Rule-Based AI Music Generation for Complete Compositions
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Solution Overview
Problem
Existing music generation software requires users to select pre-generated loops, resulting in unfinished and musically unpleasing compositions due to the complexity of creating a complete music work.
Innovation Solution
A generative music system using rule-based algorithms and AI technology, organized in selectable templates, that guides users through an input, data determination, and render phase to create structured and customizable music compositions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually select and arrange pre-generated loops to create music compositions, then the process becomes accessible to novice users without music theory knowledge, but the compositions remain unfinished and musically unpleasing due to user inability to complete full compositions
Solution Approach 1:
The system enables automated music composition where the software itself performs the creative work of assembling and arranging loops into complete compositions, rather than requiring users to manually complete the entire composition process. The AI model generates full music works autonomously based on user inputs or templates, allowing the system to serve itself in the creative process.
Solution Approach 2:
The patent replaces the manual mechanical process of loop selection and arrangement with an AI-based automated system. The machine learning model substitutes human creative labor in composing music, transforming the mechanical assembly of loops into an intelligent, autonomous composition process that generates complete and musically coherent works.
2Adaptability or versatility
If the system provides simplified loop selection interfaces for novice users, then more users can engage in music creation, but the music works remain incomplete and abandoned due to the complexity of creating full compositions
Solution Approach 1:
The AI composition model acts as an intermediary between user inputs and final music compositions. Instead of users directly creating complete compositions, the AI model mediates the process by taking simple user inputs (such as genre selection or mood preferences) and transforming them into complete, polished music works, thereby bridging the gap between novice capability and professional output quality.
Solution Approach 2:
The system performs preliminary actions by pre-generating and organizing loops and musical elements before user interaction. Templates and pre-arranged loop collections are prepared in advance, allowing users to start with ready-made musical building blocks rather than creating everything from scratch, thus reducing the complexity of completing full compositions.
3Ease of operation
If traditional music generation software provides graphical interfaces for loop arrangement, then users can visually create music without music theory knowledge, but users can only generate one or two song parts before abandoning the project
Solution Approach 1:
The AI model enables continuous automated composition that can generate complete music works without interruption. Once initiated, the system continuously processes and generates full compositions autonomously, eliminating the need for users to manually work through each song part sequentially, thereby maintaining continuous productive action throughout the composition process.
Solution Approach 2:
The patent replaces the time-consuming manual mechanical process of arranging multiple song parts with automated AI composition. The machine learning model rapidly generates complete compositions in a fraction of the time required for manual arrangement, substituting human time investment with computational efficiency while maintaining visual interface accessibility.
Data Source
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AI summary
According to a first embodiment, there is presented here a method of rule-based algorithmic generative music system. Templates are provided that contain all the information needed to build a music work, wherein this information combines the vast audio material stored in databases efficiently for selection, arrangement, and adaptation and in the end generation of the output music work.