Real-Time Music Generation With Tempo-Invariant Audio Graphs
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Solution Overview
Problem
Existing music streaming services often provide limited song selection due to licensing agreements and the finite number of songs within a genre, failing to tailor music to individual user tastes, environments, and behaviors.
Innovation Solution
A music generator system that uses machine learning algorithms, including neural networks, to create custom music content by selecting and combining audio tracks based on various parameters such as user preferences, environment, and behavior, while also utilizing image representations of audio files and user-defined controls to influence music generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If streaming services use licensing agreements and fixed song catalogs, then song selection is limited, but service cost is controlled
Solution Approach 1:
The system enables self-service by using AI to automatically generate infinite music variations from audio samples without requiring human composers or extensive licensing agreements. The AI model autonomously creates personalized music content based on user preferences and environmental data.
Solution Approach 2:
The system changes parameters by transforming static audio samples into dynamic music compositions through AI processing. It adjusts multiple parameters including tempo, key, instrumentation, and mood to generate infinite variations from a single source material.
2Adaptability or versatility
If streaming services provide fixed song catalogs, then implementation is simpler, but personalization capability is reduced
Solution Approach 1:
The system performs preliminary action by pre-training AI models on vast music datasets and pre-processing audio samples into usable formats. This preparation enables rapid real-time music generation without compromising personalization quality.
Solution Approach 2:
The system replaces mechanical systems by substituting human composers and manual music creation processes with AI algorithms. The AI model automatically generates personalized music without human intervention, dramatically increasing productivity while maintaining high personalization levels.
3Productivity
If streaming services play the same songs repeatedly, then operational cost is reduced, but user engagement decreases
Solution Approach 1:
The system implements periodic action by continuously generating new music variations at regular intervals based on changing user preferences and environmental conditions. This creates endless variety without requiring proportional increases in computational resources through repetitive processing.
Data Source
AI summary
Techniques are disclosed relating to implementing audio techniques for real-time audio generation. For example, a music generator system may generate new music content from playback music content based on different parameter representations of an audio signal. In some cases, an audio signal can be represented by both a graph of the signal (e.g., an audio signal graph) relative to time and a graph of the signal relative to beats (e.g., a signal graph). The signal graph is invariant to tempo, which allows for tempo invariant modification of audio parameters of the music content in addition to tempo variant modifications based on the audio signal graph.