Hierarchical Audio Codec for Scalable High-Fidelity Generation
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Solution Overview
Problem
Existing audio generation techniques lack scalability and robustness in handling complex and varied contextual inputs with high fidelity.
Innovation Solution
A hierarchical codec that extracts top-level semantic codes, mid-level structural codes, and low-level signal codes, and low-level signal codes, ensuring each layer captures intended features at its respective level of abstraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a single-level audio generation model is used, then the model structure is simple, but it cannot effectively handle complex contextual inputs with high fidelity
Solution Approach 1:
The audio generation model is segmented into three hierarchical levels: top-level semantic generation, mid-level structural generation, and low-level signal generation. Each level processes different aspects of audio data, with the top level handling semantic meaning, the mid level handling structural organization, and the low level handling signal details. This segmentation allows the system to achieve high fidelity audio generation by addressing different aspects at appropriate levels of abstraction.
Solution Approach 2:
The patent introduces a hierarchical dimension to the audio generation process, transforming a single-level approach into a multi-level architecture. By adding the dimension of hierarchical abstraction levels, the system can process complex contextual inputs more effectively while maintaining manageable model complexity through specialized processing at each level.
2Adaptability or versatility
If audio data is processed at a single level of abstraction, then processing is straightforward, but scalability to handle varied contextual inputs is limited
Solution Approach 1:
The processing architecture is segmented into three distinct hierarchical levels, each responsible for specific aspects of audio data. The top level processes semantic context, the mid level processes structural patterns, and the low level processes signal characteristics. This segmentation enables the system to scale to handle varied contextual inputs by assigning different types of information to appropriate processing levels.
Solution Approach 2:
Each hierarchical level in the processing architecture is designed to handle multiple types of contextual inputs through specialized processing. The top level can process various semantic contexts, the mid level can handle different structural patterns, and the low level can process diverse signal characteristics, making the overall system universally adaptable to varied inputs.
3Manufacturing precision
If semantic tokens, structural tokens, and audio signal tokens are generated simultaneously, then the generation process is simple, but the model cannot capture features at different levels of abstraction
Solution Approach 1:
The token generation process is segmented into three sequential hierarchical stages: semantic token generation at the top level, structural token generation at the mid level, and audio signal token generation at the low level. Each stage processes and refines information from the previous stage, allowing the model to capture features at different levels of abstraction with high accuracy while maintaining a structured generation process.
Data Source
AI summary
Systems, methods, software, and devices are disclosed herein process context data to encode one or more semantic elements of a desired audio composition in a semantic token sequence, process the semantic token sequence to encode one or more structural elements of the desired audio composition in a structural token sequence disentangled from the semantic token sequence, and process the structural token sequence to encode one or more audio signal elements of the desired audio composition in an audio signal token sequence disentangled from the structural token sequence. The semantic token sequence, the structural token sequence, and the audio signal token sequence may then be processed to generate at least a portion of the desired audio composition.