Subconsciousness development music generation method based on subconsciousness hierarchical coding

Abstract

The invention provides a subconsciousness development music generation method based on subconsciousness hierarchical coding, and the method comprises the following steps: obtaining subconsciousness hierarchical features of a user, and constructing a subconsciousness hierarchical coding model based on the subconsciousness hierarchical features of the user; collecting real-time physiological and behavior data of a user, and inputting the real-time physiological and behavior data into the subconsciousness hierarchical coding model to generate personalized music parameters; modulating the personalized music parameters by using a multi-mode coding technology; according to a real-time feedback dynamic adjustment coding strategy, subconsciousness development music adaptive to the current state of the user is output, and the current dominant subconsciousness level is accurately judged by comprehensively collecting real-time physiological data and subconsciousness layering information of the user. According to the method, the problem of inaccurate subconsciousness layered recognition in the past is solved, so that coding can be performed in a targeted manner according to the characteristics of different layers when music is generated.

Description

Technical Field

[0001] The present invention relates to the field of music generation technology, and in particular to a method for generating subconscious development music based on subconscious hierarchical coding. The method aims to utilize the subconscious hierarchical theory, integrate multi-source data, and use a variety of coding technologies to generate music works that can effectively act on the user's subconscious and realize personalized subconscious development. Background Art

[0002] As people's research on the subconscious mind deepens and their attention to personal growth and mental health increases, subconscious development music, as a potential auxiliary tool, has received more and more attention. However, the existing subconscious development music generation technology still has certain problems:

[0003] 1. Inaccurate identification of subconscious layers. Current technology makes it difficult to accurately distinguish between different depths and types of subconscious layers. Due to the lack of precise understanding and identification of subconscious layers, it is impossible to effectively encode each level of the subconscious in a targeted manner when generating music, resulting in a lack of accuracy and depth in the effect of music on the subconscious. For example, it is difficult to accurately judge whether the subconscious of consumers receiving information in brand marketing belongs to instinctive desires, emotional memories, or other more subtle levels, making it difficult to target music encoding.

[0004] Second, lack of personalized customization. The subconscious composition and characteristics of each person are different due to various influences such as growth experience, cultural background, and genetic factors. However, most current technologies use general models and assumptions, which makes it difficult to perform personalized subconscious hierarchical coding based on individual differences. This results in uneven effects of music on the subconscious development of different individuals, and cannot fully meet the diverse needs of individuals.

[0005] 3. The integration of music and subconscious coding is imperfect. On the one hand, the coding method is relatively simple, mostly focusing on a simple combination of basic musical elements such as rhythm, melody, and harmony, and cannot fully utilize the rich expressiveness and diversity of music to achieve more complex and in-depth subconscious coding. For example, the common method of only adjusting the rhythm to match the brain wave frequency cannot fully mobilize the influence of musical elements on the subconscious. On the other hand, during the playback of music, there is a lack of a mechanism to dynamically adjust the subconscious coding according to the real-time feedback and status of the audience. When the audience has emotional fluctuations or attention shifts, the music cannot automatically adjust the coding method to better adapt to the changes in the audience, affecting the effect of subconscious development.

[0006] IV. Lack of scientific verification and standards. The subconscious mind itself is complex and difficult to directly observe, making it hard to accurately verify the effects of subliminal development music through scientific experiments and determine whether it truly acts on the subconscious mind in accordance with the preset hierarchical coding method. At the same time, there is a lack of unified technical standards and specifications in the industry to guide the generation of subliminal development music. Different developers adopt different methods and concepts, resulting in uneven product quality in the market and making it difficult to ensure the scientific nature and effectiveness of music generation.

[0007] Therefore, a method for generating subliminal development music based on subconscious hierarchical coding is proposed to solve the above problems existing in the prior art. Summary of the Invention

[0008] In view of this, embodiments of the present invention hope to provide a method for generating subliminal development music based on subconscious hierarchical coding to solve or alleviate the technical problems existing in the prior art and at least provide a beneficial option.

[0009] To solve the above technical problems, a technical solution adopted by this application is: providing a method for generating subliminal development music based on subconscious hierarchical coding, including the following steps: obtaining the user's subconscious hierarchical characteristics, and constructing a subconscious hierarchical coding model based on the user's subconscious hierarchical characteristics; collecting the user's real-time physiological and behavioral data, and inputting it into the subconscious hierarchical coding model to generate personalized music parameters; modulating the personalized music parameters using multi-modal coding technology; dynamically adjusting the coding strategy according to real-time feedback, and outputting subliminal development music adapted to the user's current state.

[0010] As a further preference of this technical solution: The user's subconscious hierarchical characteristics include: Instinctive layer: used to show the δ / θ / α brain wave frequencies, survival instincts, and primitive desires, corresponding to the activation state of the limbic system; Emotional memory layer: used to show the emotional imprints, emotional patterns, and related memories formed by past experiences, corresponding to the activities of the emotional association areas of the prefrontal lobe and temporal lobe; Collective layer: used to show values, collective subconsciousness, and cultural symbols, corresponding to the activation of the default mode network.

[0011] As a further preference of this technical solution: The personalized music parameters include rhythm type, melody trend, harmonic tension, and semantic coding intensity, and form a definite mapping relationship with the user's genetic characteristics, growth experiences, and real-time EEG data.

[0012] As a further preference of this technical solution: The obtaining of the user's subconscious hierarchical features and the construction of the subconscious hierarchical coding model include: using EEG brain electrical equipment and psychological scales to collect the user's innate physiological data and acquired behavioral data; classifying the user's subconscious levels through machine learning algorithms to establish a mapping matrix between music elements and subconscious levels; setting the sound wave frequency thresholds and emotional semantic rules for each layer of coding according to psychological theories.

