Adaptive Audio Signal Compression Using Autocorrelation Correction
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Solution Overview
Problem
Existing audio compression methods face inefficiencies due to the uniform processing of all signals, leading to ill-conditioned cases and low compression efficiency and speech quality, especially for special input signals.
Innovation Solution
A signal compression method that adjusts autocorrelation coefficient correction factors based on the characteristics of each input signal, using techniques such as energy parameter calculation and lag-window adjustment, to improve the accuracy of linear prediction coefficients and avoid ill-conditioned cases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If uniform processing is applied to all signals, then the processing method is simple, but ill-conditioned cases occur for special input signals leading to low compression efficiency and poor speech quality
Solution Approach 1:
The patent applies parameter changes by adjusting the autocorrelation coefficient correction factors based on signal characteristics. Specifically, the white-noise correction factor and lag-window are modified according to the energy parameter and other signal features, transforming the fixed processing parameters into adaptive ones that change according to input signal conditions, thereby resolving the contradiction between processing simplicity and compression efficiency.
Solution Approach 2:
The patent implements dynamics by making the processing method adaptive rather than static. The autocorrelation coefficient correction factors are dynamically adjusted based on signal characteristics such as energy parameter, allowing the system to adapt to different signal conditions (voiced/unvoiced, high/low energy) and avoid ill-conditioned cases while maintaining operational simplicity.
2Reliability
If adaptive processing based on signal characteristics is applied, then compression efficiency and speech quality improve, but the processing complexity increases
Solution Approach 1:
The patent manages processing complexity through parameter changes by focusing adaptations on specific critical parameters (autocorrelation coefficient correction factors) rather than the entire processing pipeline. The energy parameter calculation and selective adjustment of correction factors provide adaptive performance improvements while limiting complexity increase to essential components only.
3Productivity
If the same autocorrelation coefficient correction factors are used for all signals, then the calculation is efficient, but the accuracy of linear prediction coefficients decreases for special signals
Solution Approach 1:
The patent resolves this contradiction by changing the autocorrelation coefficient correction factors from fixed to adaptive parameters. The white-noise correction factor and lag-window are adjusted based on signal characteristics (energy parameter, voiced/unvoiced detection), ensuring high accuracy for special signals while maintaining calculation efficiency through systematic adjustment rules rather than complex computations.
Data Source
AI summary
A signal compression method and apparatus are provided. The signal compression method includes: multiplying an input signal by a window function; calculating original autocorrelation coefficients of a windowed input signal; 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; calculating linear prediction coefficients according to the modified autocorrelation coefficients; and outputting a coded bit stream according to the linear prediction coefficients.