A field guide to equalisation and dynamics processing on rock and electronica records
Bibliographic record
Abstract
Abstract This paper examines two of the most common signal processing techniques, namely, equalisation and dynamics processing. As with all signal processing techniques, equalisation and dynamics processing modify audio signals in particular ways to suit the evolving requirements of a mix. Rock and electronica records currently feature the most extroverted uses for these techniques and, thus, the clearest examples for a field guide like this. It is for this reason, and this reason alone, that I focus on records from these two genres. I begin this field guide by suggesting a definition for ‘signal processing’ which is sufficiently broad to account for every technique that recordists currently use. I then relate that definition to the concept of ‘frequency response’. In my opinion, this concept is crucial to any understanding of signal processing – a core component of the knowledge base for audio engineering, which is the discipline under which signal processing is typically subsumed; the concept of ‘frequency response’ guides many of the decisions about signal processing that recordists make, especially those concerning equalisation. Finally, I explain how equalisation and dynamics processing work, and I offer a field guide to their most common applications on hit rock and electronica records today.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.073 | 0.041 |
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".