EMOTION-DRIVEN MUSIC RECOMMENDATION SYSTEM AND METHOD THEREOF

Abstract

Disclosed herein is a emotion-driven music recommendation system and method thereof (100) that comprises a processing unit (102) configured to control and manage the overall operation of the system, wherein the processing unit (102) includes; an emotion detection module (104), a recommendation module (106), a feedback module (108), a memory unit (110). The system (100) includes the image capturing unit (112), operatively connected to the processing unit (102), a music database (114), operatively connected to the processing unit (102), an audio output unit (116), operatively connected to the processing unit (102), configured to play the recommended music, a communication network (118), operatively connected to the processing unit (102), configured to facilitate the transmission of user data and music data between the system components, a user device (120) configured to allow user interaction with the system through an integrated user interface (122).

Specification

Description:FIELD OF DISCLOSURE
[0001] The present disclosure relates generally relates to music recommendation systems, more specifically, relates to an emotion-driven music recommendation system and method thereof.
BACKGROUND OF THE DISCLOSURE
[0002] One of the primary advantages of this invention is its ability to create a personalized experience by dynamically recommending music based on the user's emotional state. This level of customization ensures that users are consistently engaged and satisfied, as the system intuitively matches their emotional needs with the most appropriate content.
[0003] This invention stands out by offering real-time adaptation to the user's emotions, ensuring that the content being recommended is aligned with their mood at that specific moment. This immediacy enhances user interaction and responsiveness, creating a sense of connection between the system and the user, which can be difficult to achieve with traditional content recommendation systems.
[0004] Another major advantage is the invention's versatility. It can be applied across various contexts, such as entertainment, mental well-being, or even in therapeutic settings. Whether the user is looking for relaxation, focus, or a mood boost, the system provides tailored music recommendations to meet diverse emotional requirements.
[0005] Many existing systems lack the ability to adapt in real-time to the user's changing emotions. Most content recommendation platforms rely on historical data or preferences without factoring in the user's current emotional state, making them less effective in creating an immediate, personalized experience.
[0006] Traditional music recommendation systems often require users to manually input their preferences, moods, or desired outcomes. This creates a barrier to seamless interaction and does not cater to users who may not know what content they want, thereby limiting the overall user experience.
[0007] Some existing solutions that attempt mood-based recommendations often rely on inaccurate or incomplete data, leading to mismatched recommendations. These systems struggle to analyse emotions accurately, resulting in recommendations that fail to resonate with users' actual feelings at a given moment, thus reducing user satisfaction.
[0008] Thus, in light of the above-stated discussion, there exists a need for an emotion-driven music recommendation system and method thereof.
SUMMARY OF THE DISCLOSURE
[0009] The following is a summary description of illustrative embodiments of the invention. It is provided as a preface to assist those skilled in the art to more rapidly assimilate the detailed design discussion which ensues and is not intended in any way to limit the scope of the claims which are appended hereto in order to particularly point out the invention.
[0010] According to illustrative embodiments, the present disclosure focuses on an emotion-driven music recommendation system and method thereof which overcomes the above-mentioned disadvantages or provide the users with a useful or commercial choice.
[0011] An objective of the present disclosure is to provide a system capable of recommending music based on the real-time analysis of users' emotional states, ensuring a personalized experience based on users' moods.
[0012] Another objective of the present disclosure is to enhance user engagement by allowing the system to dynamically adapt to changes in emotional states, offering recommendations that resonate with users in the moment.
[0013] Another objective of the present disclosure is to simplify the interaction process for users, allowing them to experience seamless music recommendations without the need for manual input or emotional self-reporting.
[0014] Another objective of the present disclosure is to integrate advanced machine learning algorithms, ensuring that the system evolves and improves its recommendations based on user preferences and emotional patterns over time.
[0015] Another objective of the present disclosure is to improve the accuracy of mood detection using facial expression recognition and other biometric data, ensuring that the recommended music aligns closely with the user's current emotional state.
[0016] Another objective of the present disclosure is to enable interoperability with various music streaming platforms, allowing users to access and play music from a wide range of libraries seamlessly through the system.
