LSF Coefficient Quantization Using Inter- and Intra-Frame Prediction
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Solution Overview
Problem
The large dynamic range and sensitivity of linear predictive coding (LPC) coefficients to quantization errors compromise filter stability, and existing quantization methods struggle with frames of sharply varying frequency characteristics, leading to decreased performance.
Innovation Solution
A quantization module that includes an inter-frame predictor, a trellis coded vector quantizer, and intra-frame predictor to generate prediction vectors and quantize error vectors, optimizing quantization based on signal characteristics and bit rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If LPC coefficients are quantized directly, then the quantization process is simple, but filter stability is not guaranteed due to large dynamic range and sensitivity to quantization errors
Solution Approach 1:
The patent transforms LPC coefficients into LSF coefficients through a mathematical transformation. This parameter change converts the quantization problem from one involving large dynamic range coefficients to one involving bounded coefficients (0 to π), thereby ensuring filter stability while maintaining quantization effectiveness.
Solution Approach 2:
The patent introduces LSF coefficients as an intermediary representation between the original LPC coefficients and the quantized values. This intermediary form preserves the spectral information while providing a more stable basis for quantization, resolving the contradiction between simplicity and stability.
2Productivity
If an inter-frame predictor is used to exploit high inter-frame correlation of LSF coefficients, then quantization gain is increased, but quantization performance decreases for frames with sharply varying frequency characteristics
Solution Approach 1:
The patent employs a dynamic switching mechanism that adapts the quantization strategy based on the signal characteristics of each frame. When frames exhibit sharp variations, the system switches to a mode that prioritizes accuracy over compression efficiency, thereby maintaining reliable quantization performance across diverse signal conditions.
Solution Approach 2:
The patent changes the quantization parameters and strategy based on the detected signal characteristics. For sharply varying frames, different quantization thresholds and prediction weights are applied compared to stationary frames, allowing the system to optimize performance for each specific condition rather than using a fixed approach.
3Reliability
If multiple quantization schemes are provided for different signal characteristics, then quantization performance is optimized, but device complexity increases
Solution Approach 1:
The patent segments the quantization process into distinct stages: LSF transformation, inter-frame prediction, error quantization, and intra-frame prediction. Each segment handles a specific aspect of the quantization task, allowing the system to achieve high performance through modular processing rather than a monolithic complex structure.
Solution Approach 2:
The patent performs preliminary transformations (LPC to LSF conversion and inter-frame prediction) before the actual quantization step. This preliminary processing reduces the complexity of the subsequent quantization by pre-processing the data into a more favorable form, thereby achieving optimized performance without proportionally increasing overall device complexity.
Data Source
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AI summary
A quantization module quantizing a line spectral frequency, LSF, coefficient of a speech or audio signal comprising an inter-frame predictor configured to generate a prediction vector of a current frame from a quantized N-dimension sub-vector of a previous frame, where N is a natural number greater than or equal to 2; a first quantizer comprising a trellis coded vector quantizer configured to quantize an error vector of a current stage between a prediction vector of the current stage and a prediction error vector, wherein the prediction error vector corresponds to a difference between the prediction vector of the current frame and a N-dimension sub-vector of the current stage; an intra-frame predictor configured to generate the prediction vector of the current stage by using a quantized prediction error vector of a previous stage and a prediction matrix of the current stage, wherein the prediction matrix is a prediction coefficient having NXN matrix format; and a second quantizer comprising a vector quantizer configured to quantize a quantization error vector which corresponds to a difference between a quantized prediction error vector of the current stage and the prediction error vector, wherein the N-dimension sub-vector of the current stage is obtained by removing a predefined mean value from the LSF coefficient, and wherein the quantized prediction error vector of the current stage is obtained by adding the quantized error vector of the current stage and the prediction vector of the current stage.