Spectral Audio Decoding With IGF for Low-Bitrate Bandwidth Extension
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Solution Overview
Problem
Current audio codecs face limitations in bandwidth extension techniques, particularly in maintaining high-frequency detail and timbre at low bitrates, due to restricted spectral patching and transformation requirements, which lead to pre- or post-echoes and increased computational complexity.
Innovation Solution
The implementation of Intelligent Gap Filling (IGF) technology, which performs bandwidth extension in the same spectral domain as the core decoder, using parametric data and frequency regeneration to fill spectral gaps, reduces echo artifacts and simplifies processing by eliminating the need for downsampling and upsampling, while maintaining high coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spectral patching and domain transformation are used for bandwidth extension, then high-frequency content can be reconstructed, but pre- and post-echoes occur and computational complexity increases
Solution Approach 1:
The patent extracts the harmful echo artifacts by identifying them as separate components that can be independently processed. The echo cancellation module specifically targets and removes pre- and post-echoes that are generated during the bandwidth extension process, separating the useful high-frequency reconstruction from the harmful artifacts.
Solution Approach 2:
The patent converts the harmful echo artifacts into a detectable and manageable form. By modeling the echo characteristics and using them as input for the echo cancellation module, the system transforms the problematic byproduct into useful information that guides the artifact removal process, ultimately improving the overall audio quality.
2Measurement precision
If spectral patching and domain transformation are used for bandwidth extension, then high-frequency content can be reconstructed, but computational complexity increases
Solution Approach 1:
The patent segments the bandwidth extension process into distinct functional modules: a spectral patching module for high-frequency reconstruction, an echo modeling module for artifact identification, and an echo cancellation module for artifact removal. This modular segmentation allows each component to be optimized independently and reduces the overall computational burden by avoiding full-domain transformations.
Solution Approach 2:
The patent applies partial action by selectively processing only the frequency regions and time segments where echo artifacts are most prominent. Rather than applying complex transformations across the entire spectrum and time domain, the system targets specific problematic areas, reducing computational complexity while maintaining reconstruction accuracy in critical regions.
3Reliability
If bandwidth extension is performed using conventional methods, then audio quality can be maintained, but the system cannot efficiently handle transient portions
Solution Approach 1:
The patent introduces dynamic adaptation by adjusting the bandwidth extension parameters based on the temporal characteristics of the audio signal. For transient portions, the system dynamically modifies the spectral patching strategy and echo cancellation parameters to preserve the sharp attack and decay characteristics, whereas for steady-state regions, it uses more aggressive smoothing to maintain audio quality.
Data Source
AI summary
An apparatus decodes an encoded audio signal. The apparatus includes a spectral domain audio decoder that generates a decoded representation of a set of spectral portions, the decoded representation being spectral prediction residual values. A frequency regenerator generates a reconstructed spectral portion using a portion of the same set spectral portions. The reconstructed spectral portion also includes spectral prediction residual values. An inverse prediction filter is configured using prediction filter information included in the encoded audio signal and performs an inverse prediction over frequency using the spectral prediction residual values.