[0013] As a further preference of this technical solution: The collecting of the user's real-time physiological and behavioral data and the generation of personalized music parameters include: obtaining the user's HRV heart rate variability, skin conductivity, and eye movement trajectory in real time through wearable devices; inputting the real-time data into the trained reinforcement learning model to output the rhythm intensity coefficient, melody semitone offset, and binaural beat frequency difference that match the current dominant subconscious level.

[0014] As a further preference of this technical solution: The multimodal coding technology includes: Sound wave modulation: Embedding ultrasonic subconscious instructions with a frequency higher than 20,000 Hz in the carrier frequency and transmitting through bone conduction; Light frequency synchronization: Generating pulsed light stimuli with the same frequency as the music rhythm and in the 40 Hz gamma wave band to activate the visual cortex; Olfactory binding: Associating specific fragrances with music segments to form a multi-sensory conditioned reflex.

[0015] As a further preference of this technical solution: The dynamically adjusting the coding strategy according to real-time feedback includes: establishing a physiological response and music parameter error function with cortisol concentration change and alpha wave ratio as feedback indicators; optimizing the harmony complexity, rhythm entropy value, and semantic coding density of subsequent music through an adaptive algorithm to form a closed-loop control.

[0016] As a further preference of this technical solution: The output subconscious development music includes a dominant music layer and a recessive coding layer, where: The dominant music layer meets the requirements of melody fluency and artistic aesthetics; The recessive coding layer realizes the implantation of subconscious information through reverse speech embedding, binaural beat difference, and frequency following response technology.

[0017] To solve the above technical problems, another technical solution adopted by this application is: A computer device, the computer device includes a processor and a memory coupled to the processor, and program instructions are stored in the memory. When the program instructions are executed by the processor, the processor executes the steps of the subconscious development music generation method based on subconscious hierarchical coding as described above.

[0018] To solve the above technical problems, another technical solution adopted by this application is: A storage medium stores program instructions capable of implementing the subconscious development music generation method based on subconscious hierarchical coding as described above.

[0019] Due to the adoption of the above technical solutions in the embodiments of the present invention, it has the following advantages:

[0020] Precise layering and efficient coding: By comprehensively collecting real-time physiological data of users and subconscious layering information, the dominant subconscious level is accurately judged. This solves the problem of inaccurate subconscious layering recognition in the past, enabling targeted coding when generating music according to the characteristics of different levels. Personalized customization experience: Reflect the user's unique growth experience, cultural background, genetic factors, etc. in the state space. The model can, based on this personalized information, tailor-made subconscious development music for users by adjusting music parameters in the action space.

[0021] Optimized music and coding integration: This solution enriches the integration method of music and subconscious coding. In the action space, it covers the adjustment of various music parameters, including harmony complexity, rhythm entropy value, semantic coding density, etc., making the music expressiveness more diverse and enabling more complex and in-depth subconscious coding. At the same time, by continuously monitoring the user's physiological response and music effect, using the feedback information of the reward function to dynamically adjust music parameters in real time, it solves the problem that music cannot be dynamically adjusted according to the real-time feedback and state of the listener during playback.

[0022] Scientific verification and approaching standards: During the training process, through a clear reward function and a large amount of data interaction, the reinforcement learning model is continuously optimized, making the relationship between music generation and the effect of user subconscious development more verifiable. As the model is continuously trained and improved, it gradually approaches the establishment of scientific music generation standards. For example, according to the feedback of the user's physiological response and music effect, continuously adjust the action value function and strategy, making the effect of the generated music in subconscious development more stable and measurable, which helps to solve the problem of the lack of scientific verification and unified standards in the industry and improve the overall scientificity and effectiveness of subconscious development music generation technology.

[0023] The above summary is only for the purpose of the specification and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the present invention will be readily apparent by reference to the drawings and the following detailed description. Brief Description of the Drawings

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0025] Figure 1 It is a flowchart of the method of the present invention;

[0026] Figure 2 It is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. Specific embodiments

[0027] The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0028] It should be clear that the following uses specific specific examples to illustrate the implementation manners of the present disclosure. Those skilled in the art can easily understand other advantages and effects of the present disclosure from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all the embodiments. The present disclosure can also be implemented or applied through other different specific implementation manners. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present disclosure without creative efforts belong to the scope of protection of the present disclosure.

[0029] It should be noted that the following describes various aspects of the embodiments within the scope of the appended claims. It should be obvious that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is illustrative only. Based on the present disclosure, those skilled in the art should understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects described herein can be used to implement the device and / or practice the method. In addition, this device and / or practice this method can be implemented using other structures and / or functions in addition to one or more of the aspects described herein.

[0030] It should also be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present disclosure schematically. Only the components related to the present disclosure are shown in the diagrams, rather than drawn according to the number, shape and size of the components in actual implementation. The type, quantity and proportion of each component in its actual implementation can be an arbitrary change, and the component layout type may also be more complex.

[0031] In addition, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the described aspects can be practiced without these specific details.

[0032] Figure 1It is a schematic flowchart of the subconscious development music generation method based on subconscious hierarchical coding according to an embodiment of the present invention. It should be noted that if there are substantially the same results, the method of this application is not limited to Figure 1 the process sequence shown. For example Figure 1 As shown: The subconscious development music generation method based on subconscious hierarchical coding includes the following steps:

[0033] S100. Obtain the user's subconscious hierarchical features, and construct a subconscious hierarchical coding model based on the user's subconscious hierarchical features;

[0034] Specifically, the user's subconscious hierarchical features include: The instinct layer: used to show the δ / θ / α brain wave frequency, survival instinct and primitive desire, corresponding to the activation state of the limbic system; The emotional memory layer: used to show the emotional imprints, emotional patterns and related memories formed by past experiences, corresponding to the activities of the emotional association areas of the prefrontal lobe and the temporal lobe; The collective layer: used to show values, collective subconsciousness and cultural symbols, corresponding to the activation of the default mode network.

