Method and system for assisting practice of musical instrument playing, device and storage medium

Abstract

The application relates to the field of digital music instruments, in particular to a method and system for assisting musical instrument practice, equipment and a storage medium, the method comprising: obtaining historical performance data of a user within a preset time period; extracting a plurality of performance behavior features from the historical performance data to determine a group identity of the user; selecting an evaluation dimension from a plurality of evaluation dimensions based on a preset visual demand mapping table according to the group identity; collecting user performance data generated in a performance process in real time, and obtaining first standard performance data corresponding to the evaluation dimension; evaluating the user performance data based on the first standard performance data to generate first performance evaluation data corresponding to the evaluation dimension; and displaying the first performance evaluation data corresponding to the evaluation dimension in real time. The method can accurately and concisely display performance feedback information of part of the evaluation dimensions, provide more targeted personalized feedback and guidance, and improve the accuracy and practicability of the performance feedback information.

Description

[0001] Priority application This application claims priority to Chinese invention patent application filed on January 24, 2025, [Application No. 2025101228545] entitled "Auxiliary Practice Method, System, Device and Storage Medium for Musical Instrument Playing", which is incorporated herein by reference in its entirety. Technical Field

[0002] This application relates to the field of musical instrument digitization, and more particularly to an auxiliary practice method, system, device, and storage medium for musical instrument playing. Background Technology

[0003] With the development of technology, the digital expression of music and art has become increasingly rich, and it has broad application prospects in education, entertainment, virtual reality and other fields. It further combines knowledge from multiple disciplines such as music, computer science and visual arts, providing performers with a rich interactive learning experience.

[0004] For example, Chinese invention patent application CN104516670A discloses a piano teaching method, system, and medium, including receiving initial playing data of the current piece sent by a user terminal; extracting feature data from the initial playing data; comparing the feature data in a database; determining the user's initial playing level based on the comparison results; forming an initial learning path corresponding to the current piece based on the user's initial playing level; and feeding back and displaying the initial learning path on the user terminal so that the user can practice piano playing according to the initial learning path. Chinese invention patent application CN104516670A discloses a piano practice system used in an electronic device. The electronic device includes a microphone and a display screen. A projection device is located at the bottom of the display screen, which is fixed by a bracket connected to a piano. The system includes: a storage module for importing and storing electronic sheet music files for piano practice; an analysis module for analyzing the sheet music files based on notes, timbre, and rhythm; a level selection module for selecting the appropriate level mode for the learner before piano practice, including beginner, elementary, and intermediate levels; an audio recognition module for recognizing the pitch, rhythm, timbre, and volume of the sounds played by the learner; and a calculation and evaluation module for comparing the analysis results of the analysis module with the recognition results of the audio recognition module, and generating corresponding practice evaluation data based on the comparison results.

[0005] However, the personalized teaching capabilities of existing technologies are limited, making it difficult to match users' actual needs, and they have obvious limitations in terms of intelligence and practicality. Summary of the Invention

[0006] The main objective of this application is to provide an auxiliary practice method, system, device, and storage medium for musical instrument playing. To solve the aforementioned technical problems, this application specifically adopts the following technical solution: The first aspect of this application is to provide an auxiliary practice method for playing a musical instrument, comprising the following steps: S101. Obtain the user's historical performance data within a preset time period; S102. Extract several performance behavior features from the historical performance data, and determine the user's group identifier based on the several performance behavior features; S103. Based on a preset visualization requirement mapping table, select one evaluation dimension from multiple evaluation dimensions according to the group identifier; wherein, the preset visualization requirement mapping table is used to define the mapping rules between the group identifier and the evaluation dimension; S104. Collect user performance data generated during the performance in real time, and obtain the first standard performance data corresponding to the first evaluation dimension; evaluate the user performance data based on the first standard performance data, and generate the first performance evaluation data corresponding to the first evaluation dimension. S105. Display the first performance evaluation data corresponding to the first type of evaluation dimension in real time.

[0007] In some embodiments, the method further includes: selecting two types of evaluation dimensions from a plurality of evaluation dimensions based on the group identifier; obtaining second standard performance data corresponding to the two types of evaluation dimensions; evaluating the user performance data based on the second standard performance data to generate second performance evaluation data corresponding to the two types of evaluation dimensions; and displaying the second performance evaluation data corresponding to the two types of evaluation dimensions in real time when the second performance evaluation data continuously exceeds a preset threshold range within a first preset time period.

[0008] In some embodiments, the method further includes: when the second performance evaluation data continuously exceeds the preset threshold range within a second preset duration, determining the user's individual identifier based on the two evaluation dimensions, wherein the second preset duration is longer than the first preset duration; reselecting three evaluation dimensions from multiple evaluation dimensions based on the group identifier and the individual identifier, and obtaining third standard performance data corresponding to the three evaluation dimensions; evaluating the user's performance data based on the third standard performance data to generate third performance evaluation data corresponding to the three evaluation dimensions; and displaying the third performance evaluation data corresponding to the three evaluation dimensions in real time.

[0009] In some embodiments, the plurality of evaluation dimensions include: pitch dimension, fingering dimension, hand shape dimension, duration dimension, force dimension, and pedal dimension.

[0010] In some embodiments, the performance behavior features include performance accuracy features, performance repertoire features, and performance frequency features. S102 includes: defining several target behavior features for each group identifier; matching the several performance behavior features with the several target behavior features for each group identifier; if a match is successful, determining the successfully matched group identifier as the user's group identifier.

[0011] In some embodiments, the historical performance data includes performance accuracy data, and the performance accuracy features include performance accuracy level and / or accuracy change trend; S102 includes: comparing at least one of the performance accuracy data within the preset time period with a preset accuracy threshold to determine the performance accuracy level; and / or, comparing multiple of the performance accuracy data within the preset time period to obtain the accuracy change trend.

[0012] In some embodiments, the historical performance data includes performance repertoire data, and the performance repertoire features include repertoire difficulty level and / or special preference repertoire; S102 includes: comparing at least one of the performance repertoire data within the preset time period with a preset difficulty threshold to determine the repertoire difficulty level; and / or, determining the special preference repertoire based on multiple performance repertoire data within the preset time period.

