Multi-tone mixed playing intelligent guitar system

Abstract

The invention relates to the technical field of audio processing and musical instruments, and discloses a multi-timbre mixed playing intelligent guitar system, which comprises an acquisition module for acquiring a user music instruction and performing rapid identification and translation by using a preset multi-track synchronization algorithm; the adjusting module is used for adjusting the tone parameters corresponding to the audio tracks according to the recognition translation result so as to optimize the multi-track recording synchronization precision; the monitoring module monitors the signal intensity of the pickup in real time and stabilizes audio input through a dynamic compensation technology; and the conversion module is used for realizing the minimization of the tone distortion problem in the complex chord conversion process based on the optimized digital signal processing algorithm. According to the intelligent guitar system for multi-timbre mixed playing, different timbre parameters are intelligently regulated and controlled to solve the problem of multi-track recording synchronization precision, so that the matching of each timbre in multi-track recording is more accurate and harmonious, the overall recording quality is finally improved, and audio works are more perfect and harmonious.

Description

Technical Field

[0001] The present application relates to the technical field of audio processing and musical instruments, and in particular to an intelligent guitar system for multi-timbre mixed performance. Background Art

[0002] An intelligent guitar system for multi-timbre mixed performance is an innovative music performance device that aims to provide a more colorful and flexible guitar performance experience by integrating multiple intelligent technologies. The system has the ability to switch between multiple timbres and output simultaneously, and can simulate different instrument effects and performance scenarios. In order to achieve efficient and accurate multi-timbre mixed performance, this intelligent guitar faces several key technical challenges. The first is how to intelligently control different timbre parameters to ensure high synchronization accuracy during multi-track recording and avoid sound inconsistency caused by slight time differences between different tracks. The second is to quickly and accurately identify and translate the user's music instructions to solve possible responses. The delay problem is to ensure that the user experience is smooth and seamless. The third is to dynamically adjust the signal strength received by the pickup to maintain a stable audio input level during the performance. The fourth is to solve the timbre distortion caused by complex chord transitions by optimizing the digital signal processing algorithm to ensure that the purity of the sound of each play is not damaged. The last is how to design the system's internal timbre library update mechanism so that it can continuously introduce new timbres to meet the musicians' ever-changing creative needs while maintaining compatibility and ease of use. Solving these problems requires not only interdisciplinary comprehensive technological innovation, but also a deep understanding of the actual needs of musicians, so that the smart guitar can become a truly first-class music creation tool. Summary of the invention

[0003] In order to solve the problems raised by the above background technology, the present application provides an intelligent guitar system for multi-timbre mixed performance.

[0004] The present application provides an intelligent guitar system for multi-timbre mixed performance, which adopts the following technical solutions: An intelligent guitar system for multi-timbre mixed performance, comprising: The acquisition module obtains the user's music instructions and uses the preset multi-track synchronization algorithm for rapid recognition and translation; The adjustment module adjusts the timbre parameters corresponding to each audio track according to the recognition and translation results to optimize the synchronization accuracy of multi-track recording; Monitoring module, which monitors the pickup signal strength in real time and stabilizes the audio input through dynamic compensation technology; The conversion module minimizes the timbre distortion problem during complex chord conversion based on an optimized digital signal processing algorithm.

[0005] Preferably, minimizing the timbre distortion problem during complex chord conversion based on the optimized digital signal processing algorithm includes: Establish a set of dynamic frequency domain filtering parameter models adapted to the complex chord conversion process; Real-time detect the audio input frequency range and adjust the corresponding digital filter according to this frequency; If a frequency offset occurs, it is adjusted through the following formula: If f >= fc, then use A * sin(B), where A represents the volume gain factor and B is the phase adjustment amount related to time; This method can ensure smooth transition of timbre without distortion. Evaluate the processed signal in real time and automatically optimize the algorithm to reduce any potential remaining unnatural changes.