[0035] On the basis of the above solution, the instinct layer mainly shows physiological resonance and immersive experience as well as triggering primitive emotional reactions, among which:

[0036] Physiological resonance and immersive experience: In music generation, according to the association between the instinct layer and the δ / θ / α brain wave frequency, music that matches the natural physiological rhythm of the human body can be created. For example, when people are in a relaxed state, the α wave is more active, and the music generator can generate a soothing melody mainly with a frequency of 8-13Hz, such as gentle guitar playing, gurgling water sound effects, etc., so that the listeners can resonate physiologically, as if the body is integrated with the music and enters an immersive experience. This fit with the instinct layer can effectively reduce the listeners' defensive psychology, make them more receptive to the information conveyed by the music, and enhance the influence of the music on the subconscious.

[0037] Triggering primitive emotional reactions: Using the characteristics of the instinct layer to reflect survival instincts and primitive desires, specific elements can be incorporated into music generation to trigger the primitive emotions of the listeners. For example, when creating horror-themed music, simulate the rhythm of rapid footsteps in a dark alley, combined with low and depressing sound effects, to activate the fear reaction of the listeners' instinct layer and let them feel the tense atmosphere as if they were on the scene. By precisely mobilizing the instinct layer, music can break through the limitations of language and culture and directly touch the deepest emotions in the listeners' hearts, enhancing the expressiveness and appeal of the music.

[0038] And the emotional memory layer is to show personalized emotional connection as well as emotional guidance and healing, among which:

[0039] Personalized emotional connection: The music generation system can customize exclusive music for users based on the emotional imprints formed by past experiences. By collecting information such as important events and favorite music types provided by users, the characteristics of their emotional memories are analyzed, and then relevant melodies, harmonies or lyrics elements are incorporated into the creation. For example, if a user mentions the good times spent with his grandmother at the beach in his childhood, the music generator can create music with the sound of waves, light melodies and nostalgic style, which can evoke the user's warm emotional memories, establish a strong personalized emotional connection, and make music a place for users to express their emotions.

[0040] Emotional guidance and healing: With the help of the emotional memory layer's influence on emotional patterns and related memories, music generation can intervene in users' negative emotions. When users are in a negative emotional state such as anxiety or depression, the system generates music that can evoke positive emotional memories based on their past emotional experiences and current emotional analysis. For example, for a user who is suffering from a broken heart, a song is created that incorporates musical elements that he and his lover once liked, but is reinterpreted with positive melodies and lyrics, guiding users to recall good memories, relieve negative emotions, and achieve emotional healing. At the same time, it strengthens users' positive emotional memories and improves their psychological resilience.

[0041] Finally, the collective layer is mainly to show cultural integration and innovation and create a group resonance atmosphere, including:

[0042] Cultural integration and innovation: Based on the values, collective subconsciousness and cultural symbols involved in the collective layer, music generation can organically integrate the characteristic elements of different cultures to create innovative musical works. For example, the traditional Chinese guzheng timbre is combined with the grand arrangement of Western symphony, and the cultural symbols and emotional connotations represented by traditional festivals such as the Spring Festival and the Dragon Boat Festival are incorporated into the melody to create music that has both national characteristics and an international perspective. This integration not only enriches the expression of music, but also promotes cultural exchange and inheritance, allowing audiences around the world to feel the charm of different cultures through music and enhance their sense of cultural identity and belonging.

[0043] Create an atmosphere of group resonance: In large-scale events or public spaces, music generated according to the characteristics of the collective layer can create an atmosphere of group resonance. For example, at occasions such as the opening ceremony of the Olympic Games, music generation can create exciting, uplifting and widely appealing music around collective subconscious themes such as national culture and the Olympic spirit. Through unified melody, rhythm and cultural symbols, it can inspire the audience's deep recognition and pride in the country, nation and sports spirit, so that everyone present can integrate into the collective consciousness, form a strong cohesion and centripetal force, strengthen the influence of the collective subconscious on individuals, and also make music a bond that unites the group's emotions.

[0044] Based on the above solution, obtaining the user's subconscious hierarchical features and constructing a subconscious hierarchical coding model may include:

[0045] S110. Using an EEG brain device and a psychological scale to collect the user's innate physiological data and acquired behavioral data; assuming the EEG data obtained by the EEG brain device is E, and the data collected through the psychological scale is P, then the set of the user's innate physiological and acquired behavioral data D = (E, P).

[0046] S120. Classifying the user's subconscious levels through a machine learning algorithm and establishing a mapping matrix between music elements and subconscious levels;

[0047] S121. The hierarchical classification includes processing the data D using the machine learning algorithm M to obtain the subconscious hierarchical classification result S = M(D), where S covers the instinctive layer I, the emotional memory layer A, and the collective layer C.

[0048] S122. Establishing the mapping matrix includes: assuming the music element set is Y (including rhythm R, melody Me, harmony H, timbre T, etc.), and constructing a music element - subconscious level mapping matrix Y m = Map(Y, S), so as to clarify the corresponding relationship between different levels and music elements.

[0049] S130. Setting the sound wave frequency threshold and emotional semantic rules for each layer's encoding according to psychological theories.

[0050] According to psychological theories, set the encoding rule for the instinctive layer as R i (such as the rhythm and timbre rules related to δ / θ / α brain wave frequencies), the encoding rule for the emotional memory layer as R a (such as the melody and harmony rules related to emotional imprints and memories), the encoding rule for the collective layer as R c (such as the music element rules related to cultural symbols and values), and the encoding rule set R s = (R i , R a , R c ), and then constructing the subconscious hierarchical coding model M o = Build(S, Y m , R s ).