[0013] A second aspect of this application is to provide an auxiliary practice system for playing musical instruments, the system comprising: The historical performance module is used to obtain the user's historical performance data within a preset time period; The group identification module is used to extract several performance behavior features from the historical performance data and determine the user's group identification based on the several performance behavior features; The evaluation dimension module is used to select one evaluation dimension from multiple evaluation dimensions based on the group identifier, according to a preset visualization requirement mapping table; wherein, the preset visualization requirement mapping table is used to define the mapping rules between the group identifier and the evaluation dimension; The evaluation data module is used to collect user performance data generated during the performance in real time and obtain the first standard performance data corresponding to the first evaluation dimension; evaluate the user performance data based on the first standard performance data to generate the first performance evaluation data corresponding to the first evaluation dimension. The data display module is used to display the first performance evaluation data corresponding to the first type of evaluation dimension in real time.

[0014] A third aspect of this application provides a computer device, the device including a memory and a processor; The memory is used to store computer programs; The processor is configured to execute the computer program and, in executing the computer program, implement the steps of the auxiliary practice method for playing musical instruments as provided in any embodiment.

[0015] In a fourth aspect, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the steps of an auxiliary practice method for playing a musical instrument as provided in any embodiment of this application.

[0016] Beneficial effects: This application provides an auxiliary practice method for musical instrument playing, specifically a multi-screening mechanism for assessment dimensions. Based on the user's learning goals and feedback needs, multiple assessment dimensions are functionally divided, accurately and concisely displaying performance feedback information for some assessment dimensions. This provides users with more targeted and personalized feedback and guidance, improving the accuracy and practicality of performance feedback information, thereby increasing the efficiency of auxiliary practice.

[0017] Firstly, high-value evaluation dimensions can be quickly selected and displayed by identifying user groups.

[0018] By conducting in-depth analysis of users' historical performance behavior across multiple behavioral characteristic dimensions (such as performance accuracy, repertoire selection, and performance frequency), a group identification and classification system based on performance behavior characteristics is constructed. This allows for the precise identification of the core needs of user groups. Furthermore, based on these core needs, a small number of high-value evaluation dimensions (i.e., a category of evaluation dimensions) are further selected from multiple evaluation dimensions (such as pitch, fingering, hand shape, note value, dynamics, and pedaling) to provide users with highly personalized performance evaluations. This helps users quickly identify problems and optimize their performance, significantly improving learning efficiency and performance level.

[0019] Secondly, make full use of the evaluation dimensions that are not displayed, monitor the degree of anomalies in real-time performance data, and dynamically adjust the evaluation dimensions that are displayed.

[0020] Specifically, non-essential evaluation dimensions that users have already mastered or do not need to master are designated as secondary evaluation dimensions. When a persistent deviation is detected in a user's default mastery of a dimension, a graded processing is performed.

[0021] When the degree of abnormality is small, the error rate of abnormality is displayed on the dimensions that the user has mastered by default. The display of performance feedback information in that dimension is dynamically activated to realize the immediate capture and correction of abnormal performance behavior. At the same time, potential abilities or interests are displayed on the dimensions that the user does not need to master by default. The display of performance feedback information in that dimension is dynamically activated to form a dual drive of positive incentive and potential mining. This avoids the interference of information redundancy on the user and realizes precise intervention in the user's shortcomings and proactive stimulation of strengths.

[0022] Furthermore, when the anomaly is significant, it triggers the individual identification settings for specific individuals within the group, and dynamically adjusts the evaluation focus based on these individual identifications, selecting dimensions that better align with the user's actual needs for precise assessment. This parallel mechanism of group and individual identification avoids the limitations of a "one-size-fits-all" approach to group classification, accurately identifying the user's actual skill level and needs, providing highly personalized feedback, making performance feedback more practical, and helping users improve their performance skills in a more targeted manner. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. The elements or parts in the drawings are not necessarily drawn to scale. Obviously, the drawings described below are some embodiments of this application; for those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0024] Figure 1 This is a schematic flowchart illustrating an auxiliary practice method for playing a musical instrument, provided in an embodiment of this application. Figure 2 This is a schematic block diagram of an auxiliary practice system for playing musical instruments provided in an embodiment of this application; Figure 3 This is a schematic block diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0026] In this document, suffixes such as “module,” “part,” or “unit” used to denote elements are used only for illustrative purposes and have no specific meaning in themselves. Therefore, “module,” “part,” or “unit” may be used interchangeably.

[0027] In this document, the terms "upper," "lower," "inner," "outer," "front," "rear," "one end," and "the other end," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0028] In this document, unless otherwise explicitly specified and limited, the terms "installed," "equipped with," and "connected," etc., should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, a direct connection, or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0029] In this document, "and / or" includes any and all combinations of one or more of the listed related items.

[0030] In this article, "multiple" means two or more, that is, it includes two, three, four, five, etc.

[0031] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0032] While existing technologies have introduced level selection modules (such as beginner, elementary, and advanced), these preset levels cannot accurately reflect the unique situation of each user. They are classified solely based on the user's playing level, ignoring subtle differences and personalized needs between individuals. This results in significant limitations in terms of intelligence and practicality, leading to inaccurate guidance and feedback, which affects teaching effectiveness and user experience.

[0033] Based on this, this application provides an auxiliary practice method for musical instrument playing, specifically a multi-screening mechanism for assessment dimensions. This mechanism functionally divides multiple assessment dimensions according to the user's learning goals and feedback needs, accurately and concisely displaying performance feedback information for some assessment dimensions. This provides users with more targeted and personalized feedback and guidance, improving the accuracy and practicality of performance feedback information, and thus increasing the efficiency of auxiliary practice.

[0034] The following detailed description, in conjunction with the accompanying drawings, outlines some embodiments of this application. Unless otherwise specified, the following embodiments and features described herein can be combined with each other. Please refer to... Figure 1 , Figure 1 This is a schematic flowchart illustrating an auxiliary practice method for playing a musical instrument provided in an embodiment of this application, such as... Figure 1 As shown, this application provides an auxiliary practice method for playing musical instruments, the method including S101 to S105.

