Audio Coding Codeword Modification for Bit-Rate Reduction
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Solution Overview
Problem
Traditional audio coding techniques fail to efficiently represent certain frequency components, leading to suboptimal bit-rate and quality trade-offs, as they do not effectively utilize perceptual models to reduce bit-rate without compromising audio quality.
Innovation Solution
The technique modifies codewords using linear or non-linear transformations and combinations to represent frequency bands as shaped noise or versions of other bands, allowing for reduced bit-rate encoding while maintaining perceptual quality, by using modified codewords that can be scaled, transformed, or combined from existing codebooks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional perceptual audio coding is used to reduce bit-rate, then bit-rate is reduced, but coding efficiency is insufficient
Solution Approach 1:
The patent uses code vectors from a codebook as templates to represent frequency components. Instead of encoding each frequency component independently, the system copies and scales code vectors that match the spectral shape, significantly reducing the number of bits needed while maintaining perceptual quality.
Solution Approach 2:
The patent transforms code vectors using scaling factors and spectral shaping to adapt them to different frequency bands. By changing parameters like scale factor and spectral shape, the system can represent diverse frequency components using a limited set of code vectors, improving coding efficiency.
2Manufacturing precision
If more frequency components are accurately coded, then audio quality is improved, but bit-rate increases
Solution Approach 1:
The patent applies different coding strategies to different frequency bands based on their perceptual importance. Critical bands receive more accurate representation using code vectors, while less critical bands use simplified noise-shaped representations, optimizing the balance between quality and bit-rate.
Solution Approach 2:
The patent codes only the most perceptually important frequency components accurately using code vectors, while representing less important components with simplified models. This partial coding approach maintains perceived audio quality while significantly reducing bit-rate requirements.
3Productivity
If code vectors are transformed and combined to represent frequency bands, then coding efficiency is improved, but device complexity increases
Solution Approach 1:
The patent pre-computes and stores code vectors in a codebook during system initialization. These code vectors are prepared in advance and can be directly applied during encoding without complex real-time computation, reducing device complexity while maintaining coding efficiency.
Solution Approach 2:
The patent uses simple scaling factors and selection indices instead of complex transformations during encoding. The encoder only needs to select and scale pre-computed code vectors, avoiding expensive real-time signal processing operations and reducing device complexity.
Data Source
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AI summary
Coding of spectral data by representing certain portions of the spectral data as a scaled version of a code-vector, where the code-vector is chosen from either a fixed predetermined codebook or a codebook taken from a baseband. Various optional features are described for modifying the code-vectors in the codebook according to some rules which allow the code-vector to better represent the data they are modeling. The code-vector modification comprises a linear or non-linear transform of one or more code-vectors, such as, by exponentiation, negation, reversing, or combining elements from plural code-vectors.