Latent-Domain Signal Coding for Generative Audio Reconstruction
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Solution Overview
Problem
Generative models struggle to efficiently model audio signals due to complexity constraints, limited training data, and algorithm limitations, leading to modeling mismatches and inefficiencies in coding schemes.
Innovation Solution
A coding scheme that allocates bits in a bitstream for conveying a quantized latent frame, allowing for improved rate-distortion scalability and flexibility, and facilitates packet loss concealment by using generative models trained for zero bit-rate conditioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a generative model is used for signal reconstruction, then signal synthesis capability is improved, but modeling accuracy deteriorates due to complexity constraints and algorithm limitations
Solution Approach 1:
The patent introduces an intermediate latent representation domain that bridges the gap between the input signal and the generative model output. By transforming the signal into a latent space and back, the system achieves better modeling accuracy while maintaining synthesis capability. The latent domain acts as an intermediary that captures essential signal characteristics without requiring the generative model to directly learn complex signal representations.
Solution Approach 2:
The coding scheme segments the bitstream allocation by dedicating specific bits to convey quantized latent frames separately from other signal parameters. This segmentation allows the system to optimize the representation of different signal aspects independently, improving overall modeling accuracy by focusing computational resources on capturing the most significant signal variations in the latent domain.
2Reliability
If additional conditioning information is provided periodically, then signal variability representation is improved, but bitrate consumption increases
Solution Approach 1:
Instead of providing complete conditioning information for every signal frame, the patent applies partial action by periodically updating the conditioning information only when significant signal variations occur. This approach maintains reliable signal variability representation by providing conditioning information at critical moments while avoiding unnecessary bitrate consumption during stable signal periods.
Solution Approach 2:
The system implements periodic updating of conditioning information in the bitstream, synchronized with the latent frame rate. This periodic action ensures that the decoder receives updated conditioning information at regular intervals to accurately represent signal variability, while the periodic nature of the updates optimizes bitrate usage by avoiding continuous transmission of redundant information.
3Ease of manufacture
If the generative model approximates the true unknown model, then model training feasibility is improved, but modeling mismatch increases
Solution Approach 1:
The patent transforms the problem by changing the parameter space in which the generative model operates. Instead of directly modeling the complex signal distribution, the model learns to represent signals in a latent parameter space that is more amenable to approximation. This parameter transformation enables feasible model training while reducing modeling mismatch by capturing the essential structure of the signal in a simplified representation domain.
Data Source
AI summary
The present disclosure provides a decoder configured to receive a finite bitrate stream that includes a quantized latent frame, where the quantized latent frame includes a quantized representation of a current frame of a signal in a latent domain different from a first domain; to generate a reconstructed latent frame from the quantized latent frame; to use a generative neural network model to perform a task for which the general neural network model has been trained, wherein the task includes to generate parameters for an invertible mapping from the latent domain to the first domain; to reconstruct a current frame of the signal in the first domain, which includes to map the reconstructed latent frame to the first domain by use of the invertible mapping, and to use the reconstructed current frame of the signal in the first domain to update a state of the generative neural network model.