Paper year
2025
Paper dossier
Modeling Time-Variant Responses of Optical Compressors With Selective State Space Models
Detail viewSimilarity handoff
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Citations
3
Authors
2
Topic labels
2
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 5
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=0.8304, citation_velocity=0.1800, topic_growth=1.0000, 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: 0.1661)
Recent attentionused
Recent attention: low; used in final ranking (contribution to score: 0.0900)
Topic momentumused
Topic momentum: high; used in final ranking (contribution to score: 0.3000)
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 3
Multi-topic paper in active topics; no cluster_version on this run so bridge_score was not computed.
Signals: citation_velocity=0.1800, topic_growth=1.0000, diversity_penalty=0.3333
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: 0.0630)
Topic momentumused
Topic momentum: high; used in final ranking (contribution to score: 0.6500)
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: -0.0667)
Under-cited
In top 50 at rank 10
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=0.1800, topic_growth=1.0000, diversity_penalty=0.5579
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: 0.0540)
Topic momentumused
Topic momentum: high; used in final ranking (contribution to score: 0.7000)
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: -0.1395)
Abstract
This paper presents a method for modeling optical dynamic range compressors using deep neural networks with selective state space models. The proposed approach surpasses previous methods based on recurrent layers by employing a selective state space block to encode the input audio. It features a refined technique integrating feature-wise linear modulation and gated linear units to adjust the network dynamically, conditioning the compression's attack and release phases according to external parameters. The proposed architecture is well-suited for low-latency and real-time applications, which are crucial in live audio processing. The method has been validated on the analog optical compressors Tube-Tech CL 1B and Teletronix LA-2A, which possess distinct characteristics. Evaluation is performed using quantitative metrics and subjective listening tests, comparing the proposed method with other state-of-the art models. Results show that black-box modeling methods used here outperform all others, achieving accurate emulation of the compression process for both seen and unseen settings during training. Furthermore, it is shown that there is a correlation between this accuracy and the sampling density of the control parameters in the data set and it is identified the settings with fast attack and slow release as the most challenging to emulate.
Authors
Neighborhood labels
Topics
2 labels
Topic labels are imported metadata and can be noisy; use them as coarse navigation hints, not authoritative classifications.
Extremum Seeking Control SystemsReal-time simulation and control systems
Neighbor surface
Similar papers
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