[0006] Preferably, the further description based on the dynamic frequency domain filtering parameter model in the steps is specifically defined as: Set the personalized frequency band adjustment threshold Pth according to user preferences; Select the optimal filter parameter Pi within the frequency range Fi of each track audio signal to ensure synchronization between different tracks; Calculate the weighted average filtering effect Q = ΣWi*Pi / n, where Wi is the weight coefficient of each track and n is the number of tracks; Apply the finally optimized filtering parameters to the data stream of the next time slot to continuously improve the synchronization accuracy.

[0007] Preferably, more detailed regulations are made for the calculated weighted average filtering effect: Use historical performance data analysis to obtain the common problem pattern Hpm; Extract the reference benchmark parameter Rbp representing a specific performance situation from the model training data; When encountering a similar situation, use conditional judgment to decide whether to adjust: If the matching degree of Hpm > M, then directly overwrite the original settings with the pre-saved Rbp parameters; M here refers to the matching degree threshold; Iteratively update the best parameter set through the feedback learning mechanism to improve the overall performance.

[0008] Preferably, further definitions are made in the training and adaptive adjustment of the model: Configure exclusive basic filtering templates Ft for different types of musical instruments; Fine-tune and calibrate in combination with real-time environmental factors Fenv such as humidity and temperature on the influence of sound characteristics; Follow the formula G = (A * Ft + B * Fenv) / C to generate an integrated control strategy for immediate use, where G represents the global filtering matrix and C is a normalization factor; Regularly evaluate and archive the best configurations obtained in various performance environments for future reference and invocation to cope with the uncertainties brought by the changing recording environment.

[0009] Preferably, in order to better adapt to the diverse current creative scenarios, an intelligent recognition mechanism is specially added: Identify the music instruction content Ci and intention Iu input by the user, and quickly match the closest professional music score database resources according to Iu; Design a special module to analyze the complex multi-track synchronization algorithm logic to support high-fidelity restoration of the composition intention; Before executing each new instruction, perform a preloading operation Lpd(Ci, Iu), that is, load the information that may be used in advance into the cache according to certain rules to speed up the response speed; If the pre-estimated time delay Td < Tt (the maximum tolerable delay time that the user can accept), it can ensure timely and accurate feedback of the user's needs and avoid response delay affecting the experience.

[0010] Preferably, the technical solution for quickly identifying and translating user music instructions is as follows: Introduce a speech analysis engine and cooperate with artificial intelligence technology to perform more accurate language parsing work; Support the recognition of multiple dialects or languages to expand the applicable population base; Use conditional judgment statements: If the instruction type Dt is related to melody construction categories, enable advanced editing tools to help users improve the song structure, where Dt represents the command classification identifier; Carry out regular updates of the dictionary table and expansion of the database to capture the latest music style trends in order to keep up with the development rhythm of the times and continuously optimize the user experience.

[0011] Preferably, several key components are added to the intelligent language processing module: The context-aware component ContextAware driven by deep learning algorithms; The core NLP engine CoreNLP can deeply understand the true intention behind the user's expression; Use the decision tree DecisionTree to screen and filter out irrelevant noise data Nsd, and only retain the core useful part CoreUse. Obtain more pure and effective instruction information through the formula CoreUse = InputNsd process; Build the personalized service recommendation subsystem ServiceReco to provide targeted product function guidance for new and old users based on the experience accumulated from previous interactions.

[0012] Preferably, specific implementation steps are stipulated for the core NLP engine: Provide a multi-level corpus annotation framework LayeredAnnotation as the underlying support facility; Construct a rich dictionary table DictionaryOfTerms for professional terms in the target domain to facilitate a more fine-grained understanding; Apply the graph convolutional network GraphConvoNet to analyze the syntactic structure dependencies to form a more reasonable semantic parsing path Pathway; If it is found that the length L of the Pathway is greater than the specified limit value LimitLength, it indicates that there may be a situation of overly deep branches or abnormally complex structures, and the manual review link needs to be triggered. Here, LimitLength is a specified threshold value used to evaluate the path complexity to avoid the problem of decreased processing efficiency caused by excessive complexity.

