Inverse Filter Iteration for Signal Distortion Elimination
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Solution Overview
Problem
Conventional signal distortion elimination methods are inadequate in accurately removing transfer characteristics from observed signals due to insufficient accuracy in estimating inverse filters, resulting in residual distortion in the restored signal.
Innovation Solution
A signal distortion elimination apparatus and method that iteratively applies an inverse filter to an observed signal, calculates prediction error filters, and adjusts the filters to make innovation estimates mutually independent, using techniques such as minimizing mutual information or variances to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional prediction error filter calculation is applied to observed signal frames, then processing can be performed, but the accuracy of inverse filter estimation is insufficient due to transfer characteristics contamination
Solution Approach 1:
The patent applies a preliminary inverse filter to the observed signal to generate an ad-hoc signal before calculating prediction error filters. This preliminary action removes the dominant transfer characteristics contamination, enabling subsequent prediction error filter calculation to be based on a cleaner signal representation, thereby improving inverse filter estimation accuracy
Solution Approach 2:
The ad-hoc signal serves as an intermediary between the observed signal and the prediction error filter calculation. By using this intermediate representation that has been pre-processed with an inverse filter, the system avoids directly calculating prediction error filters from the transfer-characteristics-contaminated observed signal, thus resolving the accuracy problem
2Productivity
If prediction error filters are calculated from observed signal frames, then frame-wise processing is enabled, but the filters remain influenced by transfer characteristics causing residual distortion
Solution Approach 1:
Before performing frame-wise prediction error filter calculation, the patent applies an inverse filter to the entire observed signal to generate an ad-hoc signal. This preliminary action ensures that when the signal is subsequently divided into frames for processing, each frame contains reduced transfer characteristics contamination, allowing accurate distortion removal while maintaining frame-wise processing efficiency
Solution Approach 2:
The patent segments the ad-hoc signal (not the original observed signal) into frames for prediction error filter calculation. This segmentation approach maintains the computational efficiency of frame-wise processing while ensuring that the segmented frames are derived from a pre-processed signal with reduced transfer characteristics, thereby achieving both productivity and precision
Data Source
AI summary
Provided is a signal distortion elimination apparatus comprising: an inverse filter application means that outputs the signal obtained by applying an inverse filter to an observed signal as a restored signal when a predetermined iteration termination condition is met and outputs the signal obtained by applying the inverse filter to the observed signal as an ad-hoc signal when the predetermined iteration termination condition is not met; a prediction error filter calculation means that segments the ad-hoc signal into frames and outputs a prediction error filter of each frame obtained by performing linear prediction analysis of the ad-hoc signal of each frame; an inverse filter calculation means that calculates an inverse filter such that a concatenation of innovation estimates of the respective frames becomes mutually independent among their samples, where the innovation estimate of a single frame (an innovation estimate) is the signal obtained by applying the prediction error filter of the corresponding frame to the ad-hoc signal of the corresponding frame, and outputs the inverse filter; and a control means that iteratively executes the inverse filter application means, the prediction error filter calculation means and the inverse filter calculation means until the iteration termination condition is met.