Audio Element Versioning via Music Theory Rules

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Solution Overview

Problem

Current AI music generation technologies produce low-quality, soulless music due to the inability of algorithms to validate emotional impact and lack of expressiveness, and are limited by the need for large datasets of copyrighted works, leading to commercial viability issues and potential copyright infringement.

Innovation Solution

A system and method for versioning audio elements using an audio element generation engine that analyzes musical format data to determine harmonic and melodic characteristics, matches them with chord progressions using music theory rules, and creates versions of audio elements to fit specific chord progressions, utilizing a diverse dataset of human-recorded or synthesized audio elements.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If a fully generative model is used to create AI music, then the system can generate original melodies and chord progressions, but the music lacks emotional relatability and expressiveness

Engineering Contradiction:

Improveoriginality of melody and chord generationVSAvoidemotional relatability and expressiveness

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The patent introduces an intermediary system that bridges the gap between algorithmic generation and human emotion by using music theory rules and chord progression frameworks as mediators. The system uses predetermined chord progressions and music theory principles to guide the generative process, ensuring that the output adheres to emotionally resonant musical structures while maintaining originality.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system changes parameters by transitioning from completely random generation to generation constrained by music theory parameters. It uses adjustable parameters such as chord progression templates, scale selections, and melodic movement rules to control the emotional character of the generated music while preserving creative flexibility.

Inventive Principle:

Principle #35Parameter changes

2Productivity

If software synthesis is used to express melodic ideas, then the system can generate music efficiently, but the output lacks the expressiveness and emotional performance of a human playing an instrument

Engineering Contradiction:

Improveefficiency of music generationVSAvoidexpressiveness and emotional performance

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system performs preliminary actions by pre-establishing music theory rules, chord progression templates, and melodic frameworks before generation. These pre-configured structures encode emotional performance characteristics that guide the synthesis process, allowing efficient generation while maintaining expressive quality through predetermined emotional templates.

Inventive Principle:

Principle #10Preliminary action

3Measurement precision

If a neural network learns from large audio waveform data sets of copyrighted musical works, then the system can learn musical patterns, but it limits commercial viability and increases copyright infringement risks

Engineering Contradiction:

Improveaccuracy of musical pattern learningVSAvoidcommercial viability and copyright safety

Core Design Contradiction:

Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The patent inverts the conventional approach by instead of learning from copyrighted audio data, it uses public domain music theory rules and chord progression frameworks as the foundation. This inversion allows the system to achieve accurate musical pattern learning through established theoretical principles while avoiding copyright issues entirely, enabling commercial viability.

Inventive Principle:

Principle #13The other way round (Inversion)

4Ease of operation

If the system transforms audio into spectrograms and back into audio, then it can process and generate music, but the results produce lower quality output due to audio noise

Engineering Contradiction:

Improvecapability to process and generate musicVSAvoidquality of audio output

Core Design Contradiction:

Ease of operationVSManufacturing precision

Solution Approach 1:

The system extracts the essential musical information (melody, harmony, rhythm) directly from audio elements without converting to spectrograms. By taking out the unnecessary spectrogram transformation step, it eliminates the associated audio noise and quality degradation while maintaining the core music generation capability.

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS11790876B1Music technique responsible for versioning

Publication Date: 2023.10.17 LIBRARY X MUSIC INC

AI summary

Systems and methods for versioning audio elements used in generation of music are provided. An example method includes receiving musical format data associated with a plurality of audio elements of a melody; determining, based on the musical format data, harmonic and melodic characteristics of each of the plurality of audio elements; matching the harmonic and melodic characteristics to a plurality of chord progressions using predetermined music theory rules, counterpoint rules, and rhythm matching rules; deriving, based on the matching and predetermined melodic movement rules, from the plurality of chord progressions, melodic movement characteristics applicable to using in versioning; and creating, based on the predetermined music theory rules and the melodic movement characteristics, versions of the audio elements that match the chord progressions.