[0013] Preferably, more details about the personalized service recommendation subsystem are as follows: Set up an interest profile InterestProfile to store the user's preference settings; Develop an efficient search index system HiSearchIndex to improve the retrieval efficiency and service quality; Use the reinforcement learning model ReinforceLrnMdl to reorder the recommendation results RankResult to make it more in line with personal needs and preferences; If the user's behavior changes exceed the predefined ratio ThresholdPct, such as events like purchasing a new model guitar, etc., immediately trigger a full refresh of the InterestProfile to maintain the freshness and pertinence of the recommendation list. Here, ThresholdPct is a key indicator measuring the degree of importance conversion; Some innovation points are proposed around the design of the efficient search index: Integrate the content publicly shared on social media platforms SocialPlatforms to enrich the local material pool ResourcePool; Introduce the dimensions of geographical location Location and time period Period to refine the clustering strategy; Rank various search sources SortingSource according to the priority Priority, for example, official guide > evaluations by well-known musicians > public opinions widely recognized by the audience; Perform a comprehensive evaluation on the sorted content combination. ScoreCombine = W1*S1 +... + Wn*Sn. Each weight Wi reflects the importance of the corresponding item. Based on this, select the most valuable answers and tutorials for the end-users' reference. Here, ScoreCombine represents the comprehensive evaluation score, and Sx is the value of each evaluation index; Optimize the material recommendation algorithm by combining geographical coordinate positioning and spatio-temporal dimension information: Implement the fusion analysis of user active periods ActiveSession and regional hotspots Hotspots to create a unique content browsing environment CustomizedView for each customer; Introduce a portable device synchronization mechanism SyncDevice for out-of-town performance occasions to ensure convenient access to high-quality audio and video materials regardless of location; The interest group matching algorithm GeoGroupMatch based on geographical location association makes it easier and more convenient for like-minded people to interact with each other; If the activity participation rate ActivityLevel > AlLimit (the preset upper limit of the activity rate) in a certain area, automatically push information about ongoing or upcoming music festivals nearby to attract more attention and support. Here, AlLimit indicates the acceptable number limit within an ideal range.

[0014] In summary, this application includes at least one of the following beneficial technical effects: This intelligent guitar system for multi-timbre hybrid performance intelligently regulates different timbre parameters to solve the problem of multi-track recording synchronization accuracy, enabling the cooperation of each timbre in multi-track recording to be more precise and coordinated, ultimately improving the overall recording quality and making the audio works more perfect and harmonious.

[0015] This intelligent guitar system for multi-timbre hybrid performance solves the problem of response delay by quickly identifying and translating the music instructions input by the user, shortening the time difference between user operations and system feedback, making the music creation or performance process more smooth and natural, and enhancing the user experience.

[0016] This intelligent guitar system for multi-timbre hybrid performance dynamically adjusts the signal intensity received by the pickup to solve the problem of unstable audio input during performance, ensuring that the audio input always remains in an appropriate state during the performance, avoiding situations such as sudden changes in sound volume or interruptions, and guaranteeing the stability and reliability of audio acquisition.

[0017] The intelligent guitar system for multi-timbre hybrid performance optimizes the digital signal processing algorithm to solve the timbre distortion problem generated instantaneously during complex chord transitions, enabling the timbre during complex chord transitions to be purer and more accurate, enhancing the expressiveness and appeal of music, and making the performance effect more outstanding.

[0018] The intelligent guitar system for multi-timbre hybrid performance designs the internal timbre library update mechanism of the system to solve the problem of mismatch between existing timbre resources and innovative music requirements, enabling the music system to keep up with the trend of music innovation, providing users with more timbre resources that meet the creative needs, and inspiring the inspiration and creativity of music creation. Brief Description of the Drawings

[0019] Figure 1 A flowchart of an intelligent guitar system for multi-timbre hybrid performance. Detailed Implementation Modes

[0020] The following details the implementation modes of the present application, and examples of the implementation modes are shown in the accompanying drawings.