[0017] Another objective of the present disclosure is to provide a user-friendly interface that displays real-time feedback on emotional analysis, helping users understand the system's decision-making process in recommending specific music tracks.
[0018] Another objective of the present disclosure is to ensure privacy and security of the user's emotional and biometric data, implementing encryption and secure storage mechanisms to protect personal information.
[0019] Another objective of the present disclosure is to facilitate the integration of additional emotional cues such as voice analysis, providing a more comprehensive understanding of the user's emotional state for improved music recommendations.
[0020] Yet another objective of the present disclosure is to cater to users with diverse musical preferences and emotional ranges, ensuring the system's recommendations are inclusive and adaptable to different emotional landscapes.
[0021] In light of the above, in one aspect of the present disclosure, an emotion-driven music recommendation system is disclosed herein. The system comprises a processing unit configured to control and manage the overall operation of the system, wherein the processing unit includes; an emotion detection module configured to detect the user's emotional state based on real-time analysis of facial expressions captured by an image capturing unit, a recommendation module configured to suggest music tracks based on the detected emotion by cross-referencing the user's emotional state with metadata stored in a music database, a feedback module configured to collect user interaction data and continuously improve the accuracy of the emotion detection and music recommendation algorithms, a memory unit configured to store user preferences, interaction history, and emotional states over time to enhance future recommendations. The system includes the image capturing unit, operatively connected to the processing unit, configured to capture real-time images of the user's facial expressions. The system also includes a music database, operatively connected to the processing unit, configured to store a plurality of music tracks with associated metadata, including emotion-based categorization. The system also includes an audio output unit, operatively connected to the processing unit, configured to play the recommended music. The system also includes a communication network, operatively connected to the processing unit, configured to facilitate the transmission of user data and music data between the system components. The system also includes a user device configured to allow user interaction with the system through an integrated user interface, wherein the user device is operatively connected to the processing unit via the communication network and provides access to the emotion detection and music recommendation functionalities.
[0022] In one embodiment, the image capturing unit is configured to capture high-definition images to improve the precision of the emotion detection module.
[0023] In one embodiment, the emotion detection module uses convolutional neural networks (CNN) for detecting emotions by analysing facial landmarks and other features.
[0024] In one embodiment, the recommendation module is configured to analyse the user's historical emotional states and listening habits, generating personalized music playlists that match the user's mood.
[0025] In one embodiment, the user device displays the detected emotion in real-time, providing feedback to the user regarding the system's interpretation of their emotional state and the recommended music.
[0026] In one embodiment, the memory unit stores user data, including emotional responses to previously recommended tracks, ensuring that the recommendation module is continuously optimized to suit the user's preferences.
[0027] In one embodiment, the feedback module adapts the system's music recommendation algorithms based on real-time user interactions, thereby enhancing the relevance of the music suggestions provided.
[0028] In one embodiment, the audio output unit dynamically adjusts playback parameters such as volume or tempo, based on the user's emotional state, providing a tailored listening experience.
[0029] In light of the above, in one aspect of the present disclosure, an emotion-driven music recommendation method is disclosed herein. The method comprises capturing real-time images of the user's facial expressions using an image capturing unit. The method includes processing the captured images using an emotion detection module integrated within a processing unit, wherein the emotion detection module analyses the facial expressions to detect the user's emotional state. The method also includes storing user data including emotional states and interaction history in a memory unit operatively connected to the processing unit, wherein the memory unit stores previous user preferences and emotional responses to recommended music. The method also includes suggesting music tracks to the user using a recommendation module, wherein the recommendation module cross-references the detected emotion with a music database, selecting music based on emotion-based categorization. The method also includes playing the recommended music via an audio output unit, wherein the audio output unit dynamically adjusts playback parameters such as volume and tempo based on the detected emotional state. The method also includes collecting user interaction data through a feedback module integrated within the processing unit, wherein the feedback module improves the accuracy of future recommendations by adjusting algorithms based on real-time feedback. The method also includes displaying the detected emotional state and suggested music on a user device with an integrated user interface, providing real-time feedback to the user regarding the system's interpretation of the emotion and the selected music tracks.
