Intelligent headphone capable of adaptively playing music and system thereof

Abstract

The invention discloses a self-adaptive music playing intelligent headphone and a system thereof, and relates to the technical field of headphones, the headphone comprises a head frame and earmuffs, the head frame comprises an outer shell, a fixing frame is fixedly mounted in the outer shell, and slideways are fixedly mounted at two ends of the fixing frame; a movable connecting rod is arranged in the slide way, and a limiting groove and a limiting block are arranged at one end of the connecting rod; the earmuff comprises an earmuff shell, a connecting piece used for being connected with the connecting rod is fixedly installed in the earmuff shell, and a touch screen control module is installed on one side of the outer portion of the earmuff shell. According to the system, the mood state of the user is automatically recognized and the emotional state of the user is judged by integrating various sensors (such as a heart rate sensor and a skin electric reaction sensor), so that the accuracy of emotion recognition is improved, subtle emotion changes can be captured, more appropriate music recommendation is provided, and the use effect of equipment is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of earphones, and particularly to an intelligent head-mounted earphone for adaptive music playing and a system thereof. BACKGROUND

[0002] With the progress of technology and the improvement of people's living standards, music has become an indispensable part of daily life. As an important tool for personal music listening, earphones have undergone several innovations in design and functionality. Traditional earphones mainly focus on sound quality, wearing comfort, and appearance design, etc. With the development of smart devices, the functionality of earphones has been greatly expanded. For example, modern earphones have integrated Bluetooth wireless connection, touch control, and voice assistants, greatly improving the user experience.

[0003] However, in terms of personalized services, traditional earphones still have certain limitations. Specifically, although users can adjust their mood by manually selecting playlists or songs, this approach cannot dynamically adjust according to the user's immediate emotional changes. Therefore, how to realize the automatic recognition of the user's emotional state by the earphone and recommend corresponding music tracks accordingly has become a direction worthy of research.

[0004] In the prior art, some attempts have been made to integrate sensors to monitor the user's physiological parameters such as heart rate, skin electrical response, etc. to infer the user's mood state. For example, some fitness trackers can monitor heart rate variability (HRV) to assess stress levels; there are also smartphone applications that use front-facing cameras to analyze facial expressions to determine emotional changes. However, these solutions usually rely on external devices and fail to directly apply emotional recognition to music recommendation systems.

[0005] In addition, although there are some intelligent music service applications on the market that can provide personalized music recommendations based on the user's listening history and preferences, such recommendations are often based on long-term behavior patterns rather than real-time emotional states. Moreover, most of these services do not combine hardware devices to directly obtain the user's physiological data, limiting their accuracy in emotional recognition. Therefore, the present application proposes an intelligent head-mounted earphone for adaptive music playing and a system thereof. SUMMARY

[0006] TECHNICAL PROBLEM SOLVED

[0007] The purpose of this invention is to overcome the shortcomings of existing technologies and provide an intelligent over-ear headphone and system for adaptive music playback. This headphone integrates multiple sensors (such as heart rate sensors and skin conductance sensors) to collect users' physiological signals and behavioral data in real time. It also features a highly efficient data preprocessing module for noise reduction, normalization, and feature extraction. More importantly, it employs advanced artificial intelligence algorithms to construct an emotion recognition module that can automatically identify the user's current emotional state and, based on this, select and recommend the most suitable music tracks from a pre-built music emotion database.

[0008] Technical solution

[0009] To achieve the above objectives, the present invention provides the following technical solution: an intelligent over-ear headphone with adaptive music playback, comprising a headband and earcups, wherein the headband comprises an outer shell, a fixing frame is fixedly installed inside the outer shell, and slide rails are fixedly installed at both ends of the fixing frame, wherein a movable connecting rod is provided inside the slide rails, and a limit groove and a limit block are provided at one end of the connecting rod;

[0010] The earmuff includes an earmuff housing, inside which a connector for connecting to a connecting rod is fixedly installed. A touch screen control module is installed on the outer side of the earmuff housing, and a speaker is installed on the inner side of the earmuff housing. The touch screen control module also contains a control motherboard and a power supply.