[0035] S101. Obtain the user's historical performance data within a preset time period.

[0036] Among them, historical performance data refers to various types of data generated by users during their historical performances. This data can come from various channels, including direct recordings from performance equipment, as well as processing and analyzing audio or video files uploaded by users, practice records from online music learning platforms or applications. No specific restrictions are imposed here.

[0037] Historical performance data includes technical indicators, such as performance accuracy data, which is directly related to performance skill level, such as audio generated by the user in historical performances, as well as evaluation results from one or more dimensions such as pitch, fingering, hand shape, note value, dynamics, and pedaling. Historical performance data also includes non-technical indicators such as repertoire data and performance frequency. Repertoire data refers to data related to the repertoire performed, such as statistics on the repertoire played by the user and the corresponding sheet music; performance frequency data refers to data related to performance habits, such as the time recorded for each performance.

[0038] The preset time period can be flexibly set based on actual needs and is not limited here. For example, historical performance data of the user within the past 3 months can be obtained, which can provide a sufficient amount of data to capture the user's performance behavior characteristics, while avoiding the loss of timeliness or relevance of early data due to excessive time.

[0039] S102. Extract several performance behavior features from the historical performance data, and determine the user's group identifier based on the several performance behavior features.

[0040] Among them, performance behavior characteristics are specific attributes extracted from historical performance data that reflect the user's performance habits and performance ability. Corresponding to historical performance data, performance behavior characteristics also include technical and non-technical behavior characteristics.

[0041] For example, the performance behavior characteristics include performance accuracy characteristics, repertoire characteristics, and performance frequency characteristics. Performance accuracy characteristics refer to the user's technical level, which can be extracted from performance accuracy data, such as pitch accuracy, rhythmic stability, and fingering correctness. Repertoire characteristics refer to the features of the musical pieces the user performs, such as piece name, type, difficulty level, style preference, and completion rate. Performance frequency characteristics refer to the regularity and consistency of the user's performance, such as performance frequency, duration of each performance, and long-term consistency.

[0042] In some embodiments, S102 includes: defining a plurality of target behavioral features for each group identifier; matching the plurality of performance behavioral features with the plurality of target behavioral features for each group identifier respectively; if a match is successful, determining the successfully matched group identifier as the user's group identifier.

[0043] Among them, the target behavioral characteristics are standards set based on typical or idealized user behavior patterns. For example, a high-level career development group may have characteristics such as high playing accuracy, preference for complex pieces, and high frequency of practice.

[0044] Specifically, in order to accurately determine the user's group identity, several target behavioral characteristics are defined for each preset user group. The performance behavioral characteristics extracted from historical performance data are matched one by one with the target behavioral characteristics of each group. When several performance behavioral characteristics of a user are highly matched with the target behavioral characteristics of a specific group, the match is successful and it can be determined that the user belongs to this group.

[0045] It should be understood that the behavioral feature matching in this embodiment relies not only on the consistency of a single feature, but also on the similarity of the overall behavioral patterns. This includes a detailed comparison of various aspects such as the user's actual performance accuracy features (e.g., pitch accuracy, rhythm stability, fingering correctness), the features of the pieces played (e.g., piece difficulty, piece style), and the features of the frequency of performance (e.g., single performance duration, total weekly duration).

[0046] S103. Based on a preset visualization requirement mapping table, select one evaluation dimension from multiple evaluation dimensions according to the group identifier; wherein, the preset visualization requirement mapping table is used to define the mapping rules between the group identifier and the evaluation dimension.

[0047] For example, to fully evaluate a user's music learning and performance, the evaluation dimensions include: pitch dimension, fingering dimension, hand shape dimension, note value dimension, dynamics dimension, and pedaling dimension. Other dimensions, such as body shape, may also be included, but are not limited here.

[0048] The performance evaluation system includes the following dimensions: Pitch Dimension: assesses whether the user can accurately produce the correct pitch and the stability of intonation. Fingering Dimension: assesses whether the user's finger movements conform to standard technical requirements, such as evaluating the correctness and flexibility of fingering. Hand Shape Dimension: assesses whether the user's hand posture and movements are standardized, such as evaluating the degree of wrist and finger flexion. Timing Dimension: assesses the accuracy of the user's rhythmic delivery, such as whether the duration of each note conforms to the score. Dynamics Dimension: assesses the dynamic changes in the pressure applied to the keys, such as the impact of the speed and displacement of the pressed keys on the strength of the pressure. Pedal Dimension: assesses the timing and effectiveness of the user's pedal use, such as the appropriateness of the sustain pedal application. Body Posture Dimension: assesses whether the user's body posture is standardized during performance.

[0049] It should be understood that musical instrument performance, especially of keyboard instruments like the piano, has a wide range and dynamic range, from the softest notes to the most powerful sounds. Performers must precisely control touch, pedal use, rhythm, and intonation, while simultaneously conveying the emotion and style of the piece. Therefore, performers need to pay attention to numerous evaluation dimensions during practice, each influencing the final musical performance. For example, dynamic variations reveal emotional depth, finger fluency ensures technical accuracy, and pedal technique enriches tonal layers. These dimensions help improve a performer's overall ability, ensuring their performance is both technically superb and artistically compelling.

[0050] For example, based on the analysis of different types of user needs, a pre-defined evaluation dimension can be selected to best measure and showcase the performance and development of a specific group, thereby generating a pre-defined visual requirements mapping table. This pre-defined visual requirements mapping table can be a structured dataset or configuration file containing the correspondence between various group identifiers and multiple evaluation dimensions.

[0051] Specifically, a pre-defined visualization requirement mapping table is used to guide how to select the most appropriate evaluation dimension based on the user's group identifier. An evaluation dimension can include one or more evaluation dimensions, so that each user group can obtain data analysis and visualization that matches their needs.

