Audio Signal Compression Using Adaptive Autocorrelation Correction
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Solution Overview
Problem
Existing audio compression methods face inefficiencies due to ill-conditioned cases caused by special input signals, leading to low compression efficiency and poor quality of reconstructed speech signals, as all signals are processed uniformly without considering their characteristics.
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 produce more suitable linear prediction coefficients for accurate compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If all signals are processed in the same way using fixed autocorrelation coefficient correction factors, then the processing is simple and uniform, but ill-conditioned cases occur for special input signals leading to unstable solving and low compression efficiency
Solution Approach 1:
The patent applies dynamics by making the autocorrelation coefficient correction factors adaptive rather than fixed. The white-noise correction factor and lag-window are dynamically adjusted based on the energy parameter and zero-crossing rate of each input signal frame, allowing the processing to adapt to different signal characteristics and avoid ill-conditioned cases while maintaining operational simplicity through automated adaptation.
Solution Approach 2:
The patent changes parameters by modifying the autocorrelation coefficient correction factors based on signal characteristics. Specifically, the white-noise correction factor win_lag(0) and lag-window win_lag(k) are adjusted according to the energy parameter E and zero-crossing rate nz, transforming fixed parameters into variable ones that optimize stability and compression efficiency for different input signals.
2Device complexity
If all signals are processed in the same way, then the processing method is simple, but the compression efficiency of lossless coder and quality of reconstructed speech signals are low
Solution Approach 1:
The patent improves compression efficiency by changing parameters adaptively. The autocorrelation coefficient correction factors are modified based on signal-specific parameters (energy and zero-crossing rate), allowing the system to optimize compression for each signal type without requiring complex manual configuration, thus improving productivity while keeping the processing method relatively simple.
Solution Approach 2:
The system applies self-service by automatically calculating the energy parameter and zero-crossing rate for each signal frame and using these to self-adjust the autocorrelation coefficient correction factors. This eliminates the need for external manual tuning and allows the processing method to automatically adapt to different signal characteristics, improving compression efficiency without proportionally increasing complexity.
3Ease of operation
If uniform processing is applied to all signals, then the processing is straightforward, but the quality of reconstructed speech signals deteriorates due to ill-conditioned cases
Solution Approach 1:
The patent resolves this contradiction by changing parameters adaptively based on signal characteristics. The white-noise correction factor and lag-window are adjusted according to the energy parameter and zero-crossing rate, which improves the quality of reconstructed speech signals by preventing ill-conditioned cases while maintaining straightforward automated processing without manual intervention.
Solution Approach 2:
The system implements feedback by calculating signal characteristics (energy and zero-crossing rate) and using these to adjust the autocorrelation coefficient correction factors. This feedback mechanism ensures that the processing adapts to each signal's properties, improving reconstruction quality while keeping the operation straightforward through automated closed-loop control.
Data Source
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AI summary
The present invention relates to audio compression. 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. The technical solution under the present invention avoids ill-conditioned cases of special input signals, makes the modified autocorrelation coefficients more suitable for subsequent compression, improves the compression efficiency of a lossless coder and the quality of reconstructed speech signals of a lossy coder, and involves only simple operations.