Adaptive Autocorrelation Correction in Speech Signal Compression
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Solution Overview
Problem
Existing signal compression methods face issues with ill-conditioned cases and low compression efficiency due to uniform processing of all signals, leading to instability in autocorrelation matrix solutions and poor quality of reconstructed speech signals.
Innovation Solution
A signal compression method that adjusts autocorrelation coefficient correction factors based on the characteristics of each input signal, using energy parameters and reflection coefficients to calculate modified autocorrelation coefficients, thereby improving the accuracy and robustness of linear prediction coefficients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If uniform processing is applied to all signals using fixed autocorrelation coefficient correction factors, then the processing simplicity is maintained, but ill-conditioned cases occur for special input signals leading to instability in autocorrelation matrix solutions
Solution Approach 1:
The patent transforms the static, uniform processing approach into a dynamic one by adaptively adjusting autocorrelation coefficient correction factors based on signal characteristics. The system dynamically selects different processing parameters (such as bandwidth expansion factors) according to the specific properties of each input signal, thereby maintaining stability for ill-conditioned signals while preserving simplicity through automated adaptation.
Solution Approach 2:
The patent changes the processing parameters (autocorrelation coefficient correction factors, bandwidth expansion factors) based on signal characteristics. By monitoring signal properties and adjusting parameters accordingly, the system avoids ill-conditioned cases without requiring complex manual intervention, thus resolving the contradiction between operational simplicity and solution stability.
2Device complexity
If fixed bandwidth expansion is applied to all signals, then the implementation complexity is reduced, but compression efficiency decreases for certain signal types
Solution Approach 1:
The patent implements dynamic bandwidth expansion where the expansion factor is adjusted based on signal characteristics rather than applying a fixed expansion to all signals. This dynamic approach optimizes compression efficiency for different signal types while maintaining reasonable implementation complexity through automated parameter selection.
Solution Approach 2:
The patent changes bandwidth expansion parameters according to signal properties, allowing the system to optimize compression efficiency for different signal types. By adapting parameters like bandwidth expansion factor based on measured signal characteristics, the system achieves better compression performance without significantly increasing implementation complexity.
3Productivity
If signal-specific parameter adjustment is implemented, then compression efficiency and speech quality are improved, but calculation complexity increases
Solution Approach 1:
The patent performs preliminary analysis of signal characteristics before applying compression processing. By pre-calculating signal properties and determining appropriate parameters in advance, the system avoids more complex real-time adjustments during compression, thus improving efficiency while controlling overall calculation complexity through staged processing.
Solution Approach 2:
The patent implements self-service parameter adjustment where the system automatically analyzes its own input signals and selects optimal processing parameters without external intervention. This self-adaptive mechanism improves compression efficiency and speech quality while keeping the system relatively simple by eliminating the need for complex external control mechanisms.
Data Source
AI summary
A signal compression method includes: multiplying an input signal by a window function, calculating original autocorrelation coefficients of a windowed input signal. The method also includes calculating a white-noise correction factor or a lag-window according to the original autocorrelation coefficients, and calculating modified autocorrelation coefficients according to the original autocorrelation coefficients, the white-noise correction factor and the lag-window. The method further includes calculating linear prediction coefficients according to the modified autocorrelation coefficients, and outputting a coded bit stream according to the linear prediction coefficients.