Deep Generative Packet Loss Concealment for Long Audio Gaps
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Solution Overview
Problem
Existing packet loss concealment methods in communication protocols like VoIP result in noticeable distortions and interruptions, especially for long interruptions, despite attempts to minimize their impact, leading to degraded communication quality.
Innovation Solution
A neural network system using an encoder and decoder, trained adversarially, generates a complete audio signal by predicting a latent representation and reconstructing it, with optional generative latent models for longer-term predictions, enabling accurate and deterministic reconstruction of audio signals beyond traditional autoregressive models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional packet loss concealment methods are used, then the system is simple and easy to implement, but the audio reconstruction quality is poor with noticeable distortions and interruptions
Solution Approach 1:
The patent replaces traditional mechanical signal processing methods with a deep neural network-based system. The encoder-decoder architecture with adversarial training substitutes conventional audio processing techniques, enabling high-quality audio reconstruction that is imperceptible to human listeners while maintaining computational efficiency through the learned representations.
Solution Approach 2:
The patent transforms the audio signal from the time domain to a latent representation space through the encoder, then reconstructs it through the decoder. This parameter transformation allows the system to capture complex temporal and spectral patterns that traditional methods cannot handle, achieving superior reconstruction quality by operating in a transformed feature space.
2Reliability
If autoregressive models are used for audio reconstruction, then the system can generate audio signals, but the output is non-deterministic and requires multiple inference steps
Solution Approach 1:
Instead of generating audio signal frames sequentially one after another (autoregressive approach), the patent inverts the process by using an encoder to compress the audio into a latent representation and then decode it all at once. This inversion enables deterministic output since the same input always produces the same latent representation and consequently the same reconstructed audio, while achieving parallel processing for faster inference.
3Reliability
If simple packet loss concealment is applied, then processing time is minimal, but communication quality is degraded for long interruptions
Solution Approach 1:
The system performs preliminary action by pre-training the neural network models on extensive audio data and packet loss scenarios before actual communication occurs. The encoder learns to extract meaningful temporal and spectral patterns from complete audio signals, and the decoder learns to reconstruct them accurately. This preliminary learning enables the system to handle long interruptions efficiently during actual operation without requiring excessive processing time.
Solution Approach 2:
The patent maintains continuity of useful action by processing audio signals in continuous batches through the encoder-decoder pipeline. Rather than handling packet loss interruptions as isolated events, the system processes sequences of audio frames continuously, maintaining the flow of useful computational work and achieving efficient processing even for long interruption scenarios through sustained neural network inference.
Data Source
AI summary
The present disclosure relates to a method and system for performing packet loss concealment using a neural network system. The method comprises obtaining a representation of an incomplete audio signal, inputting the representation of the incomplete audio signal to an encoder neural network and outputting a latent representation of a predicted complete audio signal. The latent representation is input to a decoder neural network which outputs a representation of a predicted complete audio signal comprising a reconstruction of the original portion of the complete audio signal, wherein said encoder neural network and said decoder neural network have been trained with an adversarial neural network.