Autoregressive Audio Generation with Semantic-Acoustic Representation Layers
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Solution Overview
Problem
Conventional systems struggle to generate high-quality, coherent audio signals, particularly speech and music, without relying on textual annotations, and are limited by computational resources and robustness to diverse and noisy training data.
Innovation Solution
A system utilizing embedding neural networks to map inputs to semantic and acoustic representations, followed by generative neural networks to generate audio signals, which includes a decoder neural network for efficient audio synthesis, allowing for long-term coherence and high-quality audio generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional systems generate audio signals without textual annotations, then they can process diverse and noisy training data, but they fail to produce high-quality coherent audio
Solution Approach 1:
The patent segments the audio generation process into distinct stages: (1) embedding neural network processes input (audio or text) into embedding tokens, (2) semantic representation layer captures semantic content, (3) acoustic representation layer captures acoustic properties, and (4) decoder neural network synthesizes final audio. This segmentation allows each layer to specialize in specific aspects, enabling the system to handle diverse training data while generating high-quality coherent audio.
Solution Approach 2:
The patent introduces intermediary representation layers between the input and final audio output. Specifically, embedding tokens serve as intermediaries that capture input characteristics, semantic representations capture meaning, and acoustic representations capture sound properties. These intermediaries enable the system to process noisy or diverse training data while producing clean, coherent audio output.
2Manufacturing precision
If the system uses multiple neural network layers for audio generation, then audio quality and coherence improve, but computational resources and complexity increase
Solution Approach 1:
The patent divides the complex audio generation task into segmented functional layers: embedding network for input processing, semantic representation for meaning capture, acoustic representation for sound characteristics, and decoder for synthesis. Each layer operates independently with specific computational complexity, allowing optimization of each component while managing overall resource requirements.
Solution Approach 2:
The patent employs parameter changes through token discretization and representation dimensionality control. By converting continuous embeddings into discrete tokens and managing the dimensionality of semantic and acoustic representations, the system achieves high audio quality while controlling computational parameters to manageable levels.
3Stability of the object's composition
If the system processes inputs through multiple representation layers, then long-term coherence is achieved, but processing time increases
Solution Approach 1:
The patent segments the temporal processing into parallel operations across different representation layers. The embedding tokens, semantic representations, and acoustic representations are generated in parallel during each time step, allowing the system to achieve long-term coherence through layered processing while reducing the sequential time penalty.
Solution Approach 2:
The embedding neural network performs preliminary processing of input signals into embedding tokens before they enter the main generation loop. This preliminary action prepares the input in advance, reducing the computational burden during the main generation process and helping to minimize overall processing time while maintaining long-term coherence.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a prediction of an audio signal. One of the methods includes receiving a request to generate an audio signal conditioned on an input; processing the input using an embedding neural network to map the input to one or more embedding tokens; generating a semantic representation of the audio signal; generating, using one or more generative neural networks and conditioned on at least the semantic representation and the embedding tokens, an acoustic representation of the audio signal; and processing at least the acoustic representation using a decoder neural network to generate the prediction of the audio signal.