Resilient Vector Quantization for Frame-Erasure Audio Coding
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Solution Overview
Problem
Existing audio codecs experience annoying artefacts due to prediction errors during frame erasure events, particularly after long runs of prediction in vector quantization, which affect the quality of decoded speech or audio signals.
Innovation Solution
A method and apparatus for quantizing line spectral frequency coefficients that switch between predictive and non-predictive modes based on a distortion measure comparison with a threshold, using a frame error concealment process to determine the optimal mode of operation for subsequent audio frames, thereby minimizing artefacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If predictive mode is used for quantizing LSF coefficients, then coding efficiency is improved, but artefacts occur during frame erasure events
Solution Approach 1:
The vector quantizer dynamically switches between predictive and non-predictive modes based on frame erasure detection. When frame erasure is detected, the system transitions from predictive mode to non-predictive mode to avoid propagating prediction errors that cause artefacts, while maintaining the ability to return to predictive mode when normal operation resumes.
Solution Approach 2:
The invention changes the operational parameter (prediction mode status) of the vector quantizer in response to frame erasure events. By modifying the mode parameter from predictive to non-predictive during erasure conditions, the system prevents the generation of harmful artefacts while preserving coding efficiency during normal operation.
2Object-affected harmful factors
If non-predictive mode is used to avoid artefacts, then signal quality is improved, but coding efficiency decreases
Solution Approach 1:
The system dynamically adjusts the quantizer mode based on operational conditions, using non-predictive mode only when necessary (during frame erasure) to maintain signal quality, and switching to predictive mode during normal operation to maximize coding efficiency. This dynamic adaptation resolves the trade-off by applying each mode selectively.
Solution Approach 2:
The operational parameter (mode selection) is changed conditionally based on frame erasure status. The system maintains high coding efficiency by default in predictive mode and only transitions to non-predictive mode when frame erasure is detected, thus preserving signal quality only when necessary without permanently sacrificing coding efficiency.
3Quantity of substance
If predictive quantization is used continuously, then bit rate is reduced, but error propagation increases during frame erasure
Solution Approach 1:
The system prepares for potential frame erasure by having a non-predictive mode ready to switch to. This pre-configured alternative prevents error propagation during frame erasure events without affecting normal predictive coding operation, thus cushioning against reliability issues while maintaining bit rate efficiency.
Solution Approach 2:
The system changes the prediction parameter status in response to frame erasure detection. By transitioning from predictive to non-predictive mode during erasure events, the system prevents error propagation while maintaining low bit rate during normal operation, thus resolving the reliability-bit rate trade-off.
Data Source
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AI summary
It is inter alia disclosed to quantise a vector of a plurality of coefficients using a predictive mode of operation of a vector quantiser, wherein the vector quantiser can operate in either a predictive mode of operation or a non-predictive mode of operation, determine a vector of a plurality of recovered coefficients; compare the vector of the plurality of coefficients to the vector of the plurality of recovered coefficients; and determine the mode of operation of the vector quantiser for a vector of a plurality of coefficients associated with a subsequent frame of audio samples, wherein the mode of operation is dependent on the comparison.