[0030] In one embodiment, the method further comprises analysing the user's emotional trends over time through the memory unit, wherein the system dynamically updates the emotional baseline of the user, enabling the recommendation module to proactively suggest mood-enhancing or mood-stabilizing music tracks based on detected emotional fluctuations.
[0031] These and other advantages will be apparent from the present application of the embodiments described herein.
[0032] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
[0033] These elements, together with the other aspects of the present disclosure and various features are pointed out with particularity in the claims annexed hereto and form a part of the present disclosure. For a better understanding of the present disclosure, its operating advantages, and the specified object attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated exemplary embodiments of the present disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0034] To describe the technical solutions in the embodiments of the present disclosure or in the prior art more clearly, the following briefly describes the accompanying drawings required for describing the embodiments or the prior art. Apparently, the accompanying drawings in the following description merely show some embodiments of the present disclosure, and a person of ordinary skill in the art can derive other implementations from these accompanying drawings without creative efforts. All of the embodiments or the implementations shall fall within the protection scope of the present disclosure.
[0035] The advantages and features of the present disclosure will become better understood with reference to the following detailed description taken in conjunction with the accompanying drawing, in which:
[0036] FIG. 1 illustrates a block diagram of an emotion-driven music recommendation system, in accordance with an exemplary embodiment of the present disclosure;
[0037] FIG. 2 illustrates a process flow for an emotion-driven music recommendation system, in accordance with an exemplary embodiment of the present disclosure;
[0038] FIG. 3 illustrates a flowchart of an emotion-driven music recommendation system, in accordance with an exemplary embodiment of the present disclosure;
[0039] FIG. 4 illustrates a flowchart of an emotion-driven music recommendation method, in accordance with an exemplary embodiment of the present disclosure.
[0040] Like reference, numerals refer to like parts throughout the description of several views of the drawing.
[0041] The emotion-driven music recommendation system and method thereof is illustrated in the accompanying drawings, which like reference letters indicate corresponding parts in the various figures. It should be noted that the accompanying figure is intended to present illustrations of exemplary embodiments of the present disclosure. This figure is not intended to limit the scope of the present disclosure. It should also be noted that the accompanying figure is not necessarily drawn to scale.
DETAILED DESCRIPTION OF THE DISCLOSURE
[0042] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure.
[0043] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be apparent to one skilled in the art that embodiments of the present disclosure may be practiced without some of these specific details.
[0044] Various terms as used herein are shown below. To the extent a term is used, it should be given the broadest definition persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.
[0045] The terms "a" and "an" herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items.
[0046] The terms "having", "comprising", "including", and variations thereof signify the presence of a component.
[0047] Referring now to FIG. 1 to FIG. 4 to describe various exemplary embodiments of the present disclosure. FIG. 1 illustrates a block diagram of an emotion-driven music recommendation system 100, in accordance with an exemplary embodiment of the present disclosure.
[0048] The system 100 may include a processing unit 102 configured to control and manage the overall operation of the system, wherein the processing unit 102 includes; an emotion detection module 104 configured to detect the user's emotional state based on real-time analysis of facial expressions captured by an image capturing unit 112, a recommendation module 106 configured to suggest music tracks based on the detected emotion by cross-referencing the user's emotional state with metadata stored in a music database 114, a feedback module 108 configured to collect user interaction data and continuously improve the accuracy of the emotion detection and music recommendation algorithms, a memory unit 110 configured to store user preferences, interaction history, and emotional states over time to enhance future recommendations. The system 100 may also include the image capturing unit 112, operatively connected to the processing unit 102, configured to capture real-time images of the user's facial expressions.
[0049] The system 100 may also include a music database 114, operatively connected to the processing unit 102, configured to store a plurality of music tracks with associated metadata, including emotion-based categorization. The system 100 may also include an audio output unit 116, operatively connected to the processing unit 102, configured to play the recommended music. The system 100 may also include a communication network 118, operatively connected to the processing unit 102, configured to facilitate the transmission of user data and music data between the system components. The system 100 may also include a user device 120 configured to allow user interaction with the system through an integrated user interface 122, wherein the user device 120 is operatively connected to the processing unit 102 via the communication network 118 and provides access to the emotion detection and music recommendation functionalities.
