Paper year
2025
Paper dossier
Reverse Engineering of Music Mixing Graphs With Differentiable Processors and Iterative Pruning
Detail viewSimilarity handoff
Review source metadata, abstract, authors, topics, and local similarity context before moving into explanation and ranking views.
Citations
0
Authors
7
Topic labels
3
Source readout
Source and corpus status
Venue
Journal of the Audio Engineering Society
Source slug
jaes
Corpus placement
Core corpus
Similarity rows
Not available yet
Ranking readout
Where this paper lands in the current run
Run shadow-generalization-product-candidate-ranking-v1Top 50 surfaced
This block uses the same resolved ranking run as Recommended. Ranks here are materialized paper_scores ranks; live Emerging may be reordered by the bounded ML scorer. Family rank is global within each family, but rank is only shown when this paper lands inside the surfaced top 50.
Families present
3
Top 50
3
Run label
shadow-generalization-product-candidate-ranking-v1
Snapshot
source-snapshot-shadow-generalization-v1-20260521
Scope: family global | run rank-83787b91ef
Emerging
In top 50 at rank 30
Emerging: embedding slice fit vs included-corpus centroid (title+abstract), plus citation velocity and topic growth; not universal relevance. Bridge signal not used here.
Signals: semantic=0.8279, citation_velocity=0.0000, topic_growth=0.9216, diversity_penalty=0.0000
Why this surfaced | 3 used | 1 penalty | 1 not computed
Embedding slice fit (corpus centroid)used
Embedding slice fit (corpus centroid): high; used in final ranking (contribution to score: 0.1656)
Recent attentionused
Recent attention: low; used in final ranking (contribution to score: 0.0000)
Topic momentumused
Topic momentum: high; used in final ranking (contribution to score: 0.2765)
Cross-cluster signalnot computed
Cross-cluster signal: not computed for this run
Similarity penaltypenalty
Similarity penalty: reduces score when non-zero (contribution to score: 0.0000)
Bridge
In top 50 at rank 13
Multi-topic paper in active topics; no cluster_version on this run so bridge_score was not computed.
Signals: citation_velocity=0.0000, topic_growth=0.9216, diversity_penalty=0.0000
Why this surfaced | 2 used | 1 penalty | 2 not computed
Semantic matchnot computed
Semantic match: not computed for this run
Recent attentionused
Recent attention: low; used in final ranking (contribution to score: 0.0000)
Topic momentumused
Topic momentum: high; used in final ranking (contribution to score: 0.5990)
Cross-cluster signalnot computed
Cross-cluster signal: not computed for this run
Topic breadth penaltypenalty
Topic breadth penalty: reduces score when non-zero (contribution to score: 0.0000)
Under-cited
In top 50 at rank 8
Low-cite candidate pool (see docs/candidate-pool-low-cite.md v0): core corpus, recency floor, citation ceiling, title+abstract gate; popularity penalty among pool members only. Semantic and bridge not yet modeled.
Signals: citation_velocity=0.0000, topic_growth=0.9216, diversity_penalty=0.0000
Why this surfaced | 2 used | 1 penalty | 2 not computed
Semantic matchnot computed
Semantic match: not computed for this run
Recent attentionused
Recent attention: low; used in final ranking (contribution to score: 0.0000)
Topic momentumused
Topic momentum: high; used in final ranking (contribution to score: 0.6451)
Cross-cluster signalnot computed
Cross-cluster signal: not computed for this run
Pool popularity penaltypenalty
Pool popularity penalty: reduces score when non-zero (contribution to score: 0.0000)
Abstract
Reverse engineering of music mixes aims to uncover how dry source signals are processed and combined to produce a final mix. In this paper, prior works are extended to reflect the compositional nature of mixing and search for a graph of audio processors. First, a mixing console is constructed, applying all available processors to every track and subgroup. With differentiable processor implementations, their parameters are optimized with gradient descent. Next, the process of removing negligible processors and fine-tuning the remaining ones is repeated. This way, the quality of the full mixing console can be preserved while removing approximately two-thirds of the processors. The proposed method can be used not only to analyze individual music mixes but also to collect large-scale graph data for downstream tasks such as automatic mixing. Especially for the latter purpose, efficient implementation of the search is crucial. To this end, an efficient batch-processing method that computes multiple processors in parallel is presented. Also, the "dry/wet" parameter of each processor is exploited to accelerate the search. Extensive quantitative and qualitative analyses are conducted to evaluate the proposed method's performance, behavior, and computational cost.
Authors
Neighborhood labels
Topics
3 labels
Topic labels are imported metadata and can be noisy; use them as coarse navigation hints, not authoritative classifications.
Music Technology and Sound StudiesModular Robots and Swarm IntelligenceCellular Automata and Applications
Neighbor surface
Similar papers
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No embedding-backed neighbors available for this paper/version yet.
Next handoff
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