[0011] Preferably, the connector includes a first fixing member and a second fixing member, which are interlocked with each other, and a U-shaped limiting rod is provided between the first fixing member and the second fixing member. The U-shaped limiting rod is embedded in the limiting groove. A semi-circular limiting groove is also provided inside the first fixing member. The limiting block is movably installed inside the semi-circular limiting groove. The semi-circular limiting groove is used to limit the rotation angle of the connecting rod, thereby limiting the rotation angle between the headband and the earcups.

[0012] Preferably, the control motherboard integrates a 4G communication module and a WIFI module.

[0013] Preferably, the device also includes a sensor module integrated into the earphone for monitoring the user, the sensor module including one or more of a heart rate sensor, a skin conductance sensor, an accelerometer, a gyroscope, a blood oxygen sensor, and an ambient light sensor.

[0014] Preferably, one end of the connecting rod is fixedly connected to a slider, and the inside of the slide rail is provided with a groove that matches the slider.

[0015] Preferably, an ear pad is connected to the inner side of the earcup housing.

[0016] A system for adaptive music playback smart headphones, comprising:

[0017] The sensor module is used to collect the user's physiological signals and behavioral data;

[0018] The data preprocessing module is used to denoise, normalize, and extract features from the data collected by the various sensor modules.

[0019] The emotion recognition module analyzes pre-processed data based on artificial intelligence algorithms to automatically identify the user's current emotional state.

[0020] The music recommendation module selects and recommends corresponding music tracks from a pre-built music emotion database based on the identified emotional state.

[0021] The playback control module is used to automatically switch songs or playlists based on the recommendation results of the music recommendation module.

[0022] The feedback learning module optimizes the emotion recognition model and music recommendation strategy by collecting user feedback.

[0023] Preferably, the emotion recognition module employs a multimodal fusion artificial intelligence model, which combines deep neural networks and traditional machine learning algorithms to process heterogeneous data from different sensors.

[0024] Preferably, the playback control module supports voice control, gesture control, and touch operation, allowing users to control music playback without manual input.

[0025] Preferably, the feedback learning module uses reinforcement learning algorithms to continuously optimize the emotion recognition model and music recommendation strategy, thereby improving the quality of personalized services.

[0026] Beneficial effects:

[0027] Compared with existing technologies, this adaptive music playback smart headphones and system have the following advantages:

[0028] The system in this invention automatically identifies the user's mood state by integrating multiple sensors (such as heart rate sensors, skin conductance sensors, etc.) and judges the user's emotional state. This not only improves the accuracy of emotion recognition but also captures subtle emotional changes, thereby providing more appropriate music recommendations and improving the user experience of the device. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Fig. 1 This is a schematic diagram of the structure of the present invention;

[0031] Fig. 2 This is a schematic diagram of the head frame structure of the present invention;

[0032] Fig. 3 This is a schematic diagram of the installation structure of the connecting rod of the present invention;

[0033] Fig. 4 This is a schematic diagram of the head frame structure of the present invention;

[0034] Fig. 5 This is a schematic diagram of the connector structure of the present invention;

[0035] Fig. 6 This is a schematic diagram of the mounting structure of the loudspeaker of the present invention.