[0050] The image capturing unit 112 is configured to capture high-definition images to improve the precision of the emotion detection module 104.
[0051] The emotion detection module 104 uses convolutional neural networks (CNN) for detecting emotions by analysing facial landmarks and other features.
[0052] The recommendation module 106 is configured to analyse the user's historical emotional states and listening habits, generating personalized music playlists that match the user's mood.
[0053] The user device 120 displays the detected emotion in real-time, providing feedback to the user regarding the system's interpretation of their emotional state and the recommended music.
[0054] The memory unit 110 stores user data, including emotional responses to previously recommended tracks, ensuring that the recommendation module 106 is continuously optimized to suit the user's preferences.
[0055] The feedback module 108 adapts the system's music recommendation algorithms based on real-time user interactions, thereby enhancing the relevance of the music suggestions provided.
[0056] The audio output unit 116 dynamically adjusts playback parameters such as volume or tempo, based on the user's emotional state, providing a tailored listening experience.
[0057] The method 100 may include capturing real-time images of the user's facial expressions using an image capturing unit 112. The method 100 may also include processing the captured images using an emotion detection module 104 integrated within a processing unit 102, wherein the emotion detection module 104 analyses the facial expressions to detect the user's emotional state. The method 100 may also include storing user data including emotional states and interaction history in a memory unit 110 operatively connected to the processing unit 102, wherein the memory unit 110 stores previous user preferences and emotional responses to recommended music. The method 100 may also include suggesting music tracks to the user using a recommendation module 106, wherein the recommendation module 106 cross-references the detected emotion with a music database 114, selecting music based on emotion-based categorization. The method 100 may also include playing the recommended music via an audio output unit 116, wherein the audio output unit 116 dynamically adjusts playback parameters such as volume and tempo based on the detected emotional state. The method 100 may also include collecting user interaction data through a feedback module 108 integrated within the processing unit 102, wherein the feedback module 108 improves the accuracy of future recommendations by adjusting algorithms based on real-time feedback. The method 100 may also include displaying the detected emotional state and suggested music on a user device 120 with an integrated user interface 122, providing real-time feedback to the user regarding the system's interpretation of the emotion and the selected music tracks.
[0058] The method 100 further comprises analysing the user's emotional trends over time through the memory unit 110, wherein the system 100 dynamically updates the emotional baseline of the user, enabling the recommendation module 106 to proactively suggest mood-enhancing or mood-stabilizing music tracks based on detected emotional fluctuations.
[0059] The processing unit 102 controls and manages all aspects of the emotion-driven music recommendation system 100, ensuring each component works in harmony to deliver an accurate, responsive user experience. The processing unit 102 serves as the central control hub, processing all input data from other components like the image capturing unit 112, the emotion detection module 104, and the recommendation module 106. The processing unit 102 coordinates data flow, ensuring real-time emotion detection and music recommendation by processing the facial expression data from the image capturing unit 112 and cross-referencing it with the music database 114. Moreover, the processing unit 102 is responsible for managing stored user data from the memory unit 110 and integrating it into the decision-making process to enhance personalized recommendations. The processing unit 102 also communicates with the feedback module 108 to update system algorithms based on user interactions, ensuring continuous improvement in system performance. The processing unit 102 thus plays a critical role, synchronizing all elements to provide a seamless user experience, from emotion detection to music playback.
[0060] The emotion detection module 104 detects the user's emotional state by analysing real-time facial expressions captured by the image capturing unit 112. The emotion detection module 104 uses convolutional neural networks (CNN) to recognize key facial landmarks, such as eyebrow position, mouth shape, and eye openness, which are indicators of emotions like happiness, sadness, anger, or surprise. The emotion detection module 104 receives input from the image capturing unit 112 and processes this input continuously, ensuring the system is updated with the user's current emotional state. The emotion detection module 104 is vital because it serves as the foundation for determining which music recommendations will be made by the recommendation module 106. By analysing emotions accurately, the emotion detection module 104 ensures the system adapts to the user's real-time mood, making the music suggestions more relevant and personalized.
