Low Group Delay Interpolation Filter For Delta-Sigma Converters
Bibliographic record
Abstract
This paper shows how a relaxation of the high frequency requirements can help reducing the latency in linear phase interpolation filter, with an audio production system perspective. The reduced need for attenuation is justified when the interpolation filter is followed by a noise-shaping Delta-Sigma loop and an analog filtering stage. This is done by using a non-constant error weight of the stop-band. In order to use the Parks-McClellan method for finite impulse response filter design from Matlab, the stop-band is divided and weighted logarithmically. Quantitative results are shown for different example filter design, limited to situations where the Parks-McClellan converges well. It has been found that the shorter the filter length needed to respect a given filter template, the more relative group delay reduction can be achieved by relaxing the high frequency requirement. For filter size of the order of 100, reduction of group delay of 30% can be expected. For sake of simplicity, the Delta-Sigma loop is discussed but not analysed here. The idea is demonstrated in the context of Digital-to-Analog converters (DAC) but by duality could be applicable also to Analog-to-Digital converters (ADC). The main performance metric used is a relative reduction of the impulse response group delay. The results are also presented as impulse responses and power spectrum examples. The presented approach may be generalised to complex and non-linear phase filters and does not prevent the use of polyphase structures.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".