[0051] S200. Collect the user's real-time physiological and behavioral data and input it into the subconscious hierarchical coding model to generate personalized music parameters; among them, the personalized music parameters include rhythm type, melody trend, harmony tension, and semantic coding intensity, and form a definite mapping relationship with the user's genetic characteristics, growth experience, and real-time EEG data.

[0052] Specifically, step S200 may include:

[0053] S210. Obtain the user's HRV (heart rate variability), skin conductivity, and eye movement trajectory in real time through a wearable device; collect the user's real-time HRV heart rate variability data as H, skin conductivity data as S c , and eye movement trajectory data as E t , then the real-time physiological and behavioral data set R d = (H, S c , E t ).

[0054] S220. Input the real-time data into a trained reinforcement learning model, and output the rhythm intensity coefficient, melody semitone offset, and binaural beat frequency difference that match the current subconscious dominant level. Input the real-time data R d into the trained reinforcement learning model R l , and combine it with the subconscious hierarchical coding model M o , and output the personalized music parameters P a = R l (R d , M o ). Where P a includes the rhythm intensity coefficient P f , the melody semitone offset M l , and the binaural beat frequency difference B f .

[0055] More specifically, when training the reinforcement learning model, it includes:

[0056] Determine the state space: Collect the user's real-time physiological data, such as HRV heart rate variability, skin conductivity, eye movement trajectory, etc., which can reflect the user's current physical and mental state. At the same time, incorporate the user's subconscious hierarchical information, that is, the instinct layer, emotional memory layer, and collective layer, to judge the current dominant subconscious level. In addition, the previously generated music parameters, such as harmony complexity, rhythm entropy value, etc., are also included, and these information together constitute the state space.

[0057] Define the action space: The actions that the model can take are to adjust the music parameters, including adjusting the rhythm intensity coefficient, changing the melody semitone offset, setting the binaural beat frequency difference, and modifying the harmony complexity, rhythm entropy value, semantic coding density, etc. The set of these adjustable parameters forms the action space.

[0058] Set the reward function: Set the reward based on the user's physiological response and the music effect. If the user's cortisol concentration decreases, indicating a reduction in stress, or the proportion of alpha waves increases, representing an improvement in the relaxation state, and the generated music receives a high evaluation from the user in terms of melody fluency and artistic aesthetics, a positive reward is given; conversely, if the music fails to achieve the subconscious development effect or triggers uncomfortable physiological reactions in the user, a negative reward is given.

[0059] Select a strategy: Usually adopt a strategy like the ε-greedy strategy. That is, with a probability of ε, randomly select an action from the action space to explore new possibilities; with a probability of 1 - ε, select the optimal action that can obtain the maximum reward currently, and utilize the existing experience.

[0060] Estimate the action value function: Use a neural network to estimate the action value function Q(s, a). Take the state s as the input of the neural network, and through training, let the network output the Q value corresponding to each action a in this state. This Q value represents the expected value of the future cumulative reward after taking action a in state s.

[0061] Conduct training: The model starts from the initial state, selects an action according to the selected strategy. After executing the action, the environment changes, the model enters a new state, and obtains the corresponding reward. Continuously repeat this process to form a series of sequences of states, actions, and rewards. Use this data to update the action value function Q(s, a) and the strategy according to a specific algorithm.

[0062] Determine the termination condition: When the preset number of training steps is reached, or the reward value tends to be stable, or the update amplitude of the action value function Q(s, a) is less than a certain set threshold, the training ends.

[0063] S300. Modulate the personalized music parameters using multi-modal encoding technology; among them, the multi-modal encoding technology includes: Acoustic wave modulation: Embed ultrasonic subconscious instructions with a frequency higher than 20000Hz in the carrier frequency and transmit them through bone conduction; Optical frequency synchronization: Generate pulsed light stimuli with the same frequency as the music rhythm and in the 40Hz gamma wave band to activate the visual cortex; Olfactory binding: Associate specific fragrances with music segments to form multi-sensory conditioned reflexes.

[0064] Specifically, the acoustic wave modulation includes: Let the carrier frequency be C f , embed ultrasonic subconscious instructions with a frequency higher than 20000Hz in the carrier frequency, and let the instruction frequency be l f , and transmit through bone conduction. The modulated acoustic wave is S m = Modulate(C f , l f)。Optical frequency synchronization includes: generating pulsed light stimulation with the same frequency as the music rhythm and in the 40Hz gamma wave frequency band. Let the music rhythm frequency be R f , and the optical pulse frequency be L f . When R f = L f and L f ∈ 40HZ, optical frequency synchronization is achieved, that is, S y = (R f = L f ). Olfactory binding includes: setting a specific fragrance set as F S , and a music segment set as M p . Establishing an association relationship B = Relate(F S , M p ), forming a multi-sensory conditioned reflex..

[0065] S400. Dynamically adjust the coding strategy according to real-time feedback, and output subconscious development music adapted to the user's current state. The output subconscious development music includes an explicit music layer and an implicit coding layer, where: the explicit music layer meets the requirements of melody fluency and artistic aesthetics; the implicit coding layer realizes subconscious information implantation through reverse speech embedding, binaural beat difference, and frequency following response technology.

[0066] Specifically, step S400 may include:

[0067] S410. Establish a physiological response and music parameter error function with cortisol concentration change and alpha wave ratio as feedback indicators. Let the cortisol concentration change be C and the alpha wave ratio be A p as feedback indicators, and the feedback indicator set F = (C, A p );

[0068] S420. Optimize the harmony complexity, rhythm entropy value, and semantic coding density of subsequent music through an adaptive algorithm to form a closed-loop regulation, and construct a physiological response and music parameter error function E r = Error(F, P a ). According to the value of the error function E r , optimize the harmony complexity H a , rhythm entropy value R c , and semantic coding density S e of subsequent music through the adaptive algorithm A d , that is, (H c , R e , S d ) = A d (E r ), forming a closed-loop regulation and continuously optimizing the generation effect of subconscious development music.

