Speech Signal Rotation for Low-Distortion Lattice Quantization
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Solution Overview
Problem
Existing audio codecs do not optimally utilize lattice rotations for quantization, leading to suboptimal performance, especially at low bit rates, as the lattice structure is not aligned with the data distribution, resulting in higher distortion and inefficiency in encoding speech and audio signals.
Innovation Solution
The proposed solution involves rotating the signal with respect to a lattice for quantization, aligning the denser lattice directions with the data distribution, and using entropy encoding to minimize distortion, which is achieved by estimating signal distributions and applying appropriate rotation angles and axes based on Gaussian modeling and clustering analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If lattice vector quantization is used without signal rotation, then the encoding process is simpler and faster, but the quantization distortion is higher and encoding efficiency is lower
Solution Approach 1:
The patent applies signal rotation as a preliminary action before lattice vector quantization. By rotating the signal to align with the lattice structure in advance, the quantization process achieves lower distortion without requiring complex adaptive adjustments during quantization. This preliminary alignment step resolves the contradiction by preparing the signal optimally before the main quantization operation.
Solution Approach 2:
The patent changes the parameter of signal orientation by applying rotation transformations. This parameter change aligns the signal's principal components with the lattice axes, thereby improving quantization efficiency and reducing distortion. The rotation angles are optimized based on signal characteristics, dynamically adjusting the parameter to achieve better compression performance.
2Manufacturing precision
If more codebook entries are added to reduce distortion, then the quantization accuracy improves, but the bit rate and complexity increase
Solution Approach 1:
Instead of increasing codebook size, the patent changes the parameter of signal orientation through rotation. This parameter transformation aligns the signal with the lattice structure, achieving better quantization accuracy with the same codebook size. The rotation effectively exploits signal correlations to reduce the information loss inherent in quantization without requiring additional bits.
3Productivity
If fixed rate lattice vector quantization is used, then the encoding complexity is reduced, but the performance at variable bit rates is suboptimal
Solution Approach 1:
The patent introduces dynamics into the lattice vector quantization process by making the rotation angles adaptive to signal characteristics. While the lattice structure itself remains fixed for computational efficiency, the rotation parameters are dynamically adjusted based on signal statistics, enabling the system to achieve optimal performance across variable bit rates without sacrificing encoding speed.
Data Source
AI summary
An apparatus configured to model an encoded signal to estimate at least one distribution of the signal, rotate the signal with respect to a lattice, for lattice quantization of the signal, dependent on the at least one distribution of the signal, and quantize the signal rotated with respect to the lattice.