:

[0038] I N =(I-Min)*(newMax-newMin) / (Max-Min)+newMin

[0039] where IN corresponds to a new image with {newMin,...,newMax} whose intensity values ​​are in the range (newMin,newMax).

[0040] The blur and distortion filter (or bilateral filter) 210 detects images that may be poorly formed due to camera or subject (or user) motion or video compression-related artifacts. In general, images that are not well-formed are not considered. In general, the system 200 looks for candidate images with the least blur in the temporal proximity of the image to identify the preferred image. Since the video is captured by the camera 234 while the user is moving, blurred images may be attributed to motion and may be included in the input. Among these images, the model calculates the amount of blur to select predictable images. Therefore, among the collected inputs, images with less blur effects are selected as inputs, and if this is not possible, the user is requested to retake the image. In the context of the algorithm performed by the blur and distortion filter 210, this algorithm reduces image noise based on the values ​​of the surrounding pixels of the target pixel. Subsequently, the blur and distortion filter 210 also performs, for example, a CLAHE algorithm to equalize the histogram. This produces a high-contrast image. Subsequently, the blur and distortion filter 210 performs an edge detection algorithm on the image, and this algorithm exploits the lack of clarity at the edges of the blurred image. The blur and distortion filter 210 may then average this value to calculate an overall blur value for the image.

[0041] The recommendation model block 212 may be implemented as a deep learning model to provide a recommendation list corresponding to any one or more of the earplugs 100a-100f that can provide the best fit and comfort based on the shape, size, and material of such earplugs 100a-100f and also based on the anatomical features of the user's ear (e.g., the size / shape of the ear canal 122 and the concha 124, etc.). Generally speaking, the input provided to the recommendation model block 212 may need to be a clear image (e.g., an image without blur and / or distortion). Therefore, in this regard, the blur and distortion filter 210 provides such a clear image.

[0042] The ear landmark detection block 213 generally detects various anatomical landmark features of images of the user's left and right ears. For example, the ear landmark detection block 213 provides estimates of key feature points that define the geometry of the ear. Such points (or anatomical points) may include the helix, superior crus, triangular fossa, inferior crus, cymba concha (or concha), tragus, external auditory canal, cavum concha (or concha), earlobe, antitragus, antihelix, and / or scaphoid. The recommendation model block 212 may utilize a conventional neural network (CNN) as a deep learning model. The recommendation model block 212 and its corresponding deep learning model are executed by the controller 230. For example, when multiple images are provided to the recommendation block 212, the controller 230 may, for example, execute the recommendation model block 212 once per input image. The recommendation refinement block 214 refines the recommendation output provided by the recommendation model block 212. For example, the recommendation refinement block 214 provides intelligence to refine different recommendations to provide a final result (or recommended earplug). The system 200 is arranged to enhance accuracy by capturing multiple images of the user's ear region, predicting a probability value from each image, and combining the images using a soft voting algorithm. For example, the soft voting algorithm can be defined by the following equation:

[0043]

[0044] Briefly, soft voting algorithms typically involve averaging various predictions for multiple images. Other methods such as hard voting (selecting the prediction class with the most frequent top-ranked predictions) are also available. As shown in the equation above, the predicted scores can correspond to the predicted probability values ​​for each image as described above, where these values ​​are subtracted from a threshold and divided by the threshold in the manner shown in the equation above. The method used by the recommendation refinement block 214 can change based on the situation. Therefore, it can be appreciated that the recommendation model block 212 can perform other algorithms that may not only involve soft voting. For example, in another embodiment, the recommendation model block 212 can adopt (or perform) a hard voting algorithm.

[0045] Generally, the controller 230 of the electronic device 201 may be a central processing unit (CPU), such as, for example, an Intel / AMD X86 or ARM microprocessor. It will be appreciated that cloud or wireless access may or may not be required to execute one or more aspects of the system 200.