[0069] More specifically, when continuously optimizing the generation effect of the subliminal development music, the physiological response and the music parameter error function:

[0070]

[0071] Among them, C1 is the current change in cortisol concentration, A1 is the current proportion of alpha waves, H1 is the current harmony complexity, R1 is the current rhythm entropy value, S1 is the current semantic coding density, ω1, ω2, ω3 are weight coefficients, and ω1 + ω2 + ω3 = 1, C ideal 、A ideal 、H ideal 、R ideal 、S ideal are respectively the change in cortisol concentration, the proportion of alpha waves, the harmony complexity, the rhythm entropy value, and the semantic coding density under the ideal state;

[0072] Adaptive algorithm A a When optimizing the music parameters according to the error function E1, the adaptive algorithm A a is expressed as:

[0073]

[0074] Among them, η1, η2, and η3 are respectively the learning rates of harmony complexity, rhythm entropy value, and semantic coding density, which are used to control the step size of parameter adjustment. By continuously repeating steps S410 and S420, that is, continuously calculating the error function E r and adjusting the music parameters according to its value to form a closed-loop control, continuously optimizing the generation effect of the subliminal development music, so that the generated music can better adapt to the user's current state and achieve effective development of the user's subconscious.

[0075] Example 1

[0076] The present invention also provides an example of actual simulation using the method of the present invention. The simulation object is User 1:

[0077] Detect the alpha waves of the user:

[0078] The system collects EEG data in real time through an EEG device worn on the head of User 1. Through signal processing and analysis algorithms, the current brain wave situation of User 1 is identified. At a certain moment, the system detects that the proportion of alpha waves of User 1 is 30%, while in the normal relaxed state, the proportion of alpha waves of User 1 is usually between 40% and 50%. This indicates that User 1 has not reached its ideal relaxed state at this moment.

[0079] Regulate parameters:

[0080] Analyze the state space: In addition to the alpha wave ratio data, the system also collected that the current skin conductivity of User One is slightly higher than the normal level, indicating a certain degree of tension; the eye movement trajectory shows that User One's attention is relatively scattered. From the perspective of subconscious stratification, the current dominant level tends to be the emotional memory layer, and it may be that some negative emotion memories related to recent work pressure are affecting him. At the same time, the parameters of the currently playing music are: the rhythm intensity coefficient is moderate, the melodic semitone offset is small, the harmony complexity is medium, the rhythm entropy value is within the normal range, and the semantic coding density is average.

[0081] Determine the adjustment direction based on the reward function: According to the reward function, the system hopes to improve the alpha wave ratio of User One, reduce the skin conductivity, enhance the degree of attention concentration, and at the same time improve the overall effect of the music to fit the subconscious state dominated by the current emotional memory layer. Since the alpha wave ratio is lower than the ideal range and the skin conductivity is on the high side, it is necessary to adjust the music parameters to promote relaxation.

[0082] Select actions through the strategy: Adopt the ε-greedy strategy. Assume that ε takes the value of 0.2 at this time. With a 20% probability, the system randomly selects adjustment actions from the action space; with an 80% probability, the system selects the optimal action according to the current state and the action value function. In this adjustment, the system calculated according to the action value function that in order to increase the alpha wave ratio, it is necessary to reduce the rhythm intensity coefficient, increase the melodic semitone offset to make the melody more variable, and at the same time appropriately increase the harmony complexity to create a soothing atmosphere. For the rhythm entropy value, appropriately reduce it to reduce the rhythm complexity and make it easier for User One to relax. In terms of semantic coding density, adjust it to more positive and soothing semantic information to match the relaxation needs.

[0083] Update the music parameters: The system reduces the rhythm intensity coefficient from 0.6 to 0.4, increases the melodic semitone offset from 2 to 5, increases the harmony complexity from level 5 to level 7, reduces the rhythm entropy value from 0.5 to 0.3, and adjusts the semantic coding density to increase the frequency of positive and relaxing words according to the selected actions. Then, the system applies the new music parameters to the currently playing music and continuously monitors the changes in the physiological data and subconscious state of User One for further adjustment in the future.

[0084] Example Two

[0085] The present invention also provides an example of actually simulating using the method of the present invention, and the simulation object is User Two:

[0086] Determine the state space:

[0087] Physiological data: The HRV (heart rate variability) data of User 2 obtained through a wearable device shows that their heart rate fluctuates frequently, reflecting a certain degree of psychological stress; the skin conductivity data is slightly high, indicating that the body is in a mild stress state; the eye movement trajectory shows that User 2's attention is not very concentrated and the line of sight movement is relatively scattered.

[0088] Subconscious stratification: Based on the growth experience, psychological test data of User 2 collected previously and the current physiological responses, it is judged that the dominant subconscious level of User 2 at this time is the emotional memory layer, which may be caused by a setback experience in recent work, leading to this state.

[0089] Past music parameters: The rhythm intensity coefficient of the previously played music is 0.5, the melodic semitone deviation is 3, the harmony complexity is at a medium level, the rhythm entropy value is 0.4, and the semantic coding density is average. The adaptability of these past parameters to the current state of User 2 is not ideal and has not effectively alleviated their tension and improved their attention.

[0090] Define the action space:

[0091] Rhythm intensity coefficient: The adjustable range is 0 - 1, and the current value is 0.5. Considering the tense state of User 2, it may be necessary to reduce the rhythm intensity to create a soothing atmosphere, and it can be tried to adjust it to 0.3.

[0092] Melodic semitone deviation: The current value is 3. To increase the soothing feeling and attractiveness of the melody, it can be increased to 5 to make the melody more variable and fluent.

