An AI vocal music private teaching accompaniment system

Abstract

The application discloses a user AI vocal music private teaching accompaniment system and belongs to the technical field of artificial intelligence vocal music private teaching. In order to solve the technical defects of high copyright risk, waste of computing power, teaching homogeneity and no private exclusive archives of the existing vocal music teaching platform, the application discloses a user self-uploaded audio for personal learning only. The system only extracts structured MIDI data of pure music scores, completely strips the original singer's voice characteristics, relies on the self-developed original non-copyright virtual singer timbre synthesis virtual demonstration human voice, and generates standardized teaching data packages. The application builds a zero-interference immersive accompaniment closed loop, realizes the standard automatic advancement, unmet silent playback and multiple unmet frustration avoidance skipping mechanism, constructs a user encrypted personal archive cabinet, reuses data to save computing power and support personalized tuning, combines the solidified client iteration-free architecture and AI three-track chorus, accompaniment and duet system, and realizes the exclusive AI vocal music private teaching service without copyright risk, privacy and personalization.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence voice evaluation, immersive human-computer interaction, fixed client architecture, cloud-based AI audio reconstruction, and personalized vocal coaching. Specifically, it relates to a user-uploaded and analyzed exclusive AI virtual accompaniment singing coaching immersive vocal coaching method and cloud-based fixed system. Background Technology

[0002] Most existing vocal training and karaoke practice products adopt a centralized music library model, storing and distributing a large amount of song audio resources, posing a high risk of copyright infringement. Furthermore, existing technologies suffer from several significant technical flaws, failing to meet users' needs for personalized AI tutoring with low computational overhead. Firstly, traditional platforms rely on commercially licensed music libraries, unable to support user-defined song practice, resulting in poor adaptability and prominent copyright risks. Secondly, the lack of a user-owned data archiving mechanism means that repeated cloud analysis is required for users to practice the same song repeatedly, leading to high computational consumption, low loading efficiency, and significant resource waste. Thirdly, traditional AI audio processing only performs simple adaptations of the original audio, failing to completely remove the original singer's timbre, texture, and singing style, resulting in insufficient originality and copyright tracing risks. Fourthly, universal music libraries are uniformly adapted for all users, lacking personal vocal profiles and failing to provide personalized teaching adjustments based on individual vocal characteristics. Fifthly, traditional practice interfaces are cluttered with information; scoring pop-ups, real-time error correction, and voice feedback frequently interrupt the singing rhythm, disrupting the immersive practice experience. Sixth, traditional system function iterations and music library updates all rely on client version upgrades, which are cumbersome to maintain, have poor compatibility, and high expansion costs.

[0003] In summary, existing technologies are all shared music library-style tool products, which generally suffer from high copyright risks, waste of computing power, weak originality of materials, lack of private and exclusive files, homogenized teaching, and strong interactive interference. The industry lacks an integrated AI vocal coaching technology solution that enables users to upload and use their own music, perform structured analysis of pure sheet music, reconstruct original virtual vocals, reuse computing power for private files, and provide zero-interference immersive private tutoring. Summary of the Invention

[0004] The purpose of this invention is to overcome the many shortcomings of existing technologies and build a platform-free, user-controlled, and exclusive AI-assisted vocal coaching system. This system addresses the technical problems of existing products, such as high copyright risks, redundant consumption of computing power, lack of original materials, lack of personal vocal archives, homogenized teaching, strong interference during practice, and cumbersome system iteration. It enables users to upload and use their own music, perform structured analysis of pure sheet music, reconstruct original virtual vocals, reuse of personal archives, immersive and interference-free private coaching, and personalized adaptive teaching tailored to each individual.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: This invention pioneers a user-controlled, music library-free private tutoring architecture. The platform does not build a music library, store, or distribute any song audio resources. All practice materials are uploaded by users themselves, for personal learning use only, and users are responsible for the compliance of the materials, thus avoiding platform copyright risks from the source. At the same time, it adopts a pure music score MIDI structured parsing mechanism, completely stripping away the original singer's vocal timbre, vocal characteristics, and singing style, and completely abandoning the traditional processing mode based on the adaptation of the original singer's audio.

