Deep Generative Packet Loss Concealment for Audio Reconstruction
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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 them, degrading communication quality.
Innovation Solution
A neural network system using a causal adversarial auto-encoder with a generative latent model to predict and reconstruct complete audio signals, employing an encoder and decoder neural network trained with an adversarial neural network to replace lost signal portions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If traditional packet loss concealment methods replicate the most recent packet structure, then the noticeability of lost packets is decreased to some extent, but the interruptions of the communication signal still impede communication quality and users can perceive distortions
Solution Approach 1:
The patent uses a neural network to copy and generate realistic audio signals that replace lost packets. Instead of simply replicating the most recent packet structure, the neural network learns from training data to synthesize natural-sounding audio replacements that match the context, thereby reducing both noticeability and distortions.
Solution Approach 2:
The patent changes the approach from traditional signal processing parameters to neural network-based generation. The neural network processes audio signals through complex transformations in latent space, allowing for more natural and less perceptible reconstructions of lost packets while maintaining communication quality.
2Duration of action of moving object
If traditional audio processing extends the duration of the latest packet to replace lost packets, then some noticeability is reduced, but long interruptions still cause perceptible distortions
Solution Approach 1:
The neural network generates new audio content that copies the style and characteristics of surrounding audio segments, creating realistic replacements that blend seamlessly. This approach maintains natural audio flow better than simply extending the duration of the latest packet.
Solution Approach 2:
The neural network is pre-trained on large datasets of audio signals to learn patterns and characteristics. This preliminary training enables the system to generate high-quality reconstructions without needing to extend packet durations, as the network already understands audio structure and context.
3Measurement precision
If a neural network system with encoder and decoder is used to reconstruct audio signals, then high accuracy reconstruction is achieved, but the system complexity increases
Solution Approach 1:
The patent introduces a latent representation as an intermediary between the encoder and decoder. This latent space simplifies the transformation process by converting complex audio signals into a compressed representation that can be efficiently processed and reconstructed, reducing the effective complexity of the system.
Solution Approach 2:
The patent replaces traditional mechanical signal processing systems with a neural network-based system. While this increases computational complexity, it significantly improves reconstruction accuracy and reduces perceptible distortions, achieving better performance despite the added complexity.
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.