[0052] For example, exam-oriented users, whose primary training goal is to pass exams, may focus more on fingering, hand position, and pitch. Professionally-oriented users may prioritize fingering and hand position to develop strong fundamental playing skills, providing sustainable support for future, more advanced techniques. They also need to pay attention to dynamics to continuously refine their technique and enhance their artistic expression. Hobby-oriented users may emphasize pitch and note values ​​to achieve a smooth and pleasing performance. Therapeutic and rehabilitation users may focus on pitch to understand their participation and completion levels, thereby improving therapeutic outcomes.

[0053] S104. Collect user performance data generated during the performance in real time, and obtain the first standard performance data corresponding to the first type of evaluation dimension; evaluate the user performance data based on the first standard performance data, and generate the first performance evaluation data corresponding to the first type of evaluation dimension.

[0054] The standard performance data is based on high-quality performance examples from professional music education institutions or authoritative music databases, and includes the ideal performance parameters for each evaluation dimension of the repertoire. For example, in the pitch dimension, standard performance data may include precise intonation requirements; in the fingering dimension, it includes correct finger movement sequences.

[0055] The performance evaluation data includes user performance scores across various evaluation dimensions, and may also include improvement suggestions and technical guidance for lower scores, as well as encouraging tips for higher scores. For example, performance evaluation data for the fingering dimension can mark incorrect finger movements and recommend more effective practice methods, thereby improving the effectiveness and practicality of assisted practice.

[0056] Specifically, to achieve accurate evaluation of user performance, the system first collects user performance data generated during the performance in real time. This data includes multi-dimensional information such as pitch, fingering, hand shape, note value, dynamics, and pedal input. First-standard performance data corresponding to one type of evaluation dimension is obtained. Based on this first-standard performance data, the user's actual performance data is compared and evaluated in detail, differences are identified and their degree is quantified, and first-level performance evaluation data is generated—that is, a detailed evaluation result for that type of evaluation dimension.

[0057] S105. Display the first performance evaluation data corresponding to the first type of evaluation dimension in real time.

[0058] Specifically, the first performance evaluation data corresponding to the aforementioned evaluation dimension is displayed in real time during the user's performance. With each performance action of the user, the performance on a small number of selected evaluation dimensions, including pitch, fingering, hand shape, note value, dynamics, and pedal stroke, is immediately analyzed. Data related to evaluation dimensions not selected are not presented to the user at this time.

[0059] It should be understood that analyzing several pre-selected key assessment dimensions, chosen based on their importance to performance quality and the user's current needs or practice goals, ensures that real-time feedback during performance provides valuable guidance while maintaining simplicity and relevance, avoiding information overload that could cause users to lose focus, thereby improving the efficiency of assisted practice.

[0060] It should be understood that the embodiments of this application conduct in-depth analysis of the user's historical performance behavior from multiple different perspectives and behavioral feature dimensions (such as performance accuracy, repertoire selection, performance frequency, etc.), and construct a group identification classification system based on performance behavior characteristics. This allows for the accurate identification of the core needs of the user group. Furthermore, based on the core needs, a small number of high-value evaluation dimensions (i.e., a class of evaluation dimensions) are further selected from multiple evaluation dimensions (such as pitch, fingering, hand shape, note value, dynamics, pedaling, etc.) to provide users with highly personalized performance evaluations. This helps users quickly identify problems and optimize their performance, helps users focus on improving specific skills, and avoids the cognitive burden caused by excessive immediate feedback, significantly improving learning efficiency and performance level.

[0061] In some embodiments, by pre-analyzing the performance behavior characteristics of multiple sample users, sample users with similar characteristics can be grouped together to form specific groups. Each group possesses a unique combination of characteristics, and the performance behavior characteristics in these combinations are defined as the target behavioral characteristics of the group, reflecting the common behavioral patterns and technical features of the group members. For example, by performing cluster analysis or machine learning on the historical performance data of a large number of sample users, different user groups with similar behavioral patterns can be identified.

[0062] In some embodiments, a corresponding group identifier is set for each group. The group identifier can be flexibly divided according to different levels of refinement and different dimensions to refine the group segmentation, thereby more accurately capturing the specific needs of users and providing personalized feedback and support.

[0063] For example, users can be categorized into two main groups based on their performance motivation: professional performers and amateur performers. Professional performers typically have rigorous requirements for all aspects of their playing skills, while amateur performers focus more on the enjoyment music brings. Furthermore, based on performance motivation, group identifiers can be further subdivided into various types. For instance, professional performers can be divided into exam-oriented and career development-oriented groups; amateur performers can be divided into hobby-oriented and therapeutic / rehabilitation-oriented groups. Going further afield, exam-oriented groups can be subdivided into exam-preparation-focused and long-term planning-oriented groups; career development-oriented groups can be subdivided into professional performers, music educators, and composers, etc.

[0064] In addition, they can be classified according to two dimensions: learning motivation and performance level, such as beginner level exam-oriented, intermediate level exam-oriented, and advanced level exam-oriented.

[0065] It is evident that the segmentation of groups and the setting of group identifiers offer a degree of flexibility. By extracting diverse performance behavior characteristics from historical performance data across multiple dimensions, and refining group identifiers, rigid labels are avoided. This approach caters to the specific needs of various groups, thereby providing more accurate and personalized services. The segmentation of groups not only enhances the user experience but also promotes the effective allocation of platform resources and the optimized configuration of services.

[0066] In some embodiments, the corresponding algorithm model can be pre-trained using a large amount of historical performance data labeled with user groups. Based on this algorithm model, the user's historical performance data can be analyzed to quickly and accurately extract performance behavior features, and thereby infer the user's group affiliation.

[0067] In some embodiments, performance behavior characteristics can be quantified using data analysis tools and specific threshold settings, and expressed in the form of scores, grades, star ratings or textual descriptions, and then matched with target behavior characteristics based on the quantified performance behavior characteristics.

[0068] In some embodiments, the historical performance data includes performance accuracy data, and the performance accuracy features include performance accuracy level and / or accuracy change trend; S102 includes: comparing at least one of the performance accuracy data within the preset time period with a preset accuracy threshold to determine the performance accuracy level; and / or, comparing multiple of the performance accuracy data within the preset time period to obtain the accuracy change trend.