[0006] This invention features a unique and original virtual vocal reconstruction technology. Based on purified pure musical score MIDI data, it calls upon the platform's self-developed, original, and copyright-free virtual singer timbre library to resynthesize a brand-new virtual demonstration vocal, generating standardized, reusable, and copyright-free sentence-by-sentence teaching data packages, thus achieving compliant and independent mass production of vocal teaching materials.

[0007] The core innovation of this invention is a user-independent encrypted personal file cabinet mechanism, which allows users to privately store structured sheet music data, phrasal markers, silent annotations, practice records, weak phrases, personal vocal characteristics, and personalized error-tolerant parameters. When practicing the same song repeatedly, the user can directly access their private file data without repeated cloud parsing, significantly saving computing power and improving loading efficiency. Simultaneously, it iterates user-specific acoustic models based on long-term practice data, achieving personalized adaptive coaching adjustments tailored to each individual. This invention combines a zero-interference immersive coaching loop, AI-exclusive three-track chorus, backing vocals, duets, and a client-side fixed, non-iterative architecture to construct a comprehensive, private, exclusive, and personalized AI vocal coaching system. Example 1: User-uploaded content, pure sheet music analysis, and original virtual vocal generation

[0008] This invention platform does not build a public music library, nor does it store, forward, or disseminate any song audio resources. All practice audio is uploaded by users themselves and is for personal learning use only. Users are solely responsible for the compliance of the materials. After receiving the user-uploaded audio, the cloud only performs structured score analysis, accurately extracting pure MIDI score structured data including pitch, rhythm, duration, and phrase-by-phrase timeline. It completely identifies and thoroughly removes non-score redundant features such as the original singer's timbre, vocal texture, and personalized singing style, leaving no original singer vocal information. Based on the purified pure score data, the system calls the platform's self-developed original copyright-free virtual singer timbre library to resynthesize and generate a brand-new virtual human voice demonstration audio. This is then packaged into a standardized phrase-by-phrase teaching data package, combined with preset phrase parameters, qualification thresholds, and recording parameters. All teaching materials are generated by the platform itself, eliminating copyright risks at the source.

[0009] Example 2: Zero-interference immersive AI personal tutoring with sentence-by-sentence practice and frustration prevention mechanism After loading the user-specific standardized teaching data package or local encrypted archive data, the client initializes the private tutoring scenario, playing original virtual human voice demonstration audio line by line. Once the demonstration is complete, accompaniment playback and vocal recording are automatically initiated. The AI ​​evaluation model calculates a performance matching score based on a weighted average of pitch, rhythm, and duration, adaptively adjusting the weighting according to the song's style. When the performance score meets the standard, the system provides no pop-ups, voice, or text prompts, and the interface smoothly transitions to the next line. When the score is below the standard, all scoring data, error correction comments, and other distracting information are blocked; only the virtual human voice demonstration audio for the current phrase is repeatedly played, guiding the user to imitate and correct themselves. After the number of retries for a single phrase reaches a preset threshold, the system automatically triggers an anti-frustration protection mechanism, smoothly transitioning to the next phrase. Skipped phrases are automatically archived as weak phrases and added to the user's encrypted personal archive, effectively protecting the user's motivation while ensuring an immersive practice experience.

