Paper year
2025
Paper dossier
Analysis of Various 3D Acquisition Techniques and Mesh Differences for Head-related Transfer Functions Calculation
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Citations
1
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 14
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=0.7870, citation_velocity=0.0600, 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: 0.1574)
Recent attentionused
Recent attention: low; used in final ranking (contribution to score: 0.0300)
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 9
Multi-topic paper in active topics; no cluster_version on this run so bridge_score was not computed.
Signals: citation_velocity=0.0600, 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: 0.0210)
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 6
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=0.0600, topic_growth=1.0000, diversity_penalty=0.2789
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: 0.0180)
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: -0.0697)
Abstract
The head mesh is a fundamental component in simulating head-related transfer functions (HRTFs). The techniques utilized for acquiring and preprocessing 3D meshes prior to calculation directly influence HRTF results. This study aims to compare the meshes obtained through different methods and analyze the impact of mesh differences on HRTFs. Three mesh capture methods based on different technical principles were employed to obtain the meshes of the human head: magnetic resonance imaging, optical scanner, and LightCage. A comparative analysis revealed that the lateral pinna parameters of the magnetic resonance imaging mesh tend to be larger than those from other methods owing to the lack of ear shape preservation, leading to significant variations in HRTF. The impact of differences in the canal and hair areas of the meshes on HRTFs was also evaluated, revealing that the canal had minimal influence on directional transfer functions of HRTFs. Moreover, bulging caused by hair did not affect localization performance. Based on these results, the study analyzed the advantages and limitations of various methods and their corresponding principles. This research serves as a reference for selecting head mesh acquisition methods and mesh preprocessing for HRTF simulations.
Authors
Neighborhood labels
Topics
2 labels
Topic labels are imported metadata and can be noisy; use them as coarse navigation hints, not authoritative classifications.
Engineering Applied ResearchSimulation and Modeling Applications
Neighbor surface
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