[0036] In the picture:

[0037] 1. Headband; 2. Earmuffs; 101. Outer shell; 102. Fixing frame; 103. Slide rail; 104. Connecting rod; 105. Limiting groove; 106. Limiting block; 201. Earmuff shell; 202. Connector; 203. Touch screen control module; 204. Speaker; 205. Ear pad; 2021. First fixing component; 2022. Second fixing component; 2023. U-shaped limiting rod; 2024. Semi-circular limiting groove; 1041. Slider; 1031. Slide groove. Detailed Implementation

[0038] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0039] Please see Figs. 1-6As shown, the present invention provides a technical solution: an intelligent headphone with adaptive music playback, including a headband 1 and earcups 2. The headband 1 includes an outer shell 101, and a fixing frame 102 is fixedly installed inside the outer shell 101. Slide rails 103 are fixedly installed at both ends of the fixing frame 102. A movable connecting rod 104 is provided inside the slide rail 103. A limiting groove 105 and a limiting block 106 are provided at one end of the connecting rod 104. A slider 1041 is fixedly connected to one end of the connecting rod 104. A sliding groove 1031 adapted to the slider is opened inside the slide rail 103.

[0040] The earcup 2 of this invention includes an earcup housing 201. A connector 202 for connecting to a connecting rod 104 is fixedly installed inside the earcup housing 201. A touchscreen control module 203 is installed on the outer side of the earcup housing 201. The touchscreen control module 203 is magnetically attached and can be easily disassembled, facilitating user operation of the screen to control music playback. A speaker 204 is installed on the inner side of the earcup housing 201. The touchscreen control module 203 also houses a control motherboard and a power supply. The control motherboard integrates a 4G communication module and a WIFI module. Ear pads 205 are connected to the inner side of the earcup housing 201. The connector 202 includes a first fixing member 2021 and a second fixing member 2022, which are interlocked with each other. A U-shaped limiting rod 2023 is provided between the first fixing member 2021 and the second fixing member 2022. The U-shaped limiting rod 2023 is embedded in the limiting groove 105. A semi-circular limiting groove 2024 is also provided inside the first fixing member 2021. A limiting block 106 is movably installed inside the semi-circular limiting groove 2024. The semi-circular limiting groove 2024 is used to limit the rotation angle of the connecting rod 104, thereby limiting the rotation angle between the headband 1 and the earmuff 2.

[0041] It also includes sensor modules integrated into the headphones for user monitoring. These sensor modules include one or more of the following: heart rate sensor, skin conductance sensor, accelerometer, gyroscope, blood oxygen sensor, and ambient light sensor. These sensors are directly integrated into the headphones and connected to the central processing unit (CPU / GPU) via wired or wireless means. For example, heart rate sensors and skin conductance sensors can be directly embedded in the parts of the headphones that contact the skin; accelerometers and gyroscopes can be installed inside the headphones to monitor head movements; microphones are used to capture ambient sounds and user voice. The data collected by the sensors is transmitted to the data preprocessing module after being converted from analog signals to digital signals via digital interfaces (such as I2C, SPI, UART). The headphones also have microphones for picking up user voice, breathing sounds, coughs, and other sounds.

[0042] A system for adaptive music playback smart headphones, the system comprising:

[0043] The sensor module is used to collect the user's physiological signals and behavioral data;

[0044] The data preprocessing module is used to denoise, normalize, and extract features from data collected by various sensor modules. This module runs on the microcontroller built into the earphone or on an application connected to an external device (such as a smartphone) via Wi-Fi. It receives raw data from various sensor modules, performs preliminary processing on the collected data, including but not limited to filtering and denoising, and feature extraction, and then transmits the processed data to the emotion recognition module.

[0045] The emotion recognition module analyzes pre-processed data using artificial intelligence algorithms to automatically identify the user's current emotional state. It resides on a cloud server. Pre-processed data needs to be uploaded to the server via a network (Wi-Fi / 4G). Upon receiving the pre-processed data, the module uses a trained AI model to determine the user's current emotional state and sends the result to the music recommendation module.

[0046] The music recommendation module selects and recommends relevant music tracks from a pre-built music emotion database based on the identified emotional state. This music recommendation module is a cloud service that accompanies the headphones. It needs to access a large music emotion database, which can be stored locally or on a remote server. Based on the information received from the emotion recognition module and combined with the user's preference history, it uses a recommendation algorithm to select suitable music tracks and returns the recommendation list to the playback control module.