[0010] Example 3: Computational power reuse and private data management of user-encrypted personal file cabinets This invention establishes an independent, encrypted user personal archive cabinet, creating a dedicated private vocal database for each user. The archive data is only accessible to the user and is not publicly disclosed, shared, or leaked in the cloud. The archive cabinet stores the following content: structured MIDI data of the song's pure sheet music, phrase-by-phrase markings, multilingual silent annotation data, personal practice records, weak phrase tags, personal vocal range characteristics data, personalized scoring and tolerance parameters, and accompaniment adaptation parameters. When a user uploads a song for the first time, cloud-based AI analysis and virtual vocal synthesis are performed to generate a teaching data package, which is then archived and stored. When a user practices the same song a second time, the system directly reads the structured data from the encrypted archive, eliminating the need for repeated cloud analysis and vocal synthesis. This significantly reduces cloud computing power consumption, shortens material loading time, and enables efficient reuse of private teaching materials.

[0011] Meanwhile, relying on the long-term practice data accumulated in the filing cabinet, the system continuously extracts the user's multi-dimensional singing characteristics, iteratively optimizes the user's exclusive acoustic model, and provides accurate data support for personalized private coaching and adaptive tuning for each individual. Without compromising the zero-interference immersive practice experience, it continuously optimizes exclusive teaching parameters to match the user's individual singing characteristics.

[0012] During regular practice, the system seamlessly collects acoustic data of the user's singing voice, extracting multi-dimensional core features such as breath stability, breathing rhythm, vocal frequency band, vocal range, and pitch deviation. It continuously builds and iteratively updates the user's exclusive acoustic model, without requiring manual parameter configuration throughout the process, providing data support for personalized teaching and tuning.

[0013] This invention, building upon a user-centric, zero-interference personal coaching system, features a unique AI-powered automatic generation technology for backing vocal parts. During the creation of standardized teaching data packages, the cloud independently generates a standardized, reusable AI backing vocal track based on the song's arrangement structure and choral counterpoint rules. This track is independent of the user's lead vocal track and instrumental track, creating a standardized three-track parallel audio architecture. Based on the song's style and vocal type, the cloud automatically matches exclusive AI-generated original vocal lines, including male / female voices, high / low notes, and harmonic pads, generating professional backing vocals with interwoven vocal parts and layered harmonies, completely differentiating it from the traditional single-melody sing-along mode.

[0014] After the user activates the AI-powered backing vocal mode, the pre-generated AI backing vocals will play the corresponding vocal parts strictly according to the song's sequence. The user independently performs the lead vocal part, creating a precise and well-coordinated human-machine layered chorus effect that highly replicates the atmosphere of professional duets and multi-person harmonies. The system supports independent volume adjustment for the AI ​​backing vocals, adapting to different practice stages such as beginner follow-along training and advanced layered vocal training. It also fully reuses the core logic of zero-interference interaction, and is compatible with all functions such as adaptive music style evaluation, personalized parameter adaptation, and archive and review of weak musical phrases.

[0015] This AI-powered character-assisted singing mechanism completely breaks through the limitations of traditional vocal teaching products, which are limited to individual singing practice. Through independently developed AI-powered voice track generation technology, it achieves standardized and professional human-machine choral singing practice, maintaining a zero-interference, immersive practice experience throughout. It effectively fills the technological gap in the industry for the lack of human-like AI choral singing private tutors and can cover the full range of vocal training needs, from beginner solo singing to professional AI character-assisted choral singing.

[0016] Compared to existing traditional mechanical singing teaching tools, this invention constructs a complete exclusive AI vocal coaching technology system, possessing the following core technological advantages: Achieve silent, zero-interference, human-like personal coaching: Abandoning the disruptive interactive mechanisms of traditional products such as pop-ups, real-time comments, and numerical displays, it simulates an immersive, sentence-by-sentence silent guidance mode for real personal coaches, effectively improving users' concentration and vocal training results.

[0017] Construct a standardized closed-loop sentence-by-sentence practice system: Establish a standardized practice mechanism with quantitative achievement verification, automatic advancement, and skipping of limits, standardize the vocal practice process, avoid ineffective repetitive practice, and greatly improve the regularity and effectiveness of training.