[0046] Figure 5A method 300 for providing a set of recommended earplugs 100a-100f according to one embodiment is generally depicted. In operation 302, the controller 230 receives images of a user's face and ears (i.e., left and right ears). In operation 304, the controller 230 uses a deep learning model to recognize the images of the right ear and the left ear to create a bounding box around the left ear and a bounding box around the right ear. In operation 306, the controller 230 crops the image outside the bounding box to reduce the processing load, thereby processing a smaller image.

[0047] In operation 308, the controller 230 identifies the best image of the ear (e.g., the left ear and the right ear) since multiple images of the left ear and the right ear may be provided to the system 200. In operation 310, the controller 230 distinguishes the left ear from the right ear and monitors the aspect ratio of the bounding box of the left ear and the bounding box of the right ear. In operation 312, the controller 230 performs image normalization to compensate for noise, lighting, and other artifacts present in the bounding box of the left ear and the bounding box of the right ear.

[0048] In operation 314, the controller 230 detects that a poor image may be formed due to camera or subject (or user) motion or video compression related artifacts. In operation 316, the controller 230 provides a recommendation list corresponding to any one or more of the earplugs 100a-100f that can provide the best fit and comfort based on the shape, size and material of such earplugs 100a-100f and also based on the anatomical features of the user's ear (e.g., the size / shape of the ear canal 122 and / or the concha 124, etc.).

[0049] Figure 6A more detailed block diagram of the blur and distortion filter 210 according to one embodiment is generally depicted. The blur and distortion filter 210 includes an array block 400, a grayscale image block 402, a denoising block 404, a histogram equalization block 406, an edge detection block 408, an average block 410, a scale-invariant feature transform (SIFT) block 412, and a calculation similarity block 414. The array block 400 receives images of the left ear and the right ear in the form of an image array. The grayscale image block 402 generates a grayscale image of the images in the array. In one example, the grayscale image can be a grayscale image, where each pixel representing the image corresponds to a single sample representing the amount of light (or intensity information). The denoising block 404 can remove noise from the signal (or grayscale image). The histogram equalization block 406 can use image processing techniques to improve the contrast of the image. The edge detection block 408 can identify edges and / or curves in the received digital image, where the image brightness may change sharply or the form has discontinuities. The average block 410 enhances the video image that may be corrupted by random noise and provides blur values. The SIFT block 412 can detect and describe local features in the image (e.g., local aspects of the anatomical features of the user's ears 120a-120e). The calculate similarity block 41 can perform at least the following calculations to provide a clear image.

[0050]

[0051] N: number of images, i: i th image

[0052]

[0053] Figure 7 A screenshot 500 showing instructions for performing video capture of a user is depicted according to one embodiment. The electronic device 201 generally includes a user interface 502 having a display 504 that provides the screenshot 500 to the user. The user can control the electronic device 201 so that the controller 230 executes instructions to run an application to perform a video capture of the user in conjunction with the user. Figure 5 Method 300 shown. In this case, display 504 provides instructions to the user to help obtain a video (or image) capture / recording of the user's ear. For example, display 504 can guide the user to obtain a video recording in an area that provides adequate lighting and a clear background. Similarly, the user can be instructed not to move while the video is being recorded. Generally speaking, the user can sit in a line of sight relative to camera 234. The user can turn his head to the left or right and then to the other side at a predetermined rate or speed. Screen shot 500 provides a visual example of how the user turns left and right.

[0054] Figure 8Another screenshot 520 providing a prompt to start recording is depicted according to one embodiment. The display 504 provides instructions to the user to select a record button to record video from the camera 234. Fig. 9 Another screenshot 540 is depicted, which shows a video capture of the user's right ear on the display 504 of the user interface 502 according to one embodiment. The screenshot 540 shows the aspect ratio of the bounding box and the confidence value of the measurement. It can be appreciated that the screenshot 540 may or may not include the aspect ratio and / or the confidence value of the measurement. Fig.10 Another screenshot 560 is depicted, which shows a video capture of the user's left ear on the display 504 of the user interface 502 according to one embodiment. Similarly, the screenshot 560 shows the aspect ratio of the bounding box and the confidence value of the measurement. It can be appreciated that the screenshot 560 may or may not include the aspect ratio and / or the confidence value of the measurement.

