Voice Frame Reconstruction via Neural Network Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
VoIP systems face voice quality impairment due to limited signal analysis and voice processing capabilities, particularly in scenarios with sudden packet loss, where existing packet loss concealment technologies are inadequate.
Innovation Solution
A voice processing method that determines historical voice frames, extracts frequency-domain characteristics, and uses a network model comprising neural networks to predict and reconstruct target voice frames, improving signal analysis and processing capabilities for efficient packet loss concealment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional PLC technology is used for packet loss concealment, then the system is simple to implement, but the voice quality deteriorates under sudden packet loss scenarios
Solution Approach 1:
The patent replaces traditional signal processing methods with deep learning-based neural networks. Specifically, it uses autoencoders and generative adversarial networks to reconstruct voice frames, substituting conventional mechanical signal analysis with intelligent algorithms that can handle sudden packet loss more effectively while maintaining acceptable system complexity
Solution Approach 2:
The patent transforms voice signals from time-domain to frequency-domain representations using Fourier transforms, and changes the parameter space by encoding voice frames into compact representations. This parameter transformation enables more efficient reconstruction and improves voice quality under packet loss conditions
2Measurement precision
If deep learning models with multiple neural networks are used to predict frequency-domain characteristics, then the processing precision improves, but the computational complexity increases
Solution Approach 1:
The patent segments the voice processing task into distinct stages: encoding time-domain signals to frequency-domain representations, predicting spectral characteristics using separate neural network components, and reconstructing voice frames. This segmentation allows each component to be optimized independently, improving overall precision while managing complexity
Solution Approach 2:
The patent designs a universal network architecture where the encoder and decoder components can handle multiple functions: feature extraction, spectral prediction, and voice frame reconstruction. This multi-functionality reduces the need for separate specialized networks, thereby improving precision without proportionally increasing complexity
3Reliability
If real-time voice frame reconstruction is performed using neural networks, then the packet loss concealment effectiveness improves, but the processing time increases
Solution Approach 1:
The patent performs preliminary encoding of voice frames into compact frequency-domain representations before transmission. When packet loss occurs, the decoder can rapidly reconstruct lost frames using these pre-computed representations and the trained neural network model, reducing real-time processing time while maintaining concealment effectiveness
Solution Approach 2:
The patent uses the encoder to create compact copies of voice frame characteristics in the frequency domain. These copies serve as sufficient statistics for reconstruction, allowing the decoder to generate accurate approximations of lost frames without needing to process the full original signal, thereby reducing processing time
Data Source
AI summary
A voice processing method includes: determining a historical voice frame corresponding to a target voice frame; determining a frequency-domain characteristic of the historical voice frame; invoking a network model to predict the frequency-domain characteristic of the historical voice frame, to obtain a parameter set of the target voice frame, the parameter set including a plurality of types of parameters, the network model including a plurality of neural networks (NNs), and a number of the types of the parameters in the parameter set being determined according to a number of the NNs; and reconstructing the target voice frame according to the parameter set.