Artificial intelligence music generation model and method for configuring the same
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Solution Overview
Problem
Existing music generation models struggle to balance computational efficiency with high-fidelity audio production, capturing long-term dependencies, and responding to diverse textual prompts while maintaining coherence in musical pieces.
Innovation Solution
An omnidirectional diffusion model combining bidirectional and unidirectional training modes, using a masked autoencoder and omnidirectional latent diffusion model, to capture sequential dependencies and generate high-fidelity music.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autoregressive models are used for text-to-music generation, then the model can capture sequential dependencies and generate coherent music, but the generation speed is slow and computational efficiency is reduced
Solution Approach 1:
The patent combines bidirectional diffusion and unidirectional autoregressive training into a single omnidirectional diffusion model. The model integrates both approaches by allowing latent embeddings to attend to each other in all directions (bidirectional) while also maintaining causal dependencies (unidirectional), achieving both high coherence and fast parallel generation.
Solution Approach 2:
The model dynamically switches between bidirectional and unidirectional attention mechanisms during different training phases and inference steps. During training, the model can flexibly apply bidirectional attention for capturing contextual relationships and unidirectional attention for maintaining temporal coherence, optimizing performance for different generation tasks.
2Productivity
If non-autoregressive models are used for music generation, then the generation speed is fast, but the model cannot capture long-term dependencies and musical coherence is compromised
Solution Approach 1:
The omnidirectional diffusion model merges bidirectional diffusion (for capturing contextual dependencies) with unidirectional autoregressive training (for maintaining temporal coherence). This combination allows the model to achieve fast parallel generation while still capturing long-term sequential dependencies through the integrated attention mechanisms.
3Manufacturing precision
If existing diffusion models are used for music generation, then the model can generate high-fidelity audio, but the model cannot effectively capture sequential dependencies in music flow
Solution Approach 1:
The model dynamically applies different attention mechanisms during training: bidirectional attention for capturing contextual relationships and unidirectional attention for maintaining temporal coherence. This dynamic application allows the model to preserve audio fidelity while effectively capturing sequential dependencies in music flow.
4Manufacturing precision
If models with high parameter counts are used for music generation, then the model can produce impressive results in terms of audio quality and diversity, but the models become impractical for real-time applications and resource-constrained devices
Solution Approach 1:
The patent employs parameter-efficient training techniques including low-rank adaptation (LoRA) and quantization-aware training. These methods allow the model to achieve high audio quality and diversity with reduced parameter counts, making the models suitable for real-time applications and resource-constrained devices while maintaining impressive generation results.
Data Source
AI summary
The present disclosure provides a method for configuring a learning model for music generation and the corresponding learning model. The method includes training a masked autoencoder with training data comprising a combination of a reconstruction loss over time and frequency domains and a patch-based adversarial objective operating at different resolutions. An omnidirectional latent diffusion model is trained based on music data represented in a latent space to obtain a pretrained diffusion model. The pretrained diffusion model is fine-tuned based on text-guided music generation, bidirectional music in-painting, and unidirectional music continuation. The method enables high-fidelity music generation conditioned on text or music representations while maintaining computational efficiency.