[0047] The playback control module automatically switches songs or playlists based on recommendations from the music recommendation module. It directly controls the headphone's audio output unit. It can be a simple built-in controller or an application interface connected to a mobile device via Wi-Fi. It automatically starts playing corresponding music based on information provided by the music recommendation module, and also supports manual adjustment of playback settings by the user via touch, voice commands, or gestures.

[0048] The feedback learning module optimizes the emotion recognition model and music recommendation strategy by collecting user feedback. It can be integrated into the application or run as a background service. It is responsible for collecting user feedback (such as liking / disliking a song) and using this information to optimize the emotion recognition model and music recommendation strategy. It obtains feedback data from user interactions, updates the emotion recognition model parameters, improves the recommendation algorithm, and then deploys the updated model back to the system.

[0049] These modules are interconnected through hardware interfaces, software APIs, and network protocols, forming a complete closed-loop system. Each module has its specific function, but they work together to automate the entire process from physiological signal acquisition to emotion recognition and personalized music recommendation.

[0050] Working principle:

[0051] Step 1: Sensor Deployment and Data Acquisition

[0052] The following sensors are embedded in the headphones to collect users' physiological and behavioral data:

[0053] Heart rate sensors (PPG or ECG) are used to detect heart rate and heart rate variability (HRV); skin conductance sensors (GSR) are used to detect skin conductivity and reflect emotional fluctuations; accelerometers and gyroscopes detect head movements and body posture; blood oxygen sensors (SpO2) are used to help determine whether the user is in a state of tension or fatigue; ambient light sensors are used to determine ambient light and help determine time and scene.

[0054] Step 2: Data Preprocessing and Feature Extraction

[0055] The collected raw data needs to be processed as follows:

[0056] Denoising: Using filters (such as low-pass filters) to remove noise from a signal;

[0057] Normalization: Performing dimensional standardization on data from different sensors;

[0058] Feature extraction: Heart rate: average heart rate, HRV (SDNN, RMSSD); GSR: amplitude and frequency of skin conductance response; Motion state: frequency and amplitude of head movements; Speech features: pitch, speech rate, energy, MFCC (Mel-frequency cepstral coefficients).

[0059] Step 3: Emotion Recognition Algorithm (AI Model)

[0060] Model selection and training process:

[0061] Use the Valence-Arousal model (two-dimensional emotion model) in psychology or Ekman's basic six emotion categories (happiness, sadness, anger, fear, surprise, disgust).

[0062] Deep learning:

[0063] CNN: Used to extract local features from time-series signals;

[0064] RNN / LSTM: Processing time series data (such as heart rate changes);

[0065] Transformer: Processes multimodal fusion data (multi-sensor signals);

[0066] Multimodal fusion model: combines data from multiple sources such as heart rate, speech, and motion;

[0067] Use publicly available datasets (such as DEAP, SEED, RAVDESS) for model pre-training.

[0068] Data is continuously collected during user interaction and uploaded to the cloud for model fine-tuning (federated learning).

[0069] Deploy lightweight models (such as TensorFlow Lite or ONNX Runtime) locally on the headphones for real-time emotion recognition.

[0070] Building a music emotion database:

[0071] Each song is labeled with an emotional tag (such as cheerful, sad, inspiring, etc.), which can be based on manual labeling or automatic labeling by AI.

[0072] Modeling can be done by combining musical features (rhythm, melody, pitch, spectrum, etc.).

[0073] Deep recommendation model: Uses neural network models (such as DeepFM, Wide&Deep) for personalized recommendations. If the user's emotion is "sad", it recommends soothing and healing music; if the user's emotion is "excited", it recommends dynamic and rhythmic music.

[0074] Step 5: Playback Controls

[0075] Playback control module: Automatically switches between songs or playlists based on recommendations.

[0076] Step 6: User Feedback

[0077] Users can indicate whether they "like" or "dislike" the currently playing song. The feedback data is used to optimize the recommendation algorithm and improve accuracy.