[0069] Specifically, preset accuracy thresholds are a set of standards or limits used to evaluate performance accuracy. These can be thresholds of different levels set based on overall performance level, or multiple levels of accuracy thresholds set according to different evaluation dimensions. For example, preset accuracy thresholds may include thresholds for multiple dimensions such as pitch, fingering, hand shape, note value, dynamics, and pedal stroke, with different levels of thresholds set for each dimension. When multiple performance accuracy data exist, the corresponding accuracy threshold is matched according to the type of performance accuracy data. When a user's performance accuracy data is compared with the preset accuracy thresholds, the performance accuracy level for different dimensions can be determined based on the degree of matching, thereby quantifying the user's technical level.

[0070] Taking dynamics as an example, the first-level threshold can be set to a dynamic error rate of less than 5%, the second-level threshold can be set to a dynamic error rate of 5%-25%, and the third-level threshold can be set to a dynamic error rate of more than 25%. Specific thresholds can be set based on the standards of professional music education institutions or industry consensus, and are not limited here. When the evaluation result of the dynamic data in the performance accuracy data is a dynamic error rate of 10%, the user's dynamic-related performance accuracy level is the second dynamic level. It should be understood that the performance accuracy level at this time can be a set of data, such as the first pitch level, the second fingering level, the second hand shape level, the first note value level, the second dynamic level, the third pedal level, etc., generated from different angles.

[0071] Specifically, by analyzing multiple performance accuracy data over a period of time, the trend of a user's accuracy can be determined. This trend can be used to identify the pattern or direction of the user's performance accuracy over time, thereby identifying the user's rate of progress and practice effectiveness, and ultimately determining whether they have improved or regressed. It should be understood that the accuracy trend can also be a set of data, generating corresponding accuracy trends for multiple dimensions such as pitch, fingering, hand shape, note value, dynamics, and pedal stroke.

[0072] In some embodiments, the historical performance data includes performance repertoire data, and the performance repertoire features include repertoire difficulty level and / or special preference repertoire; S102 includes: comparing at least one of the performance repertoire data within the preset time period with a preset difficulty threshold to determine the repertoire difficulty level; and / or, determining the special preference repertoire based on multiple performance repertoire data within the preset time period.

[0073] The preset difficulty thresholds are a set of standards or boundaries used to assess the difficulty of a piece and categorize it into different levels. These thresholds can be based on different levels set according to the sheet music, or they can be set according to the scale span, fingering requirements, rhythmic complexity, dynamic changes, etc. in the piece. The specific thresholds can be set based on the standards of professional music education institutions or industry consensus, and are not limited here.

[0074] Specifically, the scale span, fingering requirements, rhythmic complexity, dynamic variations, and other aspects of the sheet music in at least one performance piece are analyzed and compared with a preset difficulty threshold to quantify the technical complexity of the user's selected piece, thereby determining its difficulty level. The difficulty level can be divided into multiple levels, such as different stages from beginner to advanced.

[0075] Based on the analysis of data from multiple performance pieces within the same time period, information such as the names and types of pieces selected by users, as well as the frequency of repeated practice, is collected to identify users' special preference pieces. Special preference pieces refer to a type of musical work or style that users show particular fondness for or frequently choose in multiple performance activities, and specific pieces that users tend to perform, such as examination pieces.

[0076] For example, based on various technical or non-technical performance behavior characteristics such as performance accuracy level, accuracy change trend, repertoire difficulty level, special preferred repertoire, and performance frequency characteristics, different groups can be distinguished and target behavior characteristics can be set accordingly.

[0077] For example, after performance behavior characteristics are quantitatively processed into high, medium, and low ratings, and categorized by motivation, the professional performance group exhibits the following target behavior characteristics: this group has a good grasp of basic skills such as fingering and hand position, therefore, the accuracy level corresponding to these skills is mostly high; the difficulty level of the repertoire is high or medium; and the performance frequency is characterized by regular timing and high frequency. Furthermore, those with a special preference for exam-oriented repertoire mostly exhibit positive improvement in accuracy; those with a special preference for career development exhibit diverse repertoire choices, and their accuracy mostly shows a high level with small fluctuations.

[0078] For example, amateur performers exhibit the following target behavior characteristics: their repertoire difficulty levels are medium to low; their preferred repertoire choices are relatively fixed; and their performance frequency is random and low. Furthermore, their preferred repertoire consists of popular and common pieces, and their accuracy trends mostly show positive improvement; while those in therapeutic or rehabilitation programs prefer simple and upbeat pieces, and their accuracy trends mostly show unstable fluctuations.

[0079] Furthermore, the analysis can be combined with the performance accuracy level to assess the user's mastery of the performance skills corresponding to various evaluation dimensions, thereby classifying users into two dimensions: learning motivation and performance level. For example, there are low-level career development types, mid-level career development types, and high-level career development types, which helps to more effectively differentiate user groups at different levels.

[0080] In some embodiments, performance accuracy data from historical performance data can be analyzed to determine the performance accuracy level and trend of each evaluation dimension. Evaluation dimensions with a performance accuracy level lower than a first preset level and a trend of change greater than the first preset trend (i.e., the user's performance level in this evaluation dimension needs improvement and shows a positive trend) can be classified as one type of evaluation dimension. This prioritizes displaying dimensions the user is currently refining, improving the effectiveness of assisted practice. Furthermore, evaluation dimensions with a performance accuracy level higher than a second preset level (i.e., the user's performance level in this evaluation dimension is high) or evaluation dimensions with a trend of change less than the second preset trend (i.e., the trend is small, and the user's performance level in this evaluation dimension is relatively stable) can be classified as a second type of evaluation dimension. This includes dimensions that the user has already mastered or dimensions not yet included in the training plan, enabling the immediate capture and correction of abnormal performance behavior. The first preset level is lower than the second preset level.

[0081] This application embodiment also functionally divides multiple evaluation dimensions according to the user's learning objectives and feedback needs, making full use of evaluation dimensions that are not displayed, and dynamically adjusting the displayed evaluation dimensions based on the anomaly level of real-time performance data. Non-essential evaluation dimensions that the user has already mastered or does not need to master are classified as secondary evaluation dimensions. When a persistent deviation is detected in a dimension that the user has already mastered, tiered processing is performed.