[0061] The recommendation module 106 processes the emotional data provided by the emotion detection module 104 to suggest appropriate music tracks from the music database 114. The recommendation module 106 compares the user's emotional state with metadata stored in the music database 114, which classifies songs based on their mood, genre, and other attributes. The recommendation module 106 not only suggests music tracks based on the current emotional state but also considers the user's historical listening habits and emotional patterns stored in the memory unit 110. This allows the recommendation module 106 to generate personalized music playlists that cater to both the user's immediate emotional state and long-term preferences. By continuously analysing the user's interactions and adjusting its suggestions accordingly, the recommendation module 106 enhances the system's ability to provide a dynamic and engaging listening experience.
[0062] The feedback module 108 collects real-time data on user interactions and reactions to the recommended music tracks, allowing the system to refine its algorithms over time. The feedback module 108 analyses whether the music recommendations matched the user's emotional state, how the user responded to the suggested tracks, and whether any adjustments are needed for future recommendations. By interacting directly with the processing unit 102 and the recommendation module 106, the feedback module 108 ensures that the system remains adaptable and personalized. The feedback module 108 continuously updates the system based on this real-time interaction data, enhancing the precision of both the emotion detection module 104 and the recommendation module 106. This adaptability makes the feedback module 108 an essential component for the ongoing improvement and accuracy of the system.
[0063] The memory unit 110 plays a crucial role in storing data related to user preferences, emotional states, and listening history. The memory unit 110 ensures that the system has access to historical data, which the recommendation module 106 uses to refine its music suggestions. By keeping track of the user's emotional patterns over time, the memory unit 110 allows the system to learn and adapt to individual preferences. This stored data ensures that future recommendations become increasingly accurate and personalized as the system learns more about the user's emotional responses to music. The memory unit 110 also serves as a repository for user interaction data collected by the feedback module 108, further improving the system's overall performance.
[0064] The image capturing unit 112 captures real-time images of the user's facial expressions and sends this data to the emotion detection module 104 for analysis. The image capturing unit 112 is capable of capturing high-definition images, allowing the emotion detection module 104 to identify subtle facial expressions with a high degree of accuracy. This real-time image capture is crucial for the system's ability to detect the user's emotional state continuously and accurately. The image capturing unit 112 operates in conjunction with the processing unit 102 to ensure that the captured images are analysed in real-time, providing immediate feedback for the system's other components to process. By capturing high-quality images, the image capturing unit 112 ensures that the system can accurately detect and respond to changes in the user's emotional state.
[0065] The music database 114 stores a large collection of music tracks, each with associated metadata that classifies the songs based on various attributes, including genre, mood, tempo, and emotion-based categorization. The music database 114 is operatively connected to the processing unit 102 and the recommendation module 106, providing the system with the data needed to match the user's emotional state with appropriate music tracks. The music database 114 continuously updates with new tracks and metadata to ensure the system has access to a wide variety of music options. The recommendation module 106 relies on the music database 114 to generate accurate and relevant music suggestions for the user, making this component critical for delivering a personalized listening experience.
[0066] The audio output unit 116 is responsible for playing the recommended music tracks once the recommendation module 106 selects them from the music database 114. The audio output unit 116 delivers the music in high-quality sound and may adjust playback parameters such as volume or tempo based on the user's detected emotional state. Operatively connected to the processing unit 102, the audio output unit 116 ensures that the user receives auditory feedback immediately after the music is selected. The audio output unit 116 can also adapt dynamically to the user's real-time emotional state, providing a tailored listening experience that aligns with the user's mood.
[0067] The communication network 118 enables data transmission between all components of the system, including the processing unit 102, the image capturing unit 112, the recommendation module 106, and the feedback module 108. The communication network 118 ensures that data flows seamlessly between components, allowing the system to process real-time inputs and provide immediate outputs. By facilitating communication between components, the communication network 118 ensures that the system operates smoothly and efficiently, with minimal delays between emotion detection, music recommendation, and audio output. This interconnectedness is essential for maintaining the system's responsiveness to the user's emotional state.
[0068] The user device 120 allows the user to interact with the emotion-driven music recommendation system 100, providing access to the emotion detection and music recommendation functionalities. The user device 120 is operatively connected to the processing unit 102 and serves as the primary means through which the user engages with the system. The user device 120 provides real-time feedback on the detected emotion, allowing the user to see how their emotional state is being interpreted by the system. Through the user device 120, the user can also input preferences, manage their music library, and provide feedback on the music recommendations.