[0093] Binaural beat frequency difference: Usually adjusted between 0 - 20 Hz. To promote the relaxation of User 2's brain, the binaural beat frequency difference is set to 8 Hz to stimulate the brain to generate relaxing brain waves.

[0094] Harmony complexity: Adjusted from level 1 - 10, the current level is 5. It can be appropriately increased to level 7 to enrich the music layers and enhance the soothing effect.

[0095] Rhythm entropy value: The value range is 0 - 1, the current value is 0.4, and it can be reduced to 0.2 to reduce the rhythm complexity and make it easier for User 2 to concentrate.

[0096] Semantic coding density: The frequency of positive and soothing words can be adjusted. Currently, it is average. The frequency of positive words can be increased to enhance the semantic coding density to better influence the subconscious of User 2.

[0097] Set the reward function:

[0098] Physiological response reward: If the cortisol concentration of User 2 decreases by 10%, the reward is +5 points; if the proportion of alpha waves increases by 5%, the reward is +3 points. Since reducing the cortisol concentration is more crucial for relieving stress, a higher reward score is given.

[0099] Music effect reward: Invite User 2 to rate the melodic fluency and artistic aesthetic of the music on a scale of 1 - 10. If the rating reaches 8 or above, a reward of +4 points is given; if it is between 6 - 7, a reward of +2 points is given.

[0100] Comprehensive reward calculation: Add the physiological response reward and the music effect reward to obtain the comprehensive reward score. If the comprehensive reward score is positive after music adjustment, it indicates that the adjustment direction is correct; if it is negative, readjustment is required.

[0101] Selection strategy:

[0102] Adopt the ε - greedy strategy, assuming ε is set to 0.2. With a 20% probability, randomly select an adjustment action from the action space. For example, randomly decide to adjust the rhythm intensity coefficient to 0.4. This is to explore the possibility of new parameter combinations. With an 80% probability, select the optimal action according to the current state and the action - value function. After calculation, the optimal action is to increase the melodic semitone offset to 6 and the harmony complexity to level 8.

[0103] Estimate the action - value function:

[0104] Use a neural network to estimate the action - value function Q(s,a). The neural network takes the current state s (including the physiological data of User 2, the subconscious stratification, and the past music parameters) as input. Through a large amount of previous training, it outputs the Q - value corresponding to each action a (such as actions like adjusting the rhythm intensity coefficient, melodic semitone offset, etc.) in this state. For example, for the action of adjusting the rhythm intensity coefficient to 0.3, the Q - value output by the neural network is 0.6, indicating that the expected value of the future cumulative reward after taking this action in the current state is 0.6.

[0105] Conduct training:

[0106] The model starts from the initial state and selects actions according to the selected strategy. Suppose the actions of increasing the melodic semitone offset to 6 and the harmony complexity to level 8 are selected according to the strategy. After executing the actions, the newly generated music is played to User 2.

[0107] During User 2's listening to the music, the system continuously monitors the changes in their physiological data and at the same time invites User 2 to evaluate the music effect. Suppose User 2's cortisol concentration has decreased by 8% and the proportion of α - waves has increased by 3%. The evaluation of the melodic fluency of the music is 7 points and the evaluation of artistic aesthetic is 7 points. According to the reward function calculation, the reward score obtained for this action is: Physiological response reward (reward of +4 points for a cortisol decrease of 8% and +2 points for an α - wave increase of 3%) + Music effect reward (reward of +2 points for a rating of 7 points), totaling +8 points.

[0108] The model updates the action-value function Q(s,a) and the policy according to the new state (the new physiological data, subconscious state, and new music evaluation of User 2) and the obtained rewards, following a specific algorithm (such as the Q-learning algorithm). For example, for the action of increasing the melodic semitone offset to 6, its corresponding Q value may be updated from 0.5 to 0.7 because this adjustment brings better reward feedback.

[0109] Determine the termination condition:

[0110] The preset number of training steps is 50 times. When the model completes 50 adjustments and feedbacks, if the reward value tends to be stable, such as the reward score fluctuates within ±1 point after 5 consecutive adjustments, or the update amplitude of the action-value function Q(s,a) is less than 0.05, the training ends. The determined music parameters at this time, such as the rhythm intensity coefficient of 0.3, the melodic semitone offset of 6, the binaural beat frequency difference of 8 Hz, the harmony complexity of 8 levels, the rhythm entropy value of 0.2, and the semantic coding density as the high-frequency state of positive words after adjustment, are the subconscious development music parameters finally generated for User 2 to suit their current state.

[0111] Based on these parameters, generate the music waveform:

[0112] Rhythm waveform: The rhythm intensity coefficient of 0.3 means that the rhythm is relatively gentle. Taking 4 / 4 time as an example, the waveform amplitude at the starting point of each beat is small, the duration is moderate, and the transition between adjacent beats is relatively smooth, forming a soothing and stable rhythm feeling.

[0113] Melody waveform: The melodic semitone offset is 6, and the melody waveform shows relatively rich undulations. In terms of pitch, the interval span between adjacent notes increases, making the melody line more dynamic and fluent. From the waveform diagram, the height and position changes of the wave peaks and valleys are diverse, simulating a beautiful and changing melody.

[0114] Harmony waveform: The harmony complexity is 8 levels, and the harmony waveform is composed of multiple sine waves with different frequencies and amplitudes superimposed. These sine waves represent different harmony notes, and they are intertwined with each other to form a complex and harmonious waveform. At some moments, the superposition of the waveforms will produce obvious peaks, corresponding to the climax part of the harmony; at other moments, the waveform is relatively stable, creating a harmonious background atmosphere.

