Audio Uncertainty Quantification for Reliable Neural Signal Processing
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Solution Overview
Problem
Conventional neural networks in audio devices lack reliable uncertainty quantification, leading to over- or under-confidence, making it difficult to assess the reliability of their outputs and resulting in poor calibration and inefficient signal processing.
Innovation Solution
An audio device with uncertainty quantification capabilities, comprising processors and encoders/decoders, determines uncertainty parameters by comparing audio data and decoder output signals, allowing for informed control of signal processing based on these parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional neural networks are used for audio signal processing, then processing speed and basic functionality are improved, but reliability of predictions and uncertainty estimation deteriorate
Solution Approach 1:
The patent introduces an uncertainty quantification module as an intermediary between the neural network and the output. This module computes uncertainty estimates (e.g., using Monte Carlo dropout or deep ensembles) without significantly impacting processing speed, while providing reliability information that helps determine whether to trust the network's predictions for audio processing decisions.
Solution Approach 2:
The system implements feedback by using the uncertainty estimates to adjust processing behavior. When uncertainty is high, the system can fall back to alternative processing methods or request re-processing, creating a closed-loop system that continuously improves reliability based on predicted confidence levels while maintaining efficient processing for high-confidence cases.
2Productivity
If neural networks process audio data without uncertainty quantification, then processing efficiency is improved, but ability to distinguish in-domain and out-of-domain samples deteriorates
Solution Approach 1:
The patent replaces traditional mechanical/domain-based classification methods with uncertainty-based adaptive processing. Instead of relying on pre-defined domain rules, the system uses neural network uncertainty estimates to dynamically determine whether input audio samples are in-domain or out-of-domain, allowing the system to adapt to new domains without retraining while maintaining processing efficiency for familiar domains.
3Loss of time
If neural networks provide overconfident predictions, then decision-making speed is improved, but processing accuracy deteriorates
Solution Approach 1:
The system applies partial uncertainty quantification only when needed. For audio processing tasks where the neural network is highly confident and uncertainty estimates are low, the system proceeds directly with processing without additional verification, maintaining fast decision-making. When uncertainty exceeds thresholds, the system triggers additional validation or alternative processing paths, ensuring accuracy only when necessary rather than applying exhaustive checks to all inputs.
Data Source
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AI summary
An audio device comprising memory, an interface, and one or more processors, wherein the one or more processors are configured to obtain audio data; process the audio data for provision of an audio output; determine an uncertainty parameter based on the audio data; and control the processing of the audio data for provision of the audio output based on the uncertainty parameter.