[0069] The user interface 122, integrated into the user device 120, provides an intuitive platform for users to interact with the system. The user interface 122 displays real-time information about the detected emotions, recommended music, and the system's interpretation of the user's mood. By offering a visually appealing and easy-to-use interface, the user interface 122 ensures that the user can navigate the system's features effortlessly. The user interface 122 enhances the overall user experience by providing clear and immediate feedback on how the system is interpreting and responding to the user's emotional state.
[0070] FIG. 2 illustrates a process flow for an emotion-driven music recommendation system, in accordance with an exemplary embodiment of the present disclosure.
[0071] At 202, user may upload and manage the song albums by categorizing songs according to their genre.
[0072] At 204, the face image of the user will be captured through the webcam.
[0073] At 206, the facial features will then be extracted and compared with the user's expression database.
[0074] At 208, after the emotion has been detected, the music player will play the songs accordingly.
[0075] FIG. 3 illustrates a flowchart of an emotion-driven music recommendation system, in accordance with an exemplary embodiment of the present disclosure.
[0076] At 302, the user uploads and manages song albums, categorizing songs based on their genre.
[0077] At 304, the image capturing unit captures real-time facial expressions of the user.
[0078] At 306, the emotion detection module analyses facial expressions to detect the user's emotional state
[0079] At 308, the recommendation module cross-references the detected emotion with the music database to suggest appropriate tracks.
[0080] At 310, the system plays the recommended music through the audio output unit.
[0081] At 312, the feedback module gathers user interaction data to refine the emotion detection and recommendation algorithms.
[0082] At 314, the memory unit stores user preferences, emotional states, and interaction history to improve future recommendations.
[0083] FIG. 4 illustrates a flowchart of an emotion-driven music recommendation method, in accordance with an exemplary embodiment of the present disclosure.
[0084] At 402, capturing real-time images of the user's facial expressions using an image capturing unit.
[0085] At 404, processing the captured images using an emotion detection module integrated within a processing unit, wherein the emotion detection module analyses the facial expressions to detect the user's emotional state.
[0086] At 406, storing user data including emotional states and interaction history in a memory unit operatively connected to the processing unit, wherein the memory unit stores previous user preferences and emotional responses to recommended music.
[0087] At 408, suggesting music tracks to the user using a recommendation module, wherein the recommendation module cross-references the detected emotion with a music database, selecting music based on emotion-based categorization.
[0088] At 410, playing the recommended music via an audio output unit, wherein the audio output unit dynamically adjusts playback parameters such as volume and tempo based on the detected emotional state.
[0089] At 412, collecting user interaction data through a feedback module integrated within the processing unit, wherein the feedback module improves the accuracy of future recommendations by adjusting algorithms based on real-time feedback.
[0090] At 414, displaying the detected emotional state and suggested music on a user device with an integrated user interface, providing real-time feedback to the user regarding the system's interpretation of the emotion and the selected music tracks.
[0091] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it will be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0092] A person of ordinary skill in the art may be aware that, in combination with the examples described in the embodiments disclosed in this specification, units and algorithm steps may be implemented by electronic hardware, computer software, or a combination thereof.
[0093] The foregoing descriptions of specific embodiments of the present disclosure have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed, and many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described to best explain the principles of the present disclosure and its practical application, and to thereby enable others skilled in the art to best utilize the present disclosure and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omissions and substitutions of equivalents are contemplated as circumstances may suggest or render expedient, but such omissions and substitutions are intended to cover the application or implementation without departing from the scope of the present disclosure.
[0094] Disjunctive language such as the phrase "at least one of X, Y, Z," unless specifically stated otherwise, is otherwise understood with the context as used in general to present that an item, term, etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z). Thus, such disjunctive language is not generally intended to, and should not, imply that certain embodiments require at least one of X, at least one of Y, or at least one of Z to each be present.