[0115] Binaural beat waveform: The binaural beat frequency difference is 8 Hz, and sound waves with different frequencies are output through the left and right channels respectively. For example, the left channel outputs a sound wave with a frequency of 200 Hz, and the right channel outputs a sound wave with a frequency of 208 Hz. In the user's auditory system, these two frequency sound waves interfere with each other to generate a low-frequency modulation waveform of 8 Hz, stimulating the brain to produce corresponding brain wave changes and promoting relaxation.

[0116] The present invention has the following advantages:

[0117] Precise stratification and efficient coding: By comprehensively collecting real-time physiological data of users and subconscious stratification information, the dominant subconscious level is accurately judged. This solves the problem of inaccurate subconscious stratification recognition in the past, enabling targeted coding when generating music according to the characteristics of different levels. For example, for the instinctive layer, specific rhythms and timbres can be used to match the natural physiological rhythms and primitive emotional responses of the human body; for the emotional memory layer, relevant melody and harmony elements can be selected based on the user's past experiences. This precise stratified coding greatly improves the accuracy and depth of the effect of music on the subconscious, making music more effectively act on all levels of the user's subconscious.

[0118] Personalized customization experience: Reflecting the user's unique growth experiences, cultural backgrounds, genetic factors, etc. in the state space, the model can customize subconscious development music for users based on this personalized information by adjusting music parameters in the action space. For example, according to the user's physiological data and subconscious state, parameters such as the rhythm intensity coefficient and the melodic semitone offset are precisely adjusted to meet the diverse individual needs of users, solving the problem of lack of personalized customization in traditional technologies and significantly enhancing the user's music experience and subconscious development effect.

[0119] Optimizing the integration of music and coding: This solution enriches the integration methods of music and subconscious coding. In the action space, various music parameter adjustments are covered, including harmony complexity, rhythm entropy value, semantic coding density, etc., making the music expression more diverse and enabling more complex and in-depth subconscious coding. At the same time, by continuously monitoring the user's physiological responses and music effects and using the feedback information of the reward function, the music parameters are dynamically adjusted in real time, solving the problem that music cannot be dynamically adjusted according to the real-time feedback and state of the listener during playback. For example, when the user shows emotional fluctuations, the music parameters are optimized in a timely manner to improve the melodic fluency and artistic aesthetics and enhance the subconscious development effect of the music.

[0120] Scientific verification and approaching standards: During the training process, through a clear reward function and a large amount of data interaction, the reinforcement learning model is continuously optimized, making the relationship between music generation and the subconscious development effect of users more verifiable. As the model is continuously trained and improved, it gradually approaches the establishment of scientific music generation standards. For example, according to the feedback of the user's physiological responses and music effects, the action value function and strategy are continuously adjusted, making the generated music more stable and measurable in terms of subconscious development effect, helping to solve the problems of lack of scientific verification and unified standards in the industry and enhancing the overall scientific nature and effectiveness of subconscious development music generation technology.

[0121] An electronic device according to an embodiment of the present disclosure includes a memory and a processor. The memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, and the computer program products may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0122] The processor may be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In an embodiment of the present disclosure, the processor is used to run the computer-readable instructions stored in the memory, so that the electronic device executes all or part of the steps of the subconscious development music generation method based on subconscious hierarchical coding in the foregoing embodiments of the present disclosure.

[0123] Those skilled in the art should understand that, in order to solve the technical problem of how to obtain good user experience effects, known structures such as communication buses and interfaces may also be included in this embodiment, and these known structures should also be included in the protection scope of the present disclosure.

[0124] As Figure 2 FIG. is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure. It shows a schematic structural diagram of an electronic device suitable for implementing the electronic device in the embodiments of the present disclosure. Figure 2 The shown electronic device is only an example, and should not impose any limitation on the functions and usage scope of the embodiments of the present disclosure.

[0125] As Figure 2 As shown, the electronic device may include a processor (such as a central processing unit, a graphics processing unit, etc.), which may perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) or a program loaded from a storage device into a random access memory (RAM). In the RAM, various programs and data required for the operation of the electronic device are also stored. The processor, ROM, and RAM are connected to each other through a bus. An input / output (I / O) interface is also connected to the bus.

[0126] Generally, the following devices may be connected to the I / O interface: an input device including, for example, a sensor or a visual information acquisition device; an output device including, for example, a display screen; a storage device including, for example, a magnetic tape, a hard disk, etc.; and a communication device. The communication device may allow the electronic device to communicate wirelessly or wiredly with other devices (such as edge computing devices) to exchange data. Although Figure 2An electronic device having various devices is shown, but it should be understood that it is not necessary to implement or have all the shown devices. Instead, more or fewer devices may be implemented or had.

[0127] In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains program codes for performing the methods shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network through a communication device, or installed from a storage device, or installed from a ROM. When the computer program is executed by a processor, all or part of the steps of the subconscious development music generation method based on subconscious hierarchical coding according to the embodiments of the present disclosure are performed.

[0128] For a detailed description of this embodiment, reference may be made to the corresponding descriptions in the foregoing embodiments, and details are not described herein again.

[0129] A computer-readable storage medium according to an embodiment of the present disclosure stores non-temporary computer-readable instructions. When the non-temporary computer-readable instructions are run by a processor, all or part of the steps of the subconscious development music generation method based on subconscious hierarchical coding according to the foregoing embodiments of the present disclosure are performed.

[0130] The above-mentioned computer-readable storage medium includes but is not limited to: optical storage media (such as CD-ROMs and DVDs), magneto-optical storage media (such as MOs), magnetic storage media (such as magnetic tapes or external hard drives), media with built-in rewritable non-volatile memories (such as memory cards), and media with built-in ROMs (such as ROM cartridges).

[0131] For a detailed description of this embodiment, reference may be made to the corresponding descriptions in the foregoing embodiments, and details are not described herein again.