[0021] In the description of this specification, the description referring to terms such as "certain implementation modes", "one implementation mode", "some implementation modes", "illustrative implementation modes", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the implementation mode or example are included in at least one implementation mode or example of the present application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same implementation mode or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more implementation modes or examples.

[0022] The embodiments of the present application disclose an intelligent guitar system for multi-timbre hybrid performance. Next, referring to the attached Figure 1 , an intelligent guitar system for multi-timbre hybrid performance of the present invention is described. First, the user's music instructions are obtained and quickly recognized and translated using a preset multi-track synchronization algorithm. The system captures the user's pronunciation or gesture actions through a built-in high-sensitivity microphone, and then quickly recognizes the instructions with the help of a high-performance processing chip and parses them into specific music elements such as melody, scale, beat, etc. These information are further translated into internal commands of the system to control the various functions of the guitar to address the problem of response delay. This system introduces a deep learning module to perform a large number of sample trainings on historical inputs, establish an accurate and efficient voice and action recognition model, ensure strong real-time performance and high recognition accuracy, so that no matter how the user sends music instructions, the system can instantly understand and feedback to the performance device. In one example, when a guitarist uses a voice command, just say the command "C major arpeggio, speed 120", and the system can immediately respond and switch to the required mode.

[0023] In order to enable different timbre parameters to be intelligently controlled and solve the challenge of multi-track recording synchronization accuracy, the process closely relies on the user intention data obtained above. According to the selected music style and arrangement plan, specific sound effect parameter settings are assigned to each channel, such as reverberation time, chorus effect level or vibrato frequency. The basis for adjusting specific parameters is the current note's position in the melody, its rhythm arrangement in the entire paragraph, and the expected emotional expression. For complex music arrangement, such as a symphony, it is necessary to embed multiple virtual instrument timbres such as piano, flute, and violin. At this time, the intelligent algorithm is responsible for coordinating the delay and phase differences between the channels to ensure that the final synthesized sound remains natural and harmonious without causing confusion on the timeline. This step is also applicable to simulating the role change of electric guitar in song accompaniment. When the player switches from the main melody to the background decorative lines, the corresponding effect settings are also seamlessly changed to match the new scene.

[0024] At the same time, in order to let users feel stable and pure sound output, it is necessary to dynamically adjust the signal strength of the pickup to overcome the degradation of audio input quality caused by changes in the external environment. The system has a built-in specially designed sensor to accurately monitor the input sound pressure level and is equipped with an advanced algorithm that can quickly calculate the most appropriate amplification compensation factor to maintain a good signal-to-noise ratio even under intense exercise. For example, if the performance environment suddenly becomes noisy, the pickup gain will automatically increase to highlight the performer's main voice; on the contrary, if the surroundings are too quiet and the sound itself is relatively abrupt, then the gain will be appropriately reduced to avoid excessive harshness, thereby achieving auditory enjoyment that is always within the high-quality range. In addition, this dynamic adjustment also helps solve the risk of overload that may occur during the performance, protects the safe working state of electronic components, and ensures a long and reliable service life of the entire device.

[0025] When faced with complex chord changes, the digital signal processor DSP will encounter certain difficulties, that is, the timbre will be temporarily distorted, affecting the integrity and pleasantness of the music. For this reason, the development team has carefully designed an optimized version of DSP. It uses adaptive waveform shaping technology to be able to pre-judge the subsequent development path each time a new combination of keys is pressed, prepare the best waveform file in advance, and instantly adjust parameters such as amplitude and harmonic composition to minimize the negative interference caused by the changes to the point where the difference is almost imperceptible. Especially in the jazz and blues style, the chord changes are extremely intensive and frequent. This mechanism is particularly important, because only in this way can the improvisation be guaranteed to be coherent, smooth and textured. Specifically, when a musician plays the change from Fm7 to G7sus4, the traditional system may experience a momentary freeze, and the new solution avoids such situations through pre-calculation to ensure that the performance is flawless throughout.