[0082] In some embodiments, the method further includes: selecting two types of evaluation dimensions from a plurality of evaluation dimensions based on the group identifier; obtaining second standard performance data corresponding to the two types of evaluation dimensions; evaluating the user performance data based on the second standard performance data to generate second performance evaluation data corresponding to the two types of evaluation dimensions; and displaying the second performance evaluation data corresponding to the two types of evaluation dimensions in real time when the second performance evaluation data continuously exceeds a preset threshold range within a first preset time period.

[0083] The preset threshold range is used to assess whether a user's performance data meets the expected performance level of their group. The preset threshold range may differ for different groups and different assessment dimensions, and is not limited here. If a user's performance deviates from these standards for a long period of time, it indicates that the user is exhibiting abnormal performance behavior that deviates from the group's identification. This abnormal performance behavior may be an unexpectedly low or high score.

[0084] The first preset duration can be determined based on the actual needs of the sensitivity to identify abnormal performance behavior, and is not limited here.

[0085] Specifically, to provide more personalized and targeted feedback and further improve the effectiveness of assisted practice, two types of evaluation dimensions are selected from multiple evaluation dimensions based on the user's group identifier. These two types of evaluation dimensions are used to monitor the user's performance behavior and can be dimensions that the group has already mastered or do not require special attention from. Second-type standard performance data corresponding to the two types of evaluation dimensions is obtained. Based on this data, the user's performance data is compared and evaluated in detail, differences are identified and their degree is quantified, and second-type performance evaluation data is generated—that is, detailed evaluation results for this type of evaluation dimension. When the second-type performance evaluation data continuously exceeds a preset threshold range within a first preset time period, the evaluation data will be displayed in real time.

[0086] It should be understood that when the degree of abnormality is small, the error rate of abnormality is displayed on the dimensions that the user has mastered by default, and the display of performance feedback information in that dimension is dynamically activated to realize the immediate capture and correction of abnormal behavior. At the same time, potential abilities or interests are displayed on the dimensions that the user does not need to master by default, and the display of performance feedback information in that dimension is dynamically activated to form a dual drive of positive incentives and potential mining. This avoids the interference of information redundancy on the user and realizes precise intervention in the user's shortcomings and proactive stimulation of strengths.

[0087] For example, for users focused on professional development, it's assumed they already possess high playing ability and accurate pitch control. The corresponding first-level evaluation dimension primarily focuses on dimensions that can help further improve performance, while the second-level evaluation dimension can be set to pitch. When the performance evaluation data for the pitch dimension reflects poor pitch performance, supplementary second-level performance evaluation data is displayed to alert the user. As another example, for users focused on hobbies, the hand position dimension doesn't require special attention by default, but if a high hand position evaluation score is detected, supplementary data will be displayed to motivate the user.

[0088] Furthermore, when the anomaly is significant, it triggers the individual identification settings for specific individuals within the group, and dynamically adjusts the evaluation focus based on these individual identifications, selecting dimensions that better align with the user's actual needs for precise assessment. This parallel mechanism of group and individual identification avoids the limitations of a "one-size-fits-all" approach to group classification, accurately identifying the user's actual skill level and needs, providing highly personalized feedback, making performance feedback more practical, helping users improve their performance skills more effectively, and increasing the efficiency of assisted practice.

[0089] In some embodiments, the method further includes: when the second performance evaluation data continuously exceeds the preset threshold range within a second preset duration, determining the user's individual identifier based on the two evaluation dimensions, wherein the second preset duration is longer than the first preset duration; reselecting three evaluation dimensions from multiple evaluation dimensions based on the group identifier and the individual identifier, and obtaining third standard performance data corresponding to the three evaluation dimensions; evaluating the user's performance data based on the third standard performance data to generate third performance evaluation data corresponding to the three evaluation dimensions; and displaying the third performance evaluation data corresponding to the three evaluation dimensions in real time.

[0090] The second preset duration can be determined based on the actual needs for sensitivity in identifying abnormal performance behavior, and is not limited here. Furthermore, the second preset duration is longer than the first preset duration.

[0091] Specifically, to ensure the accuracy of the assessment and the effectiveness of the feedback, if the performance of certain assessment dimensions within the second category remains abnormal, a corresponding individual identifier is assigned to the user based on the abnormal assessment dimension. A portion of the third assessment dimensions is determined based on the user's group identifier and a pre-defined visualization requirement mapping table. Then, based on the individual identifier, another portion of the three categories of assessment dimensions is selected from multiple assessment dimensions. The corresponding third performance assessment data is generated and displayed based on the third assessment dimension and the third standard performance data. This system can flexibly respond to the specific needs of different users and dynamically adjust the assessment strategy to ensure that the feedback provided always aligns with the user's actual development and technical level.

[0092] For example, for users categorized as "advanced career development," it's assumed they have mastered playing techniques in dimensions such as pitch, note value, fingering, and hand position. What they need to refine are more complex playing techniques in dimensions like dynamics and pedaling. The corresponding first-level evaluation dimension is set to dynamics and pedaling, while the second-level evaluation dimension is set to note value, fingering, and hand position. Pitch is a fundamental playing ability that advanced career development users have mastered, therefore it is not included in either the first or second-level evaluation dimensions. If their fingering performance is poor over a prolonged period, their individual identifier is set to "fingering abnormality" to reflect their current skill level and development stage.

[0093] Further, the three evaluation dimensions corresponding to the group identifier were determined to be dynamics and pedaling, while the three evaluation dimensions corresponding to the individual identifier were hand shape and fingering. Since there is a strong correlation between fingering and hand shape, both can be identified as evaluation dimensions to ensure the effectiveness and practicality of performance feedback.