[0018] Low maintenance, high scalability, and strong adaptability: It adopts a client-side fixed architecture, eliminating the need for frequent version iterations and reducing maintenance costs; the cloud data package supports automatic downgrading and adaptation of old clients, ensuring high operational stability and allowing for unlimited expansion to new music styles, new languages, and new teaching functions.

[0019] Avoiding copyright risks at the source: Abandoning the traditional platform music library storage and dissemination model, relying on the core mechanism of user-uploaded music for personal use, pure sheet music MIDI analysis, and self-developed original virtual human voice reconstruction, there is no retention of original singer materials and no audio dissemination, thus completely eliminating copyright risks.

[0020] A unique private file computing power reuse system: It builds an independent encrypted personal vocal file cabinet for users, realizing the private storage and efficient reuse of sheet music data, practice records, weak points, and personalized parameters, avoiding the computing power loss caused by repeated AI analysis, while accumulating personal vocal data to support personalized coaching adaptive optimization, taking into account privacy, efficiency and personalization.

[0021] Achieve personalized adaptive teaching for each individual: Based on the user's exclusive acoustic model, teaching parameters are dynamically adjusted to break the limitations of traditional uniform teaching, accurately adapt to the vocal characteristics and singing habits of different users, and greatly improve the personalized training adaptation effect.

[0022] It pioneers an AI-exclusive character-based private coaching mode for duets, backing vocals, and duets: breaking through the limitations of traditional single-person singing as a tool, it achieves professional coaching effects for fixed vocal parts, human-machine layered duets, and duets through independent three-track original vocal generation technology, creating a personal AI-exclusive private coaching mode for duets and filling the gap in the industry for anthropomorphic vocal coaching technology.

[0023] This invention is not limited to the above-described embodiments. Within the scope of knowledge possessed by those skilled in the art, various optimizations and adaptations can be made without departing from the core concept of this invention, and all such modifications and adaptations fall within the protection scope of this invention. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the overall system architecture of the present invention, showing the hierarchical architecture and data interaction logic of the solidified client module, cloud AI data production module, user acoustic feature modeling module, and personalized parameter adaptation module, reflecting the core architectural features of zero client iteration and cloud-driven full-dimensional operation; Figure 2 This is a flowchart of the zero-interference, sentence-by-sentence teaching method of the present invention, which fully demonstrates the fully automatic interactive process of demonstration playback, automatic recording, intelligent evaluation, closed-loop judgment, and over-limit operation, reflecting the core logic of zero-interference and pure auditory closed-loop practice throughout the process. Figure 3 This is a schematic diagram of the cloud-based AI fully automated data package production and personalized teaching adaptation pipeline of the present invention, which includes the entire process logic of AI secondary creation processing of songs, generation of AI-exclusive character backing vocals, separation of multi-track audio, configuration of harmony layering, encapsulation of standardized teaching data packages, collection and modeling of user vocal features, dynamic adaptation of personalized teaching parameters, and silent upgrade of data packages.

Claims

1. A user-uploaded and parsed, zero-interference, immersive AI vocal coaching method, characterized in that, include: The platform does not build a music library, store, or distribute any song audio materials. Users upload their own audio of songs to practice, which is for personal learning use only, and users are responsible for the compliance of the source of their materials. The cloud connects to the user-uploaded audio and extracts only the pure musical score MIDI structured data containing pitch, rhythm, duration, and phrase time axis, completely stripping away and discarding the original singer's timbre, vocal characteristics, and singing style. Based on the platform's self-developed original copyright-free virtual singer timbre, a brand-new virtual human voice demonstration audio is resynthesized based on the pure musical score data to generate a standardized phrase-by-phrase teaching data package. The client loads the standardized teaching data package, plays the virtual human voice demonstration audio phrase by phrase, and simultaneously starts the accompaniment playback and vocal recording. The AI ​​evaluation model performs quantitative matching evaluation of the user's performance based on the weighted weights of pitch, rhythm, and duration. The system performs a zero-interference closed-loop judgment. If the singing score meets the standard, there is no prompt and the interface smoothly moves to the next sentence for learning. If the score does not meet the standard, the virtual human voice demonstration audio of the current sentence is replayed. When the number of retries for a single sentence reaches the preset threshold, the anti-frustration skipping mechanism is automatically triggered. The interface only displays minimal operation controls, and the skipped sentences are automatically marked as weak sentences and archived in the user's exclusive encrypted archive cabinet.

