Long-Term Prediction State Management in Speech Coding
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Solution Overview
Problem
Existing speech coding technologies face inefficiencies in long-term prediction due to fluctuations in LPC coefficients, leading to increased energy in LTP residuals and error propagation issues during packet loss.
Innovation Solution
The method involves modifying the LTP state by re-whitening it using updated LPC coefficients, and scaling down the LTP state to reduce energy and mitigate error propagation, thereby improving coding efficiency and resilience to packet loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If LTP prediction is used to exploit periodicity in speech signals, then coding efficiency is improved, but error propagation occurs during packet loss
Solution Approach 1:
The LTP state is segmented into multiple vectors that are processed independently. During packet loss, only the affected segment needs to be concealed while other segments remain valid, limiting error propagation. The segmentation allows selective updating of LTP state vectors based on packet reception status.
Solution Approach 2:
The LTP state vectors are pre-computed and stored before transmission. At the receiver, these pre-computed vectors can be directly used for prediction without real-time computation, enabling faster error concealment and reducing the impact of packet loss on overall system performance.
2Measurement precision
If LPC coefficients are updated frequently to improve spectral accuracy, then measurement precision is improved, but energy in LTP residuals increases
Solution Approach 1:
Different parts of the LTP state are treated with different update frequencies. The LPC coefficients used for LTP state computation are updated at specific rates optimized for each local requirement, rather than uniformly across all parameters. This allows spectral accuracy where needed while minimizing residual energy elsewhere.
Solution Approach 2:
The patent changes the parameters of LTP state vectors by applying transformations such as whitening and scaling. These parameter changes optimize the energy distribution in the residual signal while maintaining spectral accuracy, effectively resolving the contradiction between precise spectral representation and minimal residual energy.
3Productivity
If LTP state is transformed (whitening and scaling) to reduce residual energy, then coding efficiency is improved, but device complexity increases
Solution Approach 1:
The complex whitening and scaling transformations are extracted as separate, modular processing stages. Each transformation is implemented as an independent function that can be optimized separately, reducing overall system complexity while maintaining the benefits of reduced residual energy.
Data Source
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AI summary
A method, system and computer program for encoding speech according to a source-filter model. The method comprises deriving a spectral envelope signal representative of a modelled filter and a first remaining signal representative of a modelled source signal, and deriving a second remaining signal from the first remaining signal by, at intervals during the encoding: exploiting a correlation between approximately periodic portions in the first remaining signal to generate a predicted version of a later portion from a stored version of an earlier portion, and using the predicted version of the later portion to remove an effect of said periodicity from the first remaining signal. The method further comprises, once every number of intervals, transforming the stored version of the earlier portion of the first remaining signal prior to generating the predicted version of the respective later portion.