Paper year
2025
Paper dossier
Feature separation of music across diverse dataset: a comparative perspective
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Citations
0
Authors
0
Topic labels
0
Source readout
Source and corpus status
Venue
Unknown venue
Source slug
unknown
Corpus placement
Controlled edge slice
Similarity rows
Not available yet
Ranking readout
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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
2
Top 50
0
Run label
shadow-generalization-product-candidate-ranking-v1
Snapshot
source-snapshot-shadow-generalization-v1-20260521
Scope: family global | run rank-83787b91ef
Emerging
Present in run, outside top 50
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=0.8630, citation_velocity=0.0000, topic_growth=0.0000, 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: 0.1726)
Recent attentionused
Recent attention: low; used in final ranking (contribution to score: 0.0000)
Topic momentumused
Topic momentum: low; used in final ranking (contribution to score: 0.0000)
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
Present in run, outside top 50
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=0.0000, diversity_penalty=1.0000
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: low; used in final ranking (contribution to score: 0.0000)
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: -0.2000)
Under-cited
No materialized row for this family in the resolved run
This paper did not surface into the current materialized family row set.
Abstract
In music, feature separation is the process of separating distinguishable auditory characteristics, such as pitch, timbre, rhythm, and harmonic content, from a complicated, mixed signal. Virtual reality (VR), gaming, music transcription, karaoke systems, audio restoration, music information retrieval (MIR), music education, and audio forensics, are just a few of the areas where the topic has attracted a lot of attention. Feature extraction is crucial in music separation as it identifies and isolates sound elements, improving accuracy, and reducing noise. It simplifies raw audio into meaningful data for efficient processing and effective model learning. Without it, clean separation of audio components is very difficult. In this research, extracting features from mixed audio sources enables clean and accurate isolation of musical elements, enhancing quality, supporting precise evaluations, and boosting neural network performance across varied datasets including DSD100, MUSDB, and MUSDB18-HQ, which collectively afford rich musical content for making evaluations and benchmarks. Evaluation metrics, such as F1-score, precision, and recall, are utilized to demonstrate the performance data of the extracted features. The MUSDB18-HQ dataset yielded an overall increase of 17.86% in the F1-score metrics with significant increases in drums (+25.05%) and vocals (+20.04%), showing that the dataset was highly effective for feature separation.
Authors
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