GAN-Based Packet Loss Concealment for Voice Signal Restoration
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Solution Overview
Problem
Conventional packet loss concealment technologies in voice communication systems, such as VoIP, often result in reduced voice call quality due to limitations in estimating lost packet feature vectors, leading to unnatural sound generation when deep neural networks fail or simple feature vector copying methods are used.
Innovation Solution
A method employing a generative adversarial network (GAN) to train both a classification model and a generative model competitively, using time-area based feature vectors and fast Fourier transform coefficients and phases to estimate and restore lost packets, thereby improving voice signal synthesis and quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional packet loss concealment algorithms (G.722 Appendix IX) are used, then implementation simplicity is maintained, but voice call quality deteriorates due to poor packet loss compensation
Solution Approach 1:
The patent replaces conventional mechanical signal processing methods (G.722 Appendix IX algorithms) with a deep learning-based neural network system. The neural network model learns optimal packet loss concealment strategies from training data, substituting traditional algorithmic approaches with data-driven intelligent processing that achieves superior voice call quality while maintaining reasonable implementation complexity through standardized deep learning frameworks.
2Reliability
If deep neural networks are used for feature vector estimation, then packet loss concealment performance is improved, but unnatural sound is generated when estimation fails
Solution Approach 1:
The patent implements a fallback mechanism that prepares alternative processing paths in advance. When the deep neural network estimation fails or produces unnatural results, the system switches to conventional packet loss concealment algorithms as a pre-prepared backup solution. This cushioning approach ensures continuous natural sound generation by having ready-to-use alternative methods that activate when the primary AI-based method deteriorates.
Solution Approach 2:
The system incorporates quality assessment feedback mechanisms that continuously monitor the output of feature vector estimation. When unnatural sound is detected in the generated audio, the feedback loop triggers a switch from the deep neural network approach to conventional methods. This closed-loop control ensures that packet loss concealment performance is optimized while preventing harmful unnatural sound generation through real-time quality monitoring and adaptive method selection.
3Device complexity
If simple feature vector copying methods are used, then computational complexity is reduced, but packet loss concealment performance deteriorates
Solution Approach 1:
The patent applies feature vector copying selectively rather than universally. The deep neural network performs complex feature vector estimation only when necessary (when packet loss occurs and advanced concealment is needed), while relying on simpler conventional methods for routine operations. This partial application of complex processing maintains packet loss concealment performance for critical cases while reducing overall computational complexity through selective use of resource-intensive operations.
Data Source
AI summary
Disclosed are a packet loss concealment method and apparatus a using a generative adversarial network. A method for packet loss concealment in voice communication may include training a classification model based on a generative adversarial network (GAN) with respect to a voice signal including a plurality of frames, training a generative model having a contention relation with the classification model based on the GAN, estimating lost packet information based on the trained generative model with respect to the voice signal encoded by a codec, and restoring a lost packet based on the estimated packet information.