Adaptive LPC Window Selection for Better Voice Prediction
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Solution Overview
Problem
Current Linear Prediction Coding (LPC) analysis methods suffer from suboptimal performance due to the use of fixed window functions and increased complexity from applying both short and long windows in successive analysis rounds, which affects the compression ratio and quality of reconstructed voice signals.
Innovation Solution
An adaptive windowing method is introduced, where signal feature information is analyzed to select a suitable window function for LPC analysis, improving prediction performance with minimal increase in coding complexity by dynamically adjusting the window function based on amplitude, energy, or other signal characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a fixed window function is applied in the windowing process, then the device complexity is reduced, but the linear prediction performance deteriorates
Solution Approach 1:
The patent implements dynamic window function selection by analyzing signal features (such as signal type, energy distribution, or spectral characteristics) and adaptively choosing the most appropriate window function for each signal segment. This transforms the static fixed window approach into a dynamic adaptive system, resolving the contradiction between simplicity and performance by introducing conditional logic that selects optimal window functions based on real-time signal characteristics
Solution Approach 2:
The patent changes the parameter of window function selection from a fixed constant to a variable determined by signal analysis. By introducing signal feature analysis and conditional selection logic, the system adjusts the window function parameter dynamically based on input signal characteristics, thereby improving linear prediction performance without requiring a complete redesign of the coding system
2Manufacturing precision
If two rounds of LPC analysis are applied with short and long windows, then the linear prediction performance is improved, but the device complexity increases
Solution Approach 1:
The patent implements dynamic window function selection by analyzing signal features (such as signal type, energy distribution, or spectral characteristics) and adaptively choosing the most appropriate window function for each signal segment. This transforms the static fixed window approach into a dynamic adaptive system, resolving the contradiction between simplicity and performance by introducing conditional logic that selects optimal window functions based on real-time signal characteristics
Solution Approach 2:
The patent changes the parameter of window function selection from a fixed constant to a variable determined by signal analysis. By introducing signal feature analysis and conditional selection logic, the system adjusts the window function parameter dynamically based on input signal characteristics, thereby improving linear prediction performance without requiring a complete redesign of the coding system
Data Source
AI summary
The present invention relates to communication technologies and discloses a method, an apparatus and a system for Linear Prediction Coding (LPC) analysis to improve LPC prediction performance and simplify analysis operation. The method includes: obtaining signal feature information of at least one sample point of input signals; comparing and analyzing the signal feature information to obtain an analysis result; selecting a window function according to the analysis result to perform adaptive windowing for the input signals and obtain windowed signals; and processing the windowed signals to obtain an LPC coefficient for linear prediction. The embodiments of the present invention are applicable to LPC.