[0026] In addition, it is necessary to consider how to keep the ever - enriching tone database up - to - date with the changing trends of pop music and meet the growing needs of artists. On the one hand, actively collect fresh and interesting sampling materials globally to expand the inventory scale; on the other hand, build an open - source platform that allows users to upload their unique sounds and create a community - based resource - sharing network. At the same time, formulate scientific and reasonable classification and indexing rules to ensure that any member can locate and find their favorite content in the shortest time, and support a remote online upgrade mechanism. Once an update occurs, it will send a reminder to the registered hardware to prompt the device to synchronize the latest information in a timely manner, thus constructing an open, integrated, and continuously growing and improving ecosystem that perfectly fits the contemporary diversified cross - border music ecology.

[0027] A multi - tone hybrid performance intelligent guitar system of the present invention further includes: a complex system that integrates advanced signal processing technology, machine learning algorithms, and dynamic parameter optimization functions, which can effectively solve various music performance problems existing in traditional musical instruments, thereby significantly enhancing the user's multi - tone performance experience.

[0028] The operation steps of the intelligent guitar system are as follows: The first step is that the user issues music instructions through the system interface. The system is equipped with an advanced multi - track synchronization recognition and translation module. When receiving instructions from the user regarding music arrangement or instant performance, it will quickly analyze these instructions using its preset high - speed analysis logic and construct a corresponding sound playback track model according to the analysis results. Specifically, this rapid recognition and translation process relies on the specialized machine learning algorithms integrated in the system, which are trained through learning and accumulation of a large amount of historical music data. This algorithm has the ability to understand musical creative expressions in various forms such as understanding various musical score symbols, oral instructions, and even direct action imitation. Therefore, when performing the actual translation action, it can be completed immediately and accurately, which greatly reduces various delay phenomena that may be caused by traditional manual input.

[0029] Next is the fine - tuning process of the system's core part for the sound elements on each independent track - mainly reflected as different frequency audio information generated by the sounds of different strings. In order to achieve the highest precision level of multi - track recording effect, in the present invention, a method of automatic calibration of tone parameters based on an intelligent calculation model is used. It not only considers the fluctuation range of each sound characteristic itself, such as basic attributes like intensity changes, but also takes into account factors that may interact between tracks, such as complex situations like stereo sound effect layout or sound field simulation. Thus, it ensures that even in the reproduction of polyphonic works that are extremely particular about time differences, a stable and coherent performance state can be maintained, greatly enhancing the consistency of recording quality.

[0030] Regarding the problem of unstable sound pickup that may occur during a performance, dynamic pickup signal compensation measures are adopted to achieve audio capture stability management. This set of technologies real-time monitors the fluctuation trend of the level values at all detected points, and then through a feedback mechanism, guides the adjustment of the amplification range setting or other related circuit parameter settings, ensuring that no matter how unpredictable the external interference source is, it can always maintain the best working efficiency to collect the purest and most complete original sound materials for further processing and output. Moreover, no manual intervention is required throughout the process, and everything can operate and adjust autonomously as needed without imposing additional operation burdens on users or disrupting the smooth and continuous feeling of the performance.

[0031] In addition, another key problem is also solved, that is, how to reduce the risk rate of sound quality damage caused by frequently switching different types of chords, especially the occurrence probability of problems such as instant distortion aggravation caused by certain specific structural combinations. In this regard, the digital signal processing algorithm optimized specifically by the system is given special functional paragraphs to evaluate the difference point distribution patterns between each transformation before and after and predict the subsequent change direction trajectory. With this forward-looking consideration design principle, the newly generated version is made as close as possible to the ideal reference template requirements, effectively avoiding the impact force brought by a series of non-linear mutation situations from damaging and interfering with the complete reproduction of the original intention.

[0032] Finally, a special design plan is made for the timbre library update mechanism. Considering that the traditional hardware devices have problems such as fixed capacity limits and long update cycles, which restrict the development space of their flexibility and adaptability, while this product allows regular acquisition of new sample data sources through cloud networking and automatically adds them to the local reserve pool for users to select and explore a wider range of more innovative possibilities.