[0095] In a case that no conflict occurs, the embodiments in the present disclosure and the features in the embodiments may be mutually combined. The foregoing descriptions are merely specific implementations of the present disclosure, but are not intended to limit the protection scope of the present disclosure. Any variation or replacement readily figured out by a person skilled in the art within the technical scope disclosed in the present disclosure shall fall within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
, Claims:I/We Claim:
1. An emotion-driven music recommendation system (100) comprising:
a processing unit (102) configured to control and manage the overall operation of the system, wherein the processing unit (102) includes:
an emotion detection module (104) configured to detect the user's emotional state based on real-time analysis of facial expressions captured by an image capturing unit (112);
a recommendation module (106) configured to suggest music tracks based on the detected emotion by cross-referencing the user's emotional state with metadata stored in a music database (114);
a feedback module (108) configured to collect user interaction data and continuously improve the accuracy of the emotion detection and music recommendation algorithms;
a memory unit (110) configured to store user preferences, interaction history, and emotional states over time to enhance future recommendations;
the image capturing unit (112), operatively connected to the processing unit (102), configured to capture real-time images of the user's facial expressions;
a music database (114), operatively connected to the processing unit (102), configured to store a plurality of music tracks with associated metadata, including emotion-based categorization;
an audio output unit (116), operatively connected to the processing unit (102), configured to play the recommended music;
a communication network (118), operatively connected to the processing unit (102), configured to facilitate the transmission of user data and music data between the system components;
a user device (120) configured to allow user interaction with the system through an integrated user interface (122), wherein the user device (120) is operatively connected to the processing unit (102) via the communication network (118) and provides access to the emotion detection and music recommendation functionalities.
2. The system (100) as claimed in claim 1, wherein the image capturing unit (112) is configured to capture high-definition images to improve the precision of the emotion detection module (104).
3. The system (100) as claimed in claim 1, wherein the emotion detection module (104) uses convolutional neural networks (CNN) for detecting emotions by analysing facial landmarks and other features.
4. The system (100) as claimed in claim 1, wherein the recommendation module (106) is configured to analyse the user's historical emotional states and listening habits, generating personalized music playlists that match the user's mood.
5. The system (100) as claimed in claim 1, wherein the user device (120) displays the detected emotion in real-time, providing feedback to the user regarding the system's interpretation of their emotional state and the recommended music.
6. The system (100) as claimed in claim 1, wherein the memory unit (110) stores user data, including emotional responses to previously recommended tracks, ensuring that the recommendation module (106) is continuously optimized to suit the user's preferences.
7. The system (100) claimed in claim 1, wherein the feedback module (108) adapts the system's music recommendation algorithms based on real-time user interactions, thereby enhancing the relevance of the music suggestions provided.
8. The system (100) as claimed in claim 1, wherein the audio output unit (116) dynamically adjusts playback parameters such as volume or tempo, based on the user's emotional state, providing a tailored listening experience.
9. An emotion-driven music recommendation method (100) comprising:
capturing real-time images of the user's facial expressions using an image capturing unit (112);
processing the captured images using an emotion detection module (104) integrated within a processing unit (102), wherein the emotion detection module (104) analyses the facial expressions to detect the user's emotional state;
storing user data including emotional states and interaction history in a memory unit (110) operatively connected to the processing unit (102), wherein the memory unit (110) stores previous user preferences and emotional responses to recommended music;
suggesting music tracks to the user using a recommendation module (106), wherein the recommendation module (106) cross-references the detected emotion with a music database (114), selecting music based on emotion-based categorization;
playing the recommended music via an audio output unit (116), wherein the audio output unit (116) dynamically adjusts playback parameters such as volume and tempo based on the detected emotional state;
collecting user interaction data through a feedback module (108) integrated within the processing unit (102), wherein the feedback module (108) improves the accuracy of future recommendations by adjusting algorithms based on real-time feedback;
displaying the detected emotional state and suggested music on a user device (120) with an integrated user interface (122), providing real-time feedback to the user regarding the system's interpretation of the emotion and the selected music tracks.
10. The method (100) as claimed in claim 9, wherein the method (100) further comprises analysing the user's emotional trends over time through the memory unit (110), wherein the system (100) dynamically updates the emotional baseline of the user, enabling the recommendation module (106) to proactively suggest mood-enhancing or mood-stabilizing music tracks based on detected emotional fluctuations.

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