[0094] It should be understood that when the second performance evaluation data continuously exceeds the preset threshold range within the second preset time period, the abnormal evaluation dimension corresponding to the abnormal second performance evaluation data is determined. The abnormal evaluation dimension can be used as one of the three evaluation dimensions determined based on individual identifiers. At the same time, individual identifiers can be analyzed to determine other evaluation dimensions that are strongly correlated with the abnormal evaluation dimension as one of the three evaluation dimensions.

[0095] It should be understood that combining group and individual identifiers allows for a more accurate understanding of a user's current skill level and development stage, enabling dynamic adjustments to the displayed data and providing feedback and support that better meets actual needs. Group identifiers are the result of classifying users based on their primary motivations and playing abilities. They provide basic information about the broad category to which a user belongs, but may not fully capture the unique circumstances of each user. Individual identifiers further refine user characteristics, especially for users at specific developmental stages or with special needs. For example, an advanced professional might find it difficult to maintain high standards of fingering and hand position when refining their pedaling skills due to distractions during practice. In this case, setting individual identifiers and then customizing corresponding feedback schemes based on these dual identifiers improves the efficiency of assisted practice.

[0096] For example, the first and second performance evaluation data can be displayed simultaneously. This not only allows for focusing on the user's main needs based on the first evaluation dimension, but also enables flexible responses to special circumstances based on the second evaluation dimension, ensuring that the feedback provided always aligns with the user's actual needs and development stage. On the other hand, the third performance evaluation data will replace the display of the first performance evaluation data, further aligning with the user's actual needs.

[0097] Please see Figure 2 , Figure 2 This is a schematic block diagram of an auxiliary practice system for playing musical instruments provided in an embodiment of this application. The auxiliary practice system for playing musical instruments can be configured in a server to execute the aforementioned auxiliary practice method for playing musical instruments.

[0098] like Figure 2 As shown in the figure, this application embodiment also provides an auxiliary practice system 200 for playing musical instruments, the system comprising; The historical performance module 201 is used to obtain the user's historical performance data within a preset time period. The group identification module 202 is used to extract several performance behavior features from the historical performance data and determine the user's group identification based on the several performance behavior features. The evaluation dimension module 203 is used to select one evaluation dimension from multiple evaluation dimensions based on the group identifier according to a preset visualization requirement mapping table; wherein, the preset visualization requirement mapping table is used to define the mapping rules between the group identifier and the evaluation dimension; The evaluation data module 205 is used to collect user performance data generated during the performance in real time, and obtain the first standard performance data corresponding to the first evaluation dimension; evaluate the user performance data based on the first standard performance data, and generate the first performance evaluation data corresponding to the first evaluation dimension. The data display module 205 is used to display the first performance evaluation data corresponding to the first type of evaluation dimension in real time.

[0099] For example, the instrument playing auxiliary practice system 200 also includes: a second assessment dimension module, a second assessment data module, and a second data display module.

[0100] The second evaluation dimension module is used to select two types of evaluation dimensions from multiple evaluation dimensions based on the group identifier; The second evaluation data module is used to obtain the second standard performance data corresponding to the two types of evaluation dimensions; evaluate the user performance data based on the second standard performance data, and generate the second performance evaluation data corresponding to the two types of evaluation dimensions. The second data display module is used to display the second performance evaluation data corresponding to the two evaluation dimensions in real time when the second performance evaluation data continuously exceeds the preset threshold range within a first preset time period.

[0101] For example, the auxiliary practice system 200 for musical instrument playing also includes: an individual identification module, a third assessment dimension module, and a third data display module.

[0102] The individual identification module is used to determine the user's individual identification based on the two types of evaluation dimensions when the second performance evaluation data continuously exceeds the preset threshold range within a second preset duration, wherein the second preset duration is longer than the first preset duration; The third evaluation dimension module is used to reselect three evaluation dimensions from multiple evaluation dimensions based on the group identifier and the individual identifier, and obtain the third standard performance data corresponding to the three evaluation dimensions; evaluate the user performance data based on the third standard performance data, and generate the third performance evaluation data corresponding to the three evaluation dimensions. The third data display module is used to display the third performance evaluation data corresponding to the three evaluation dimensions in real time.

[0103] For example, the evaluation dimensions include: pitch dimension, fingering dimension, hand shape dimension, duration dimension, force dimension, and pedal dimension.

[0104] For example, the performance behavior features include performance accuracy features, performance repertoire features, and performance frequency features. The group identification module 202 also includes a target behavior feature submodule and a behavior feature matching submodule.

[0105] The target behavior feature submodule is used to define several target behavior features for each of the group identifiers; The behavior feature matching submodule is used to match several performance behavior features with several target behavior features of each group identifier. If the match is successful, the group identifier that has been matched is determined as the user's group identifier.

[0106] For example, the historical performance data includes performance accuracy data, and the performance accuracy features include performance accuracy level and / or accuracy change trend; the group identification module 202 further includes a performance accuracy feature submodule, used to compare at least one of the performance accuracy data within the preset time period with a preset accuracy threshold to determine the performance accuracy level; and / or, compare multiple of the performance accuracy data within the preset time period to obtain the accuracy change trend.

[0107] For example, the historical performance data includes performance repertoire data, and the performance repertoire features include repertoire difficulty level and / or special preference repertoire; the group identification module 202 also includes a performance repertoire feature submodule, used to compare at least one of the performance repertoire data within the preset time period with a preset difficulty threshold to determine the repertoire difficulty level; and / or, to determine the special preference repertoire based on multiple performance repertoire data within the preset time period.

[0108] For example, the above-described method or system can be implemented as a computer program. This computer program can run on a computer device or a smart musical instrument, wherein the instrument has keys arranged in a fixed order, and each key has a fixed pitch register; these keys can be arranged to form a keyboard. The instrument can be a piano, organ, accordion, or electronic keyboard, etc.

[0109] Please see Figure 3 , Figure 3 This is a schematic block diagram of the structure of a computer device provided in an embodiment of this application. The computer program can run as follows: Figure 3 It runs on the computer device shown. Figure 3 As shown, the computer device includes a processor, memory, and network interface connected via a system bus, wherein the memory may include non-volatile storage media and internal memory.

