LLM-Guided Audio Loop Selection for Faster Track Generation
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Solution Overview
Problem
Existing audio production systems require users to manually search, select, and synchronize multiple audio loops, which is time-consuming and overwhelming, especially for new users, lacking an efficient way to automatically combine and align loops based on user input.
Innovation Solution
A system utilizing a large language model (LLM) to translate text-based prompts into song specifications, selecting and formatting audio loops to match desired BPM, scale, and key, generating a starting point for further editing and providing alternative loops.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users manually search, select, and synchronize multiple audio loops, then they can create customized tracks, but the process becomes time-consuming and overwhelming
Solution Approach 1:
The system performs automatic track generation by having the computer execute the complex tasks of searching, selecting, and synchronizing audio loops without requiring manual user intervention for each step, while still allowing user customization through prompts and parameters
Solution Approach 2:
The system pre-processes and organizes audio loops by metadata (BPM, key, genre, mood) before user interaction, so that when a user provides a prompt, the system can quickly retrieve and combine pre-organized loops that match the desired criteria
2Productivity
If the system automatically selects and combines audio loops, then track generation becomes faster, but the level of control and customization decreases
Solution Approach 1:
The system dynamically adjusts between automatic and manual control modes, allowing users to specify the degree of automation through prompt complexity and system parameters, with the ability to review and modify generated tracks
Solution Approach 2:
The system provides feedback to users about the selected loops and generated tracks, allowing users to review the automatic selection results and make adjustments, creating a loop where user feedback refines the automatic generation process
3Adaptability or versatility
If the system stores audio loops in multiple keys and rhythms, then more musical options are available, but the storage requirements and data management complexity increase
Solution Approach 1:
The system segments the audio loop library by metadata categories (BPM, key, genre, mood, instrument type), organizing loops into discrete searchable segments that can be efficiently retrieved and combined without storing redundant copies of each loop in every possible variation
Solution Approach 2:
The system uses parameter-based organization where audio loops are tagged with metadata parameters (BPM, musical key, genre, mood), allowing the system to retrieve and adapt loops by changing these parameters rather than storing separate copies for each variation
Data Source
AI summary
The present disclosure relates to a system and method for selecting and generating audio using a large language model. The method includes receiving from a user a text-based prompt for a desired song, generating a song specification from a prompt that includes the text-based prompt and instructions on how to create a suitable instruction file format for representing the requested song, for each of the list of tracks in the song specification, generating a ranked list of potential sound loops matching the song specification for a selected track, selecting a sound loop from the ranked list of potential sound loops for each of the list of tracks, and generating a track specification file including the sound loop selected for each of the list of tracks.