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PLC is an important part of audio telecommunications technology and codec development, and methods for performing PLC using machine learning approaches are now becoming viable for practical use. Packet loss, either by missing packets or high packet jitter, is one of the top reasons for speech quality degradation in Voice over IP calls.
With the ICASSP 2024 Audio Deep Packet Loss Concealment Challenge, we intend to stimulate research in audio packet loss concealment. Initially, we will provide a set audio files for validation, degraded by removing segments corresponding to lost packets from real recordings of packet loss events, including lost-packet annotations, with high rates of packet loss, along with the corresponding clean reference files. Participants will be able to use this dataset to validate their approaches. Towards the end of the challenge, we will provide a blind test set constructed in the same way (audio files and lost packet annotations, without references). Building on the previous PLC Challenge at INTERSPEECH 2022, this challenge will feature an overall harder task and improved evaluation procedure.
Submissions will have to fill the gaps in the test set audio files using only a maximum of 20 milliseconds of look-ahead, mirroring the tight requirements of real-time voice communication. We will evaluate each submission’s performance on the blind test set based on crowd-source ITU P.804 mean opinion scores as well as speech recognition rate, and the three approaches with the best weighted average scores will be declared the winners.
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