Voice Quality Determination Using Terminal Model Segmentation
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Solution Overview
Problem
Existing methods for assessing voice transmission quality in telecommunication networks do not adequately consider the influence of modern telecommunication terminals, leading to inaccurate predictions and monitoring of voice quality.
Innovation Solution
A method that identifies model types for telecommunication terminals, selects call aspects, and determines end-to-end quality parameters using engineering and operating parameters, including key performance indicators and key quality indicators, to assess voice transmission quality from the user's perspective, considering factors like echo, double talk, and listening effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the E-model is used for network planning to assess voice transmission quality, then a standardized transmission factor (R-factor) can be computed, but the influence of real modern terminals is not considered or considered only in a very basic manner
Solution Approach 1:
The method segments the voice transmission system into distinct components: network portion and terminal portions. Each component is modeled separately with dedicated model types (network model types and terminal model types), allowing independent characterization and combination of their respective quality influences.
Solution Approach 2:
The method introduces dynamic model type identification that adapts to the specific terminal devices involved in a call. By identifying actual model types of participating terminals and selecting appropriate quality parameter sets, the system dynamically adjusts to accommodate various modern terminal configurations rather than using static basic models.
2Measurement precision
If call tests with test persons are performed to assess audio quality, then subjective quality assessment is obtained, but the method is very complicated
Solution Approach 1:
Instead of performing actual subjective call tests with human operators, the method uses pre-determined quality parameter sets that represent typical human quality assessments. These parameter sets are derived from subjective testing but can be applied automatically without requiring actual human participants, thus copying the essence of subjective assessment while eliminating its complexity.
Solution Approach 2:
Quality parameter sets are determined in advance through laboratory testing and subjective assessment procedures. These pre-determined parameter sets are then stored and can be automatically applied during actual network operation, eliminating the need to perform complex subjective tests in real-time while maintaining assessment accuracy.
3Measurement precision
If multiple quality parameters are determined for different call aspects, then comprehensive voice quality assessment is achieved, but the computational complexity increases
Solution Approach 1:
The method applies different quality parameter sets to different call aspects (e.g., one-way quality, two-way quality, echo, background noise). Each call aspect is assessed with locally optimized parameters appropriate to that specific aspect, allowing comprehensive evaluation without requiring a single overly complex unified model.
Solution Approach 2:
The framework uses a universal structure where model type identification and parameter set selection can be applied across multiple call aspects and different terminal combinations. The same basic methodology serves multiple assessment functions, reducing overall computational complexity through reuse of established models and parameter sets.
Data Source
AI summary
A method for determining a quality of voice transmitted by electrical signals between a first telecommunication terminal and a second telecommunication terminal in a telecommunication network, the method comprising: identifying a model type for the first telecommunication terminal and the second telecommunication terminal respectively; identifying at least one operating parameter for the telecommunication network; selecting at least one call aspect (i) of the transmitted voice wherein a quality of the transmitted voice shall be determined for the at least one call aspect; identifying a value of at least one quality parameter (KQIA/B, i, n) for the at least one call aspect (i) for at least one of the first telecommunication terminal and the second telecommunication terminal as a function of a value of at least one selected engineering parameter (KPIA/B) of the at least one telecommunication terminal as well as a function of the at least one operating parameter.