High quality coding of wideband audio signals using transform coded excitation (TCX)
Bibliographic record
Abstract
This paper describes the application of transform coded excitation (TCX) coding to encoding wideband speech and audio signals in the bit rate range of 16 kbits/s to 32 kbits/s. The approach uses a combination of time domain (linear prediction; pitch prediction) and frequency domain (transform coding; dynamic bit allocation) techniques, and utilizes a synthesis model similar to that of linear prediction coders such as CELP. However, at the encoder, the high complexity analysis-by-synthesis technique is bypassed by directly quantizing the so-called target signal in the frequency domain. The innovative excitation is derived at the decoder by inverse filtering the quantized target signal. The algorithm is intended for applications whereby a large number of bits is available for the innovative excitation. The TCX algorithm is utilized to encode wideband speech and audio signals with a 50-7000 Hz bandwidth. Novel quantization procedures including inter-frame prediction in the frequency domain are proposed to encode the target signal. The proposed algorithm achieves very high quality for speech at 16 kbits/s, and for music at 24 kbits/s.>
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".