[0132] The basic principles of the present disclosure have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, benefits, effects, etc. mentioned in the present disclosure are only examples and not limitations, and it cannot be considered that these advantages, benefits, effects, etc. are essential for each embodiment of the present disclosure. In addition, the above-mentioned specific details are only for the purposes of illustration and facilitating understanding, rather than limitations, and the above details do not limit the present disclosure to necessarily adopt the above specific details for implementation.

[0133] In this disclosure, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. The block diagrams of devices, apparatuses, equipment, and systems involved in this disclosure are only illustrative examples and do not intend to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any way. Words such as "comprising", "including", "having", etc. are open-ended words, meaning "including but not limited to", and can be used interchangeably with each other. The words "or" and "and" used herein refer to the word "and / or", and can be used interchangeably with each other, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to", and can be used interchangeably with each other.

[0134] In addition, as used herein, "or" in a list of items beginning with "at least one" indicates a disjunctive list, so that for example, a list of "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Further, the phrase "exemplary" does not mean that the examples described are preferred or better than other examples.

[0135] It should also be noted that in the systems and methods of this disclosure, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent solutions of this disclosure.

[0136] Various changes, substitutions, and alterations to the technologies described herein can be made without departing from the teachings defined by the appended claims. In addition, the scope of the claims of this disclosure is not limited to the specific aspects of the processes, machines, manufactures, compositions of matters, means, methods, and acts described above. Current or later-developed processes, machines, manufactures, compositions of matters, means, methods, or acts that perform substantially the same function or achieve substantially the same result as the corresponding aspects described herein can be utilized. Accordingly, the appended claims include such processes, machines, manufactures, compositions of matters, means, methods, or acts within their scope.

[0137] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this disclosure. Therefore, this disclosure is not intended to be limited to the aspects shown herein, but to the broadest scope consistent with the principles and novel features disclosed herein.

[0138] The foregoing description has been presented for purposes of illustration and description. In addition, the description is not intended to limit embodiments of the present disclosure to the form disclosed herein. Although several example aspects and embodiments have been discussed above, those of ordinary skill in the art will recognize some variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A subconscious development music generation method based on subconscious hierarchical coding, characterized in that It includes the following steps: Obtain the user's subconscious hierarchical features, and construct a subconscious hierarchical coding model based on the user's subconscious hierarchical features; Collect the user's real-time physiological and behavioral data, and input it into the subconscious hierarchical coding model to generate personalized music parameters; Modulate the personalized music parameters using multimodal coding technology; Dynamically adjust the coding strategy according to real-time feedback, and output subconscious development music adapted to the user's current state.

2. The subconscious development music generation method based on subconscious hierarchical coding according to claim 1, characterized in that: The user's subconscious hierarchical features include: Instinct layer: used to show the δ / θ / α brain wave frequencies, survival instincts and primitive desires, corresponding to the activation state of the limbic system; Emotional memory layer: used to show the emotional imprints, emotional patterns and related memories formed by past experiences, corresponding to the activities in the emotional association areas of the prefrontal lobe and temporal lobe; Collective layer: used to show values, collective subconsciousness and cultural symbols, corresponding to the activation of the default mode network.

3. The subconscious development music generation method based on subconscious hierarchical coding according to claim 1, characterized in that: The personalized music parameters include rhythm type, melody trend, harmonic tension, semantic coding intensity, and form a definite mapping relationship with the user's genetic characteristics, growth experiences, and real-time EEG data.

4. The subconscious development music generation method based on subconscious hierarchical coding according to claim 1, characterized in that: The obtaining of the user's subconscious hierarchical features and the construction of the subconscious hierarchical coding model include: Use EEG brain wave devices and psychological scales to collect the user's congenital physiological data and acquired behavioral data; Classify the user's subconscious levels through machine learning algorithms, and establish a mapping matrix between music elements and subconscious levels; Set the sound wave frequency thresholds and emotional semantic rules for each layer of coding according to psychological theories.

5. The subconscious development music generation method based on subconscious hierarchical coding according to claim 4, characterized in that: The collecting of the user's real-time physiological and behavioral data and the generation of personalized music parameters include: Obtain the user's HRV heart rate variability, skin conductivity, and eye movement trajectories in real time through wearable devices; Input the real-time data into the trained reinforcement learning model, and output the rhythm intensity coefficient, melody semitone offset, and binaural beat frequency difference that match the current dominant subconscious level.

6. The subconscious development music generation method based on subconscious stratification coding according to claim 1, characterized in that: The multimodal coding technology includes: Sound wave modulation: Embed ultrasonic subconscious instructions with frequencies higher than 20000Hz in the carrier frequency and transmit them through bone conduction; Optical frequency synchronization: Generate pulsed light stimuli with the same frequency as the music rhythm and in the 40Hz gamma wave band to activate the visual cortex; Olfactory binding: Associate specific fragrances with music segments to form multi-sensory conditioned reflexes.

7. The subconscious development music generation method based on subconscious hierarchical coding according to claim 1, wherein: The dynamically adjusting the coding strategy according to real-time feedback includes: Establish a physiological response and music parameter error function with cortisol concentration change and α wave ratio as feedback indicators; Optimize the harmony complexity, rhythm entropy value, and semantic coding density of subsequent music through an adaptive algorithm to form a closed-loop regulation.

8. The subconscious development music generation method based on subconscious hierarchical coding according to claim 1, characterized in that: The output subconscious development music includes a dominant music layer and a recessive coding layer, where: The dominant music layer meets the requirements of melody fluency and artistic aesthetics; The recessive coding layer realizes the implantation of subconscious information through reverse speech embedding, binaural beat difference, and frequency following response technology.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the subconscious development music generation method based on subconscious hierarchical coding according to any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a computer to execute the subconscious development music generation method based on subconscious hierarchical coding according to any one of claims 1-8.

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