High Quality and Low Latency Interpolation Filters for FPGA-Based Audio Digital-to-Analog Converters
Bibliographic record
Abstract
This paper explores the implications of designing and implementing a high-quality interpolation filter in a low-latency digital to analog converter (DAC) context. The main finding is that the phase delay and the implementation complexity of the filter augment sub-linearly with the filter size. To achieve high quality and low latency, the proposed method uses a multi-level minimum-phase finite-impulse-response filter. The focus is given in the first interpolation stage since it is the one that requires the sharpest transition band. The implementation uses parallelized polyphase filters with special attention given to quantization. Substantial variations in the coefficient's amplitude level between the parallelization branches made it possible to use different quantization while reducing the overall complexity. Comparing the phase delay between a 2046 anda 8184 coefficients filters at 2 kHz frequency shows an increased latency of 0.67% from an original delay of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$66.9\ \mu\mathrm{s}$</tex> . The system was implemented on System Generator for DSP (SysGen).
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How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".