Linear Prediction Coefficient Tuning for Spectral Envelope Accuracy
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Solution Overview
Problem
Conventional linear prediction analysis methods for encoding speech and acoustic signals suffer from reduced accuracy due to the use of fixed coefficients, which can lead to inaccuracies in approximating the spectral envelope when spectral peaks are not modified appropriately.
Innovation Solution
A linear prediction analysis method that adjusts coefficients based on a monotonically increasing or decreasing relationship with the fundamental frequency or period, using dynamically determined coefficients to modify autocorrelation, thereby improving the accuracy of spectral envelope representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If fixed coefficients are used for modifying autocorrelation in linear prediction analysis, then the device complexity is reduced and ease of operation is improved, but the manufacturing precision and measurement precision of spectral envelope approximation deteriorate
Solution Approach 1:
The patent applies dynamics by making the autocorrelation modification coefficient variable rather than fixed. The coefficient is determined based on the fundamental frequency of the input signal, allowing it to adapt dynamically to different speech characteristics. This resolves the contradiction by enabling accurate spectral envelope approximation across varying fundamental frequencies while maintaining reasonable operational simplicity through automated coefficient selection.
Solution Approach 2:
The patent changes the parameter of the autocorrelation modification coefficient from a fixed value to a variable that depends on fundamental frequency. By establishing a relationship between the coefficient and fundamental frequency parameters, the system achieves accurate spectral envelope approximation for different speech conditions without requiring complex manual adjustment, thus resolving the precision-ease of operation contradiction.
2Device complexity
If fixed coefficients are used for modifying autocorrelation in linear prediction analysis, then the device complexity is reduced, but the manufacturing precision and reliability of spectral envelope approximation deteriorate
Solution Approach 1:
The patent makes the autocorrelation modification coefficient dynamic by linking it to the fundamental frequency of the input signal. This allows the system to adapt to different speech characteristics automatically, achieving high spectral envelope approximation accuracy without increasing device complexity, as the coefficient selection is performed through automated algorithms rather than complex hardware adjustments.
Solution Approach 2:
The patent transforms the fixed coefficient parameter into a variable parameter that changes with fundamental frequency. This parameter change enables the system to maintain high manufacturing precision for spectral envelope approximation across different operating conditions while keeping the device structure relatively simple, as the parameter adaptation is achieved through computational methods.
3Ease of operation
If fixed coefficients are used for modifying autocorrelation in linear prediction analysis, then the ease of operation is improved, but the reliability of spectral envelope approximation deteriorate
Solution Approach 1:
The patent implements dynamics by making the autocorrelation modification coefficient adaptive to fundamental frequency variations. This ensures reliable spectral envelope approximation across different speech conditions while maintaining ease of operation, as the system automatically selects appropriate coefficients without requiring user intervention or complex configuration.
Solution Approach 2:
The patent changes the coefficient parameter from fixed to variable based on fundamental frequency. This parameter adaptation ensures reliable spectral envelope approximation for different speech inputs while keeping the system easy to operate, as the coefficient selection is performed automatically through established relationships between frequency and coefficient values.
Data Source
AI summary
An autocorrelation calculation unit 21 calculates an autocorrelation RO(i) from an input signal. A prediction coefficient calculation unit 23 performs linear prediction analysis by using a modified autocorrelation R′O(i) obtained by multiplying a coefficient wO(i) by the autocorrelation RO(i). It is assumed here, for each order i of some orders i at least, that the coefficient wO(i) corresponding to the order i is in a monotonically increasing relationship with an increase in a value that is negatively correlated with a fundamental frequency of the input signal of the current frame or a past frame.