Quality Estimation Models for Signal Characteristics
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Solution Overview
Problem
Existing quality estimation models struggle to accurately evaluate specific signal characteristics, such as speech quality and background noise, due to reliance on overall quality labels, which can lead to inadequate training of data enhancement models and introduction of artifacts, and they lack robustness across diverse impairments and recording conditions.
Innovation Solution
Training separate quality estimation models to estimate specific signal characteristics using diverse impairments and artifacts introduced by different data enhancement models, allowing for the generation of synthetic quality labels that can optimize data enhancement models for specific applications and improve robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate quality estimation models are trained for different signal characteristics, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent divides the quality estimation task into multiple specialized models, each trained to estimate a specific signal characteristic (e.g., speech quality, background noise, artifacts). This segmentation allows each model to focus on particular features, improving measurement precision for each characteristic while managing overall system complexity through modular architecture.
Solution Approach 2:
The patent creates a multi-functional quality estimation system where multiple specialized models work together to provide comprehensive signal quality assessment. Each model contributes to different aspects of quality evaluation, and their combined outputs provide a complete picture of signal characteristics across various dimensions.
2Adaptability or versatility
If diverse impairments and artifacts are used in training, then adaptability is improved, but loss of information increases
Solution Approach 1:
The patent applies parameter changes by systematically varying impairment types, severity levels, and artifact characteristics during training data generation. This includes adjusting noise levels, distortion types, and degradation parameters to create diverse training scenarios, enabling models to adapt to various real-world conditions while maintaining signal quality through controlled parameter ranges.
Solution Approach 2:
The patent converts harmful impairments and artifacts into beneficial training elements. By intentionally introducing diverse degradations during training, the system learns to recognize and compensate for these issues, transforming potential quality losses into opportunities for improved model robustness and generalization to real-world degraded signals.
Data Source
AI summary
This document relates to training and employing of quality estimation models to estimate the quality of different signal characteristics. One example includes a method or technique that can be performed on a computing device. The method or technique can include obtaining training signals exhibiting diverse impairments introduced when the training signals are captured or diverse artifacts introduced by different processing characteristics of a plurality of data enhancement models. The method or technique can also include obtaining quality labels for different signal characteristics of the training signals. The method or technique can also include training at least two different quality estimation models to estimate quality of at least two different signal characteristics based at least on the training signals and the quality labels.