Sample Generation via Joint Probability Distribution
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Solution Overview
Problem
Existing methods for generating sample data, such as reconstructed audio samples, often fail to account for local and across-sample dependencies, resulting in lower quality samples with increased jitteriness.
Innovation Solution
A system comprising a neural network and a sample generator that processes neural network inputs, including previous sample data, to generate a joint probability distribution. This distribution is then used to generate sample data that accounts for estimated dependencies between samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional sample generation methods are used, then the generation process is simple, but the sample quality is low with increased jitteriness
Solution Approach 1:
A neural network is introduced as an intermediary component between the input data and the sample generation process. The neural network processes previous sample data and generates a joint probability distribution, which then guides the sample generator to produce higher quality samples that account for inter-sample dependencies, thereby improving sample quality without directly modifying the core generation algorithm
Solution Approach 2:
The system implements feedback by feeding previous sample data back into the neural network as input. This allows the neural network to learn from historical samples and generate probability distributions that reflect temporal or sequential dependencies, enabling the sample generator to produce smoother, less jittery samples by considering the context of previous samples
2Manufacturing precision
If dependencies between samples are not accounted for, then the generation process is computationally efficient, but the reconstructed audio quality deteriorates
Solution Approach 1:
Instead of modeling all possible dependencies between samples (which would be computationally prohibitive), the system applies partial action by considering only the dependencies captured in the previous sample data fed to the neural network. This selective approach to modeling dependencies achieves improved audio quality without the excessive computational cost of comprehensive dependency modeling
Data Source
AI summary
A device includes a neural network and a sample generator. The neural network is configured to process one or more neural network inputs to generate a joint probability distribution. The one or more neural network inputs include at least first previous sample data and second previous sample data associated with at least one previous data sample of a sequence of data samples. The sample generator is configured to generate first sample data and second sample data based on the joint probability distribution. The first sample data and the second sample data are associated with at least one data sample of the sequence of data samples.