Automated Music Generation Engine Using Pre-Composed Elements
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Solution Overview
Problem
Current AI music generation technologies produce low-quality, emotionally unrelatable music due to algorithmic limitations and require large datasets of copyrighted works, restricting commercial use and raising copyright issues.
Innovation Solution
An automated music generation system that selects audio elements based on user inputs, musical development scenarios, and music theory rules, using pre-composed modular audio and MIDI elements, and incorporates machine learning to optimize emotional impact and coherence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If fully generative AI models are used to create music, then automation and scalability are improved, but music quality and emotional resonance deteriorate
Solution Approach 1:
The system segments music generation into distinct phases: selecting pre-composed musical elements (melodies, chords, rhythms) and then arranging them using AI. This segmentation allows human-created quality elements to be combined with automated arrangement capabilities, resolving the contradiction between automation and quality.
Solution Approach 2:
The system performs preliminary action by using human composers to pre-compose high-quality musical elements before the AI arrangement process. This ensures that the foundational musical content meets quality standards before automated processing begins, preventing quality degradation from pure generative approaches.
2Manufacturing precision
If pre-composed modular audio elements are used, then music quality is improved, but device complexity increases
Solution Approach 1:
The system uses universal pre-composed musical elements that can serve multiple functions across different genres and styles. These modular elements (melodies, chords, rhythms) are designed to be universally applicable, reducing the need for extensive custom content creation while maintaining quality across various music types.
3Reliability
If large datasets of copyrighted works are used for training, then AI model performance is improved, but copyright infringement risks and commercial use restrictions increase
Solution Approach 1:
Instead of training AI models to generate original content that may infringe copyrights, the system copies and recombines existing pre-composed musical elements that are already in the public domain or properly licensed. This approach achieves model performance through arrangement and composition of existing works rather than generating potentially infringing new content.
4Manufacturing precision
If digital audio workstation software is used, then music production capability is improved, but ease of operation deteriorates
Solution Approach 1:
The system enables self-service music production by automatically handling the complex arrangement and composition tasks that traditionally required skilled operators of digital audio workstations. Users can generate professional-quality music without needing to master complex software, as the AI performs the technical production work autonomously.
Data Source
AI summary
Systems and methods for automated music generation are provided. An example method includes receiving, from a user, a user input including at least one of configuration settings and a musical audio input in the form of audio files or an audio recording; selecting, based on the user input and from a plurality of predetermined musical development scenarios, a musical development scenario including a chronologically ordered sequence of set settings; selecting, based on the musical development scenario, from a plurality of event probability scenarios, an event probability scenario defining a probability of a music element creation event; selecting a plurality of sets of audio elements from a plurality of pre-composed audio elements based on the musical development scenario, the user input, the event probability scenario, and predetermined music theory rules; and synthesizing the plurality of sets of audio elements to generate an audio output for providing to the user.