Dynamic Tempered Sampling for Generative Model Audio Inference
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing packet loss concealment methods using regressive generative models face challenges with static tempered sampling, which can lead to model collapse into silence or 'babbling' during packet loss events in VoIP communications.
Innovation Solution
A dynamic tempered sampling method is introduced, where the sampling temperature is dynamically adjusted based on the number of time steps since the start of a packet loss concealment event, using a function that increases the temperature sampling value over time to prevent model collapse and babbling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static tempered sampling is used in regressive generative models, then the sampling process is simple and consistent, but the model collapses into silence or 'babbling' during packet loss events
Solution Approach 1:
The patent applies dynamics by transitioning from static tempered sampling to dynamic tempered sampling, where the temperature parameter is adjusted in real-time based on the duration of packet loss events. The temperature is increased over time during packet loss to prevent model collapse into silence or babbling, making the sampling process adaptive rather than fixed.
Solution Approach 2:
The patent changes the temperature parameter dynamically during packet loss concealment. Specifically, the temperature sampling value is increased as a function of the number of time steps since the packet loss event began, allowing the model to maintain stability and generate meaningful audio output throughout the packet loss duration rather than collapsing.
2Reliability
If the temperature sampling value is increased to prevent model collapse, then the model generates more varied output, but the risk of 'babbling' increases
Solution Approach 1:
The patent implements periodic action by adjusting the temperature sampling value in discrete time steps during packet loss events. The temperature increases periodically based on the number of time steps elapsed since the packet loss began, allowing controlled exploration that prevents collapse while gradually managing the risk of babbling through structured temporal progression.
3Reliability
If dynamic temperature adjustment is implemented, then model stability during packet loss is improved, but computational complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-defining the temperature adjustment schedule as a function of time steps during packet loss events. Rather than requiring complex real-time optimization, the temperature adjustment strategy is prepared in advance based on expected packet loss durations, reducing computational complexity while maintaining reliability.
Data Source
AI summary
A method of sampling output audio samples includes, during a packet loss concealment event, obtaining a sequence of previous output audio samples. At each time step during the event, the method includes generating a probability distribution over possible output audio samples for the time step. Each sample includes a respective probability indicating a likelihood that the corresponding sample represents a portion of an utterance at the time step. The method also includes determining a temperature sampling value based on a function of a number of time steps that precedes the time step, and an initial, a minimum, and a maximum temperature sampling value. The method also includes applying the temperature sampling value to the probability distribution to adjust a probability of selecting possible samples and randomly selecting one of the possible samples based on the adjusted probability. The method also includes generating synthesized speech using the randomly selected sample.