[0033] The above constitutes a comprehensive and complete closed-loop control architecture solution system to address the challenges faced in modern performances and provide more potential opportunities for future personalized creation needs.

[0034] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. An intelligent guitar system for multi-timbre mixed performance, characterized in that: include: The acquisition module obtains the user's music instructions and uses the preset multi-track synchronization algorithm for rapid recognition and translation; The adjustment module adjusts the timbre parameters corresponding to each audio track according to the recognition and translation results to optimize the synchronization accuracy of multi-track recording; Monitoring module, which monitors the pickup signal strength in real time and stabilizes the audio input through dynamic compensation technology; The conversion module minimizes the timbre distortion problem during complex chord conversion based on an optimized digital signal processing algorithm.

2. The intelligent guitar system for multi-timbre mixed performance according to claim 1, characterized in that: Minimizing the timbre distortion problem during complex chord conversion based on optimized digital signal processing algorithms includes: Establish a set of dynamic frequency domain filter parameter models that adapt to complex chord conversion processes; Real-time detection of the audio input frequency range and adjustment of the corresponding digital filter according to the frequency; If frequency offset occurs, the following formula is used for adjustment: If f >= fc, A * sin(B) is used, where A represents the volume gain factor and B is the time-related phase adjustment. This method can ensure smooth transition of timbre without distortion. The processed signal is evaluated in real time and the algorithm is automatically optimized to reduce any potential residual artifacts.

3. The intelligent guitar system for multi-timbre mixed performance according to claim 2, characterized in that: The dynamic frequency domain filter parameter model is further described based on the steps involved in the specific definition: Setting a personalized frequency band adjustment threshold Pth according to user preferences; Select the optimal filter parameter Pi within the frequency interval Fi of each audio signal to ensure synchronization between different audio tracks; Calculate the weighted average filtering effect Q = ΣWi*Pi / n, where Wi is the weight coefficient of each track and n is the number of tracks; The final optimized filtering parameters are applied to the data stream of the next time slot to continuously improve the synchronization accuracy.

4. The intelligent guitar system for multi-timbre mixed performance according to claim 3, characterized in that: The calculated weighted average filtering effect is specified in more detail: Use historical performance data analysis to derive common problem patterns Hpm; Extract the reference benchmark parameter Rbp representing a specific performance situation from the model training data; When encountering similar situations, use conditional judgment to decide whether to adjust: if Hpm matching degree > M, use the pre-saved Rbp parameter to directly overwrite the original setting; here M refers to the matching degree threshold; The optimal parameter set is iteratively updated through a feedback learning mechanism to improve the overall expressiveness.

5. The intelligent guitar system for multi-timbre mixed performance according to claim 4, characterized in that: Further definitions are made in terms of model training and adaptive adjustment: Configure exclusive basic filter templates Ft for different types of instruments; Fine-tune and calibrate the effects of real-time environmental factors Fenv, such as humidity and temperature, on sound characteristics; Follow the formula G = (A * Ft + B * Fenv) / C to generate a comprehensive control strategy for immediate use, where G represents the global filter matrix and C is a factor used for normalization; Regularly evaluate and archive the best configurations obtained under various performance environments for future reference, in order to cope with the uncertainties brought about by changing recording environments.

6. The intelligent guitar system for multi-timbre mixed performance according to claim 5, characterized in that: In order to better adapt to the current diverse creative scenarios, an intelligent recognition mechanism has been added: Identify the music instruction content Ci and intention Iu input by the user, and quickly match the closest professional music score database resource according to Iu; Design a dedicated module to analyze the complex multi-track synchronization algorithm logic to support high-fidelity restoration of composition intent; Before executing each new instruction, a preload operation Lpd is performed, that is, the information that may be used is loaded into the cache in advance according to certain rules to speed up the response; If the estimated time delay Td<Tt, we can ensure timely and accurate feedback of user needs to avoid response delays affecting the experience.

