LPC Residual Coding via Cross-Module Learning for Speech
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional speech codecs face challenges in balancing low bit rate, high perceptual quality, and low complexity, particularly in modeling LPC residual signals for efficient speech coding, which affects the scalability and efficiency of neural speech codecs.
Innovation Solution
A method and apparatus for optimizing LPC coefficient and residual signal quantization using a stepwise autoencoder structure, incorporating cross-module residual learning, 1D-CNN autoencoders, and differential coding to improve bit assignment and reduce model complexity, enabling scalable waveform coding with improved speech quality at lower bit rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If LPC analysis and quantization are applied to speech signals, then speech coding efficiency is improved, but model complexity increases during decoding
Solution Approach 1:
The speech signal is segmented into frames and further into sub-frames, with LPC analysis performed on each segment. The decoding process is similarly segmented, with different neural network modules handling different aspects of reconstruction, distributing complexity across multiple simpler components rather than one complex decoder
Solution Approach 2:
A neural network-based intermediate representation is introduced between the LPC coefficients and the final speech reconstruction. This intermediary learns optimal transformations and mappings, simplifying the overall decoding process while maintaining high coding efficiency
2Manufacturing precision
If residual signals are directly compressed to desired bit rate, then speech quality is improved, but bit rate control becomes less scalable
Solution Approach 1:
The system uses dynamic bit rate allocation where the neural network adapts the compression level and reconstruction quality based on the desired bit rate. Different quantization levels and network module configurations can be activated dynamically to achieve scalable performance across multiple bit rates
Solution Approach 2:
The system changes key parameters such as quantization bit depth, neural network activation levels, and filter bank resolutions to achieve different bit rates while maintaining optimal speech quality for each rate. This allows seamless adaptation from low to high bit rates
3Use of energy by moving object
If conventional vocoders use LPC residual modeling with pitch pulse train or white noise, then computational efficiency is improved, but perceptual quality deteriorates
Solution Approach 1:
The traditional mechanical/mathematical LPC residual modeling (pitch pulse trains and white noise generation) is replaced with a neural network-based residual signal reconstruction system. The neural network learns optimal residual signal characteristics from data, providing superior perceptual quality while maintaining computational efficiency through optimized network architectures
Data Source
AI summary
Disclosed are a method for coding a residual signal of LPC coefficients based on collaborative quantization and a computing device for performing the method. The residual signal coding method includes: generating encoded LPC coefficients and LPC residual signals by performing LPC analysis and quantization on an input speech; Determining a predicted LPC residual signal by applying the LPC residual signal to cross module residual learning; Performing LPC synthesis using the coded LPC coefficients and the predicted LPC residual signal; It may include the step of determining an output speech that is a synthesized output according to a result of performing the LPC synthesis.