Dynamically Adding Redundancy For Improved Error Concealment In Packet Voice Coding
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Data is sent in packets of bits over the Internet. However, packets may not arrive in order or in time for playout. Packet loss is a frequently encountered problem in Voice-over-IP (VoIP) applications. Modern speech coders use past information to decode current packets in order to reach very low bit-rates. Therefore, when a packet is lost, the effect of this packet loss propagates over several subsequent packets. In this thesis, a new redundancy-based packet-loss-concealment scheme is presented. Many redundancy-based packet-loss-concealment schemes send a fixed amount of extra information about the current packet as part of the subsequent packet, but not every packet is equally important for packet loss concealment. We have developed an algorithm to determine the importance of packets and we propose that extra information should only be sent for the important packets. This provides a lower average bit-rate compared to sending the same amount of extra information for each and every packet. We use a linear prediction (LP) based speech coder (ITU-T G.723.1) as a test platform and we propose that only the excitation parameters should be sent as extra information since LP parameters of a frame can be estimated using the LP parameters of the previous frame. Furthermore, we propose that excitation parameters of an important frame that are sent as redundant information should be used in the reconstruction of the lost waveform---as a consequence, the states of the subsequent frame will also be updated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it