arXiv:2607.01823v1 Announce Type: cross Abstract: Packet loss concealment (PLC) reconstructs audio packets that are missing at the receiver, usually with a trained model whose parameters remain fixed at deployment time. This treats the PLC model as static, even though each call or recording exposes signal-specific information through the packets that did arrive. We present TTT-PLC, a self-supervised test-time tuning framework that adapts existing PLC models using only those received packets. The method creates supervision by synthetically masking portions of the available signal, training the
The proliferation of audio communication channels and the increasing sophistication of AI models are driving continuous innovation in signal processing and quality enhancement.
Improving the robustness of audio communication against packet loss has direct implications for real-time applications like telemedicine, defense communication, and financial trading platforms, where clear audio is critical.
Existing PLC models can now adapt to unique signal characteristics dynamically at deployment, potentially leading to more resilient and higher-quality audio experiences without constant retraining.
- · Telecommunication companies
- · Real-time audio platform providers
- · AI model developers
- · Consumers of internet-based calls
- · Providers of static, non-adaptive PLC solutions
Enhanced audio quality and reliability in real-time communication across unstable networks.
Improved user experience in VoIP, online gaming, and virtual collaboration tools, potentially increasing their adoption.
Reduced burden on network infrastructure by making applications more tolerant to latency and packet loss, leading to more efficient bandwidth usage.
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