AI Audio Track Separation for Seamless DJ Song Blending
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional DJ equipment and methods struggle to seamlessly blend songs without vocal or tonal clashes, especially in genres like Pop and Hip Hop, due to lack of individual source tracks and inefficient decomposition techniques, which are time-consuming and complex, limiting creative freedom during live performances.
Innovation Solution
A method and device using AI systems with neural networks to decompose mixed audio data into distinct timbres, allowing intuitive control through volume settings on selected tracks, enabling seamless transitions and creative remixing without requiring individual volume adjustments for each track.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional decomposition techniques are used to separate audio tracks, then individual source tracks can be obtained, but the process is time-consuming and complex
Solution Approach 1:
The patent replaces conventional mechanical signal processing decomposition techniques with an AI-based neural network system. The neural network automatically learns and separates audio tracks through training, eliminating the need for complex manual decomposition processes and significantly reducing processing time while maintaining or improving separation accuracy.
Solution Approach 2:
The AI system performs self-learning and automatic track separation without requiring manual intervention or complex configuration. The neural network automatically identifies and separates different audio tracks (vocals, instruments, etc.) from mixed audio signals through its trained capabilities, making the decomposition process autonomous and efficient.
2Ease of operation
If individual volume adjustments are made for each decomposed track, then precise control is achieved, but the operation becomes complex and time-consuming
Solution Approach 1:
The patent merges multiple individual track controls into a single master volume control mechanism. Instead of requiring separate volume adjustments for each decomposed track, the system allows unified control of all tracks through one parameter, dramatically simplifying the user interface and operation while maintaining the ability to control individual tracks when needed.
Solution Approach 2:
The volume control mechanism is designed to serve multiple functions simultaneously: it can control all decomposed tracks collectively as a group, or individual tracks can be selectively controlled when required. This multi-functional approach reduces the number of control parameters needed while preserving operational flexibility and precision when necessary.
3Adaptability or versatility
If AI decomposition is used to separate audio tracks, then creative freedom is enhanced, but system complexity increases
Solution Approach 1:
The patent replaces complex manual audio manipulation systems with an AI-based neural network that automatically performs track separation and manipulation. This substitution enables enhanced creative freedom through automatic decomposition into multiple controllable tracks (vocals, instruments, etc.) while the AI handles the computational complexity, presenting a simplified interface to users.
Solution Approach 2:
The neural network acts as an intermediary between the mixed audio input and the user's creative control. It automatically separates and organizes audio tracks, providing a structured foundation for creative manipulation. This intermediary handles the complexity of decomposition internally, allowing users to focus on creative decisions without directly managing system complexity.
Data Source
AI summary
A system processes mixed input data using a neural network trained to separate audio data of predetermined timbres from mixed audio data and to obtain a group of decomposed tracks comprising at least first, second, and third decomposed audio tracks representing audio signals of first, second, and third predetermined timbres, respectively. The system reads a control input representing a setting of a first volume level and of a second volume level. The system recombines at least a first selected track and a second selected track selected from the group of decomposed tracks to generate a first recombined track. The system recombines the first recombined track at the first volume level with at least a third track selected from the group of decomposed tracks, at the second volume level, to obtain a second recombined track. The system plays the audio data based on the second recombined track.