[0055] Fig.11 Another screenshot 580 is depicted, which shows the earplugs 100n and 100m recommended to the user according to one embodiment. Thus, in this regard, the system 200 or 300 can use the captured images of the right ear and the left ear to determine the recommendation of the earplugs 100n and 100m that can provide the best fit and comfort for the earplugs based on the anatomical features of the user's ears captured on the images. The earplugs 100n-100m will be presented in order of preference. In this example, the display 504 may indicate JJBL TUNE or JBL LIVE The system 200 and / or 300 can help the user select an optimal set of earplugs, thereby avoiding the situation where the user purchases earplugs and then finds that the purchased earplugs may not be compatible with the anatomical features of the user's ears and avoids the situation where the earplugs must be returned.

[0056] Fig.12 Another screenshot 600 is depicted showing various earplugs 100 on the display 504 of the user interface 502 for evaluation in conjunction with ear measurements of a user according to one embodiment. In general, the memory device 232 can store any number of earplugs 100 to evaluate the compatibility or comfort of the earplugs based on the anatomical layout of the user's ear. It can be appreciated that the memory device 232 can be continuously or periodically updated with new earplugs via wireless communication with other electronic devices (e.g., a server, etc.). Fig.13Another screenshot 610 is depicted, which shows a user feedback screen on the display 504 of the user interface 502 according to one embodiment. The screenshot 610 enables the user to answer questions related to the earplug recommendation / evaluation. For example, the user can answer questions asking whether the user agrees with the recommendation, whether the order of preference is incorrect, whether one or more earplugs that the user may prefer are missing from the list, or whether the preferred earplugs 100m-100n are not recommended in the screenshot 580.

[0057] If the user indicates that the system 200 or 300 has not yet considered a particular earbud, the electronic device 201 can electronically communicate with an external server (not shown) and obtain information related to the requested device and provide a suggestion after obtaining corresponding information related to the requested earbud.

[0058] It should be appreciated that the controller as disclosed herein may include various microprocessors, integrated circuits, memory devices (e.g., flash memory, random access memory (RAM), read-only memory (ROM), electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or other suitable variations thereof) and software that cooperate with each other to perform the operations disclosed herein. In addition, such controllers as disclosed utilize one or more microprocessors to execute computer programs embodied in non-transitory computer-readable media that are programmed to perform any number of functions as disclosed. In addition, the controller as provided herein includes a housing and various numbers of microprocessors, integrated circuits, and memory devices (e.g., FLASH, random access memory (RAM), read-only memory (ROM), electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM)) positioned within the housing. The disclosed one or more controllers also include hardware-based inputs and outputs, which are used to receive data from and transmit data to other hardware-based devices as discussed herein, respectively.

[0059] Although exemplary embodiments are described above, it is not intended that these embodiments describe all possible forms of the present invention. In fact, the wording used in the specification is for description rather than limitation, and it should be understood that various changes can be made without departing from the spirit and scope of the present invention. In addition, the features of various exemplified embodiments can be combined to form further embodiments of the present invention.

Claims

1. A system for predicting a user's fitting preference for an in-ear headphone, the system comprising: an image detection device programmed to capture at least one image of at least one user's ear; at least one controller programmed to: detecting one or more anatomical features on the at least one image of the at least one user's ear; and At least one selected in-ear headphone is provided to the user based at least on the one or more anatomical features.

2. The system of claim 1 , wherein the at least one controller is further programmed to utilize a machine learning algorithm to provide the user with at least one selected in-ear headphone based at least on the one or more anatomical features.

