Paper year
2025
Paper dossier
Toward an Improved Auditory Model for Predicting Binaural Coloration
Detail viewSimilarity handoff
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Citations
6
Authors
2
Topic labels
1
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 3
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=0.8446, citation_velocity=0.3600, 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: 0.1689)
Recent attentionused
Recent attention: medium; used in final ranking (contribution to score: 0.1800)
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 4
Multi-topic paper in active topics; no cluster_version on this run so bridge_score was not computed.
Signals: citation_velocity=0.3600, topic_growth=1.0000, diversity_penalty=0.6667
Why this surfaced | 2 used | 1 penalty | 2 not computed
Semantic matchnot computed
Semantic match: not computed for this run
Recent attentionused
Recent attention: medium; used in final ranking (contribution to score: 0.1260)
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: -0.1333)
Under-cited
In top 50 at rank 11
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=0.3600, topic_growth=1.0000, diversity_penalty=0.7831
Why this surfaced | 2 used | 1 penalty | 2 not computed
Semantic matchnot computed
Semantic match: not computed for this run
Recent attentionused
Recent attention: medium; used in final ranking (contribution to score: 0.1080)
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: -0.1958)
Abstract
The evaluation of audio quality is important in the development of immersive audio algorithms and reproduction systems, and binaural models are often used for this as a quick alternative to listening tests. Coloration (i.e., perceived loudness differences integrated across ears and frequency) is one key quality aspect; however, the majority of models used to predict coloration are often oversimplified or are missing a dedicated binaural stage to consider the relative contribution of the left and right ear signals. A binaural coloration model is presented that builds upon previous work and tests three different approaches for its binaural stage. The proposed model is evaluated in comparison with nine models that are frequently used to predict coloration by using data from five listening tests totaling 252 stimuli with various audio contents and source positions. The proposed model performed best with 85% of explained variance, followed by predictions based on ISO 532-1 loudness, yielding 78% explained variance. The commonly used log-spectral distance performed worst, with only 44% explained variance. The three tested binaural stages had little influence on the performance of the proposed model. The model is made freely available to download.
Authors
Neighborhood labels
Topics
1 labels
Topic labels are imported metadata and can be noisy; use them as coarse navigation hints, not authoritative classifications.
Color Science and Applications
Neighbor surface
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Next handoff
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