Adaptive Frequency Tile Decoding for Low-Bitrate Audio Artifacts
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Solution Overview
Problem
Current audio codecs face challenges in maintaining high-frequency detail and timbre when implementing bandwidth extension, leading to perceptual losses and artifacts such as dissonance and warbling, particularly at low bitrates, due to the need for transformation into new domains and fixed frequency alignment methods.
Innovation Solution
A signal-adaptive approach that detects tonal components near transition regions and adjusts frequency borders or removes them to minimize artifacts, using cross-over filtering and spectral interpolation to reduce beating and filter ringing, while maintaining the tonal grid and preserving audio quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If bandwidth extension is implemented using fixed frequency alignment methods, then audio compression efficiency is improved, but artifacts such as beating and warbling occur due to misalignment with the tonal grid
Solution Approach 1:
The patent applies dynamics by making the frequency alignment adaptive rather than fixed. The decoder determines whether to align regenerated spectral portions with the tonal grid based on signal characteristics, allowing the system to dynamically adjust its behavior to match the actual signal content and avoid artifacts while maintaining compression efficiency
Solution Approach 2:
The patent changes the parameter of frequency alignment from a fixed value to a variable that depends on signal analysis. By analyzing whether the signal contains tonal components and adjusting the alignment accordingly, the system optimizes both compression efficiency and audio quality by matching the regeneration strategy to the actual signal characteristics
2Device complexity
If spectral portions are regenerated using fixed frequency borders, then decoding complexity is reduced, but tonal components are split causing perceptual artifacts
Solution Approach 1:
The patent makes the frequency border selection dynamic by analyzing signal characteristics at transition regions. The decoder adjusts frequency borders adaptively based on detected tonal components, ensuring that tonal structures are preserved without excessive complexity by only applying complex processing when actually needed
Solution Approach 2:
The system performs self-analysis to determine its own processing requirements. By analyzing the decoded core signal to detect tonal components and automatically adjusting frequency borders accordingly, the system serves itself to avoid artifacts without requiring external control or excessive complexity
3Object-affected harmful factors
If cross-over filtering is applied at transition regions, then artifacts are reduced, but processing complexity increases
Solution Approach 1:
The patent applies cross-over filtering locally only at transition regions where spectral portions are regenerated, rather than throughout the entire signal. This targeted approach reduces artifacts at critical boundaries while minimizing additional processing complexity by avoiding unnecessary filtering in other regions
Solution Approach 2:
The patent applies filtering selectively and partially - only where needed at transition regions with regenerated spectral portions. This partial action approach provides sufficient artifact reduction without the excessive processing complexity that would result from applying filtering universally across the entire signal
Data Source
AI summary
Apparatus for decoding an encoded audio signal including an encoded core signal and parametric data, including: a core decoder for decoding the encoded core signal to obtain a decoded core signal; an analyzer for analyzing the decoded core signal before or after performing a frequency regeneration operation to provide an analysis result; and a frequency regenerator for regenerating spectral portions not included in the decoded core signal using a spectral portion of the decoded core signal, the parametric data, and the analysis result.