2. The zero-interference immersive AI vocal coaching method according to claim 1, characterized in that, Different evaluation weights are assigned to different music styles. Lyrical music is given priority in being assigned a high weight for voice-over, while rap and fast-paced music are given priority in being assigned a weight for rhythm, so as to achieve accurate evaluation that adapts to different music styles.

3. The zero-interference immersive AI vocal coaching method according to claim 1, characterized in that, The standardized teaching data package includes built-in silent annotations in multiple languages ​​and dialects that can be shown or hidden. It only assists in recognizing lyrics and has no voice broadcasts or text pop-ups to interfere with practice, ensuring an immersive learning experience.

4. A client-side fixed cloud-based AI vocal coaching system, characterized in that, The system is used to execute the method described in any one of claims 1 to 3, comprising a fixed client module and a cloud AI service module. The fixed client module freezes all basic business logic of audio playback, recording acquisition, intelligent evaluation, closed-loop judgment, and interface rendering, requiring no code modification or version iteration. The cloud AI service module has no built-in music library and does not store audio materials for dissemination. It is configured with a pipeline for user audio parsing, score extraction, and virtual human voice synthesis. It can extract pure score MIDI structured data from user-uploaded audio, strip the original singer's vocal features, and then resynthesize virtual demonstration vocals based on the timbre of a self-developed original copyright-free virtual singer, generating standardized teaching data packages in batches. The system builds an independent encrypted personal archive cabinet for users to privately store structured score data, phrase markings, multilingual silent annotations, personal practice records, weak phrases, singing history, and personalized fault tolerance parameters, avoiding repeated parsing and consuming computing power. The client loads standardized teaching data packages distributed from the cloud or reads local encrypted archive data. All teaching content and operating rules are driven by cloud data or user private archives. Function expansion and parameter optimization can be achieved by relying on cloud updates, without requiring client iteration upgrades.

5. The client-side embedded cloud-based AI vocal coaching system according to claim 4, characterized in that, The cloud supports silent incremental upgrades of teaching data packages during idle periods, automatically optimizing audio precision, sentence segmentation parameters, and annotation data to achieve seamless iterative optimization.

6. The client-side embedded cloud-based AI vocal coaching system according to claim 4, characterized in that, The cloud aggregates user practice data, automatically compiles and generates a dataset for reviewing weak musical phrases, and only calls it in scenarios where users actively review, without interfering with the regular practice process.

7. The client-side embedded cloud-based AI vocal coaching system according to claim 4, characterized in that, The system collects user singing acoustic data seamlessly in the cloud, constructs a unique acoustic model that includes breath stability, vocal frequency bands, and vocal range characteristics, and adaptively adjusts accompaniment speed, scoring tolerance threshold, and musical phrase segmentation parameters to achieve personalized vocal coaching.

8. The client-side embedded cloud-based AI vocal coaching system according to claim 4, characterized in that, In the process of generating standardized teaching data packages, cloud-based AI independently generates a fixed and reusable AI-specific character backing vocal track, constructing a three-track independent audio architecture consisting of a pure accompaniment track, a user lead vocal track, and an AI character backing vocal track. Based on the song's style and vocal rules, cloud-based AI matches the corresponding character's vocal line, harmonic backing, and counterpoint, achieving precise timing adaptation for human-machine layered duet and chorus singing, as well as AI character backing vocal accompaniment. Furthermore, the AI ​​backing vocals support independent volume adjustment.

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