3. The system of claim 2, wherein the at least one controller is further programmed to position a bounding box around the first image of the user's right ear and the second image of the user's left ear. 4 . The system of claim 3 , wherein the at least one controller is further programmed to crop portions of the first image and the second image that are outside of the bounding box to reduce a processing load of the at least one controller.

5. The system of claim 3, wherein the bounding box corresponds to coordinates of a rectangular boundary surrounding the first image of the user's right ear and the second image of the user's left ear.

6. The system of claim 4, wherein the at least one controller is further programmed to monitor an aspect ratio of the bounding box positioned around the first image of the user's right ear and the bounding box positioned around the second image of the user's left ear to determine reliability of the first image and the second image.

7. The system of claim 6, wherein the at least one controller is further programmed to perform image normalization on at least the first image of the right ear of the user and at least the second image of the left ear of the user to compensate for at least one of noise, lighting, and artifacts present in the bounding box of the right ear of the user and the bounding box of the left ear of the user.

8. The system of claim 3, wherein the at least one controller is further programmed to detect one or more of a helix, superior crus, triangular fossa, inferior crus, cymba concha, tragus, external auditory canal, cavum concha, earlobe, anti-tragus, anti-helix, and scaphoid of the at least one user's ear, and to provide an output indicating a recommendation for the at least one selected in-ear headphone to the user after positioning the bounding box around the first image of the user's right ear and the second image of the user's left ear.

9. The system of claim 1, wherein the image detection device and the at least one controller are implemented on one of a mobile device, a laptop, and a tablet.

10. A method for predicting a user's preference for fitting an in-ear headphone, the method comprising: receiving at least one image of an ear of a user; detecting one or more anatomical features on the at least one image of the user's ear; and At least one selected in-ear headphone is provided to the user based at least on the one or more anatomical features.

11. The method of claim 10, wherein providing the at least one selected in-ear headphone to the user based at least on the one or more anatomical features comprises providing the at least one selected in-ear headphone to the user based at least on the one or more anatomical features using a machine learning algorithm.

12. The method of claim 11, further comprising positioning a bounding box around the first image of the user's right ear and the second image of the user's left ear. 13 . The method of claim 12 , further comprising cropping portions of the first image and the second image that are outside of the bounding box to reduce a processing load of at least one controller.

14. The method of claim 13, wherein the bounding box corresponds to coordinates of a rectangular boundary surrounding the first image of the user's right ear and the second image of the user's left ear.

15. The method according to claim 13 further includes monitoring the aspect ratio of the bounding box positioned around the first image of the user's right ear and the bounding box positioned around the second image of the user's left ear to determine the reliability of the first image and the second image.

16. The method of claim 15, further comprising performing image normalization on at least the first image of the right ear of the user and at least the second image of the left ear of the user to compensate for at least one of noise, illumination, and artifacts present in the bounding box of the right ear of the user and the bounding box of the left ear of the user.

17. The method of claim 13, further comprising detecting one or more of a helix, superior crus, triangular fossa, inferior crus, cymba concha, tragus, external auditory canal, cavum concha, earlobe, anti-tragus, anti-helix, and scaphoid of the user's ear, and providing an output indicating a recommendation for the at least one selected in-ear headphone to the user after positioning the bounding box around the first image of the user's right ear and the second image of the user's left ear.

18. A computer program product embodied in a non-transitory computer readable medium, the computer program product stored in a memory and executable by at least one controller to predict a user's fit preference for an in-ear headphone, the computer program product comprising instructions for: receiving at least one image of an ear of a user; detecting one or more anatomical features on the at least one image of the user's ear; as well as At least one selected in-ear headphone is provided to the user based at least on the one or more anatomical features.

19. A computer program product according to claim 18, wherein providing the at least one selected in-ear headphone to the user based at least on the one or more anatomical features includes utilizing a machine learning algorithm to provide the at least one selected in-ear headphone to the user based at least on the one or more anatomical features.

20. The computer program product of claim 18, further comprising positioning a bounding box around the first image of the user's right ear and the second image of the user's left ear.

Images