[0110] Non-volatile storage media can store operating systems and computer programs. These computer programs include program instructions that, when executed, cause the processor to perform auxiliary practice methods for playing any musical instrument.

[0111] The processor provides computing and control capabilities, supporting the operation of the entire computer device.

[0112] Internal memory provides an environment for the execution of computer programs stored in non-volatile storage media. When executed by a processor, the computer program can enable the processor to perform auxiliary practice methods for playing any musical instrument.

[0113] This network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0114] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0115] In one embodiment, the processor is configured to run a computer program stored in memory to perform the following steps: S101. Obtain the user's historical performance data within a preset time period; S102. Extract several performance behavior features from the historical performance data, and determine the user's group identifier based on the several performance behavior features; S103. Based on a preset visualization requirement mapping table, select one evaluation dimension from multiple evaluation dimensions according to the group identifier; wherein, the preset visualization requirement mapping table is used to define the mapping rules between the group identifier and the evaluation dimension; S104. Collect user performance data generated during the performance in real time, and obtain the first standard performance data corresponding to the first evaluation dimension; evaluate the user performance data based on the first standard performance data, and generate the first performance evaluation data corresponding to the first evaluation dimension. S105. Display the first performance evaluation data corresponding to the first type of evaluation dimension in real time.

[0116] For example, the processor is used to run a computer program stored in a memory, and is also used to implement the steps of the auxiliary practice method for playing musical instruments provided in any embodiment of this application, which will not be repeated here.

[0117] The embodiments of this application also provide a computer-readable storage medium storing a computer program, the computer program including program instructions, and the processor executing the program instructions to implement any of the auxiliary practice methods for musical instrument playing provided in the embodiments of this application.

[0118] The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiments, such as the hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, SmartMedia Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the computer device.

[0119] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for assisting in the practice of playing a musical instrument, characterized in that, Including the following steps: S101. Obtain the user's historical performance data within a preset time period; S102. Extract several performance behavior features from the historical performance data, and determine the user's group identifier based on the several performance behavior features; S103. Based on a preset visualization requirement mapping table, select one evaluation dimension from multiple evaluation dimensions according to the group identifier; wherein, the preset visualization requirement mapping table is used to define the mapping rules between the group identifier and the evaluation dimension; S104. Collect user performance data generated during the performance in real time, and obtain the first standard performance data corresponding to the first evaluation dimension; evaluate the user performance data based on the first standard performance data, and generate the first performance evaluation data corresponding to the first evaluation dimension. S105. Display the first performance evaluation data corresponding to the first type of evaluation dimension in real time.

2. The method according to claim 1, characterized in that, The method further includes: Two evaluation dimensions are selected from the multiple evaluation dimensions based on the group identifier; Obtain the second standard performance data corresponding to the two types of evaluation dimensions; evaluate the user performance data based on the second standard performance data, and generate the second performance evaluation data corresponding to the two types of evaluation dimensions; When the second performance evaluation data continuously exceeds the preset threshold range within the first preset time period, the second performance evaluation data corresponding to the two evaluation dimensions is displayed in real time.

3. The method according to claim 2, characterized in that, The method further includes: When the second performance evaluation data continuously exceeds the preset threshold range within the second preset duration, the user's individual identifier is determined according to the two evaluation dimensions, wherein the second preset duration is longer than the first preset duration; Based on the group identifier and the individual identifier, three evaluation dimensions are reselected from multiple evaluation dimensions, and the third standard performance data corresponding to the three evaluation dimensions is obtained; the user performance data is evaluated based on the third standard performance data to generate the third performance evaluation data corresponding to the three evaluation dimensions. The third performance evaluation data corresponding to the three evaluation dimensions is displayed in real time.

4. The method according to any one of claims 1-3, characterized in that, The evaluation dimensions include: pitch dimension, fingering dimension, hand shape dimension, duration dimension, force dimension, and pedal dimension.

5. The method according to claim 1, characterized in that, The performance behavior characteristics include performance accuracy characteristics, repertoire characteristics, and performance frequency characteristics. S102 includes: Define several target behavioral characteristics for each of the aforementioned group identifiers; Several performance behavior features are matched with several target behavior features of each group identifier. If a match is successful, the group identifier that has been matched is identified as the user's group identifier.

6. The method according to claim 1 or 5, characterized in that, The historical performance data includes performance accuracy data, and the performance accuracy characteristics include performance accuracy level and / or accuracy change trend; S102 includes: The performance accuracy level is determined by comparing at least one performance accuracy data within the preset time period with a preset accuracy threshold; and / or, the accuracy change trend is obtained by comparing multiple performance accuracy data within the preset time period.

7. The method according to claim 1 or 5, characterized in that, The historical performance data includes performance repertoire data, and the characteristics of the performance repertoire include the difficulty level of the repertoire and / or special preference for repertoire. S102 includes: The difficulty level of the piece is determined by comparing at least one of the performance pieces data within the preset time period with a preset difficulty threshold; and / or, the special preference piece is determined based on multiple performance pieces data within the preset time period.

8. An auxiliary practice system for playing a musical instrument, characterized in that, The system includes: The historical performance module is used to obtain the user's historical performance data within a preset time period; The group identification module is used to extract several performance behavior features from the historical performance data and determine the user's group identification based on the several performance behavior features; The evaluation dimension module is used to select one evaluation dimension from multiple evaluation dimensions based on the group identifier, according to a preset visualization requirement mapping table; wherein, the preset visualization requirement mapping table is used to define the mapping rules between the group identifier and the evaluation dimension; The evaluation data module is used to collect user performance data generated during the performance in real time and obtain the first standard performance data corresponding to the first evaluation dimension; evaluate the user performance data based on the first standard performance data to generate the first performance evaluation data corresponding to the first evaluation dimension. The data display module is used to display the first performance evaluation data corresponding to the first type of evaluation dimension in real time.

9. A computer device, characterized in that, The device includes a memory and a processor; The memory is used to store computer programs; The processor is configured to execute the computer program and, in executing the computer program, implement the auxiliary practice method for playing musical instruments as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, causes the processor to implement the auxiliary practice method for playing a musical instrument as described in any one of claims 1 to 7.

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