Frequency-Domain Long-Term Predictor for Audio Codecs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing audio codecs struggle to efficiently remove long-term redundancy in audio signals, leading to inefficiencies in coding gain and perceptual distortion, particularly in transform-based codecs that fail to account for spectral flatness and vector quantization errors.
Innovation Solution
The system employs a frequency-domain analysis to determine optimal long-term predictor parameters, considering spectral flatness and vector quantization errors, using techniques such as peak picking and fractional frequency estimation to minimize quantization noise and distortion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If transform-based codecs are used to encode audio signals, then coding efficiency is improved through frequency domain transformation, but long-term redundancy removal capability deteriorates due to lack of temporal prediction mechanisms
Solution Approach 1:
The patent combines transform-based coding with long-term prediction mechanisms by integrating an adaptive filter into the transform codec framework. The long-term predictor operates in parallel with the frequency transformation, merging temporal redundancy removal with spectral analysis to achieve both coding efficiency and redundancy exploitation.
Solution Approach 2:
The audio signal processing is segmented into distinct functional blocks: long-term prediction stage, frequency transformation stage, and quantization stage. This segmentation allows each component to optimize its specific function while working together within the overall codec architecture, with the long-term predictor handling temporal redundancy separately from the transform stage handling spectral representation.
2Loss of information
If conventional long-term predictors are used in audio codecs, then temporal redundancy is removed, but spectral flatness and quantization errors are not accounted for leading to suboptimal prediction parameters
Solution Approach 1:
The patent implements feedback mechanisms where the quantization error and spectral flatness information are fed back into the long-term predictor parameter optimization process. The predictor adapts its parameters based on the actual quantization performance and spectral characteristics, creating a closed-loop system that continuously refines prediction accuracy.
Solution Approach 2:
The long-term predictor parameters (such as lag and gain) are dynamically adjusted based on spectral flatness measurements and quantization error analysis. The system changes prediction parameters adaptively according to the local spectral characteristics of the audio signal, optimizing prediction performance for different signal types and conditions.
3Measurement precision
If full analysis-by-synthesis is performed to optimize predictor parameters, then prediction accuracy is improved, but computational complexity and encoding time increase significantly
Solution Approach 1:
Instead of performing complete analysis-by-synthesis for all predictor parameters, the patent applies partial optimization by focusing computational effort on the most critical parameters (such as lag selection) while using simplified models for less critical ones. This selective approach achieves sufficient prediction accuracy without the full computational burden of exhaustive analysis-by-synthesis.
Solution Approach 2:
The patent performs preliminary spectral flatness analysis and initial parameter estimation before the main encoding process. By pre-computing spectral characteristics and initializing predictor parameters based on these preliminary analyses, the system reduces the computational load required during the actual encoding phase while maintaining optimization accuracy.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A frequency domain long-term prediction system and method for estimating and applying an optimum long term predictor. Embodiments of the system and method include determining parameters of a single-tap predictor using a frequency- domain analysis having an optimality criteria based on spectral flatness measure. Embodiments of the system and method also include determining parameters of the long-term predictor by accounting for the performance of the vector quantizer in quantizing the various subbands. In some embodiments other encoder metrics (such as signal tonality) are used as well. Other embodiments of the system and method include determining the optimal parameters of the long-term predictor by accounting for some of the decoder operation. Other embodiments of the system and method include extending a 1-tap predictor to a k-th order predictor by convolving the 1-tap predictor with a pre-set filter and selecting from a table of such pre-set filters based on a minimum energy criteria.