[0078] This system integrates multiple sensors to collect users' physiological signals and behavioral data, uses artificial intelligence algorithms to analyze the data, automatically identifies the user's current emotional state (such as happiness, sadness, anger, relaxation, etc.), and combines it with a music emotion database to intelligently recommend music that matches the user's current mood, achieving adaptive music playback without requiring manual input from the user.

[0079] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0080] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A smart headphone with adaptive music playback, comprising a headband (1) and earcups (2), characterized in that: The head frame (1) includes an outer shell (101), and a fixing frame (102) is fixedly installed inside the outer shell (101). Slide rails (103) are fixedly installed at both ends of the fixing frame (102). A movable connecting rod (104) is provided inside the slide rail (103). One end of the connecting rod (104) is provided with a limiting groove (105) and a limiting block (106). The earmuff (2) includes an earmuff housing (201), inside which a connector (202) for connecting to a connecting rod (104) is fixedly installed. A magnetic touch screen control module (203) is installed on the outer side of the earmuff housing (201), and a speaker (204) is installed on the inner side of the earmuff housing (201). The touch screen control module (203) contains a control motherboard and a power supply.

2. The smart headphones with adaptive music playback according to claim 1, characterized in that: The connector (202) includes a first fixing member (2021) and a second fixing member (2022). The first fixing member (2021) and the second fixing member (2022) are interlocked with each other, and a U-shaped limiting rod (2023) is provided between the first fixing member (2021) and the second fixing member (2022). The U-shaped limiting rod (2023) is embedded in the limiting groove (105). A semi-circular limiting groove (2024) is also provided inside the first fixing member (2021). The limiting block (106) is movably installed inside the semi-circular limiting groove (2024). The semi-circular limiting groove (2024) is used to limit the rotation angle of the connecting rod (104), thereby limiting the rotation angle between the head frame (1) and the earmuff (2).

3. The smart headphones with adaptive music playback according to claim 1, characterized in that: The control motherboard integrates a 4G communication module and a WIFI module.

4. The smart headphones with adaptive music playback according to claim 1, characterized in that: It also includes a sensor module integrated into the headphones for user monitoring, including one or more of a heart rate sensor, skin conductance sensor, accelerometer, gyroscope, blood oxygen sensor, and ambient light sensor.

5. The smart headphones with adaptive music playback according to claim 1, characterized in that: One end of the connecting rod (104) is fixedly connected to a slider (1041), and the slide rail (103) has a groove (1031) inside that is adapted to the slider.

6. The smart headphones with adaptive music playback according to claim 1, characterized in that: The ear pad (205) is connected to the inside side of the ear cup housing (201).

7. A system for adaptive music playback of smart headphones, characterized in that: The system includes: The sensor module is used to collect the user's physiological signals and behavioral data; The data preprocessing module is used to denoise, normalize, and extract features from the data collected by the various sensor modules. The emotion recognition module analyzes pre-processed data based on artificial intelligence algorithms to automatically identify the user's current emotional state. The music recommendation module selects and recommends corresponding music tracks from a pre-built music emotion database based on the identified emotional state. The playback control module is used to automatically switch songs or playlists based on the recommendation results of the music recommendation module. The feedback learning module optimizes the emotion recognition model and music recommendation strategy by collecting user feedback.

8. The system for an adaptive music playback smart headphone according to claim 7, characterized in that: The emotion recognition module employs a multimodal fusion artificial intelligence model, which combines deep neural networks and traditional machine learning algorithms to process heterogeneous data from different sensors.

9. The system for an adaptive music playback smart headphone according to claim 8, characterized in that: The playback control module supports voice control, gesture control, and touch operation, allowing users to control music playback without manual input.

10. The system for an adaptive music playback smart headphone according to claim 9, characterized in that: The feedback learning module uses reinforcement learning algorithms to continuously optimize the emotion recognition model and music recommendation strategy, thereby improving the quality of personalized services.

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