7. The intelligent guitar system for multi-timbre mixed performance according to claim 6, characterized in that: The technical solution for rapid recognition and translation based on obtaining user music instructions is as follows: Introducing a speech analysis engine in conjunction with artificial intelligence technology for more accurate language analysis; Supports recognition of multiple dialects or languages ​​to expand the applicable population base; Use conditional judgment statements: If the instruction type Dt is a melody construction related category, enable advanced editing tools to help users improve the song structure, where Dt represents a command category identifier; We regularly update the dictionary and expand the database to capture the latest music style trends in order to keep up with the pace of development and continuously optimize the user experience.

8. The intelligent guitar system for multi-timbre mixed performance according to claim 7, characterized in that: Several key components have been added to the intelligent language processing module: ContextAware, a context-aware component driven by deep learning algorithms; CoreNLP, the core NLP engine, can deeply understand the true intention behind the user's expression; Use decision tree DecisionTree to screen and filter irrelevant noise data Nsd, retaining only the core useful part CoreUse, and obtain more pure and effective instruction information through the formula CoreUse=InputNsd process; Build a personalized service recommendation subsystem ServiceReco to provide new and old users with highly targeted product function guidance based on the experience accumulated from previous interactions.

9. The intelligent guitar system for multi-timbre mixed performance according to claim 8, characterized in that: Specific implementation steps are specified for the core NLP engine: Provides a multi-level corpus annotation framework LayeredAnnotation as the underlying support facility; A rich dictionary table DictionaryOfTerms for target domain terminology is constructed to facilitate more fine-grained understanding; Apply graph convolutional network GraphConvoNet to analyze grammatical structure dependencies to form a more reasonable semantic parsing path; If the length L of the Pathway is found to be greater than the specified limit LimitLength, it means that there may be too deep branches or abnormally complex structures that need to trigger a manual review phase. Here, LimitLength is a specified threshold value used to evaluate path complexity to avoid problems that are too cumbersome and lead to reduced processing efficiency.

10. The intelligent guitar system for multi-timbre mixed performance according to claim 9, characterized in that: More details about the personalized service recommendation subsystem are as follows: Set interest profile InterestProfile to store user preference information; Developed an efficient search index system HiSearchIndex to improve retrieval efficiency and service quality; ReinforceLrnMdl is used to rearrange the order of recommendation results to make them more in line with personal needs and preferences. If the user behavior changes by more than a predefined ratio, ThresholdPct, such as buying a new model guitar, the InterestProfile is immediately triggered to refresh to maintain the freshness and relevance of the recommendation list. Here, ThresholdPct is a key indicator to measure the degree of importance conversion. Some innovations are proposed around efficient search index design: Integrate publicly shared content on social media platforms to enrich the local resource pool ResourcePool; The dimensions of location and time period are introduced to refine the clustering strategy; Ranking various search sources by priority, such as official guides > reviews by famous musicians > public opinions recognized by a wide audience; The sorted content combinations are comprehensively scored ScoreCombine = W1*S1 + ... + Wn*Sn. Each weight Wi reflects the importance of the corresponding item. Based on this, the most valuable answers and tutorials are selected for reference by end users. ScoreCombine represents the comprehensive score, and Sx represents the value of each score indicator. Combining geographic coordinate positioning and spatiotemporal dimension information to optimize the material recommendation algorithm: Implement ActiveSession and Hotspots fusion analysis to create a unique content browsing environment CustomizedView for each customer; Introducing the portable device synchronization mechanism SyncDevice for outdoor performances, ensuring that high-quality audio and video materials can be easily enjoyed no matter where you are; GeoGroupMatch, an interest group matching algorithm based on geographic location association, makes interaction and communication between like-minded people easier and more convenient; If the activity participation level in a certain area is > AlLimit, the information about the music events that are currently taking place or are about to take place nearby will be automatically pushed to attract more attention and support. AlLimit indicates the limit of the number of people that can be accommodated within an ideal range.

Images