Paper year
2025
Paper dossier
Chinese instrument music source separation with frequency-attentive multi-band neural networks
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Citations
1
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
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
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=0.8659, citation_velocity=0.0600, 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: 0.1732)
Recent attentionused
Recent attention: low; used in final ranking (contribution to score: 0.0300)
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=0.0600, 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: 0.0210)
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
Recent progress in music source separation has been accelerated by deep learning techniques, yet most studies have focused on Western instruments and vocals, with limited attention to traditional Chinese instruments. These instruments possess distinctive timbral characteristics and complex performance techniques, which require specialized treatment in separation tasks. This paper introduces a deep learning approach that is tuned to the frequency band to separate traditional Chinese instrument sources. By analyzing the spectral energy distributions of guzheng, dizi, pipa, and xiao, the model adopts differentiated frequency band processing strategies based on each instrument's acoustic profile. The architecture integrates convolutional and recurrent modules with frequency attention and multi-head attention mechanisms to enhance music source separation performance. Extensive experiments with 13 band-division configurations reveal significant variations in sensitivity across instruments, with optimal frequency splits aligning closely with their spectral characteristics. The results demonstrate that the proposed method achieves high-quality music source separation while reducing computational costs through adaptive spectral processing. These findings highlight the importance of culturally informed modeling in the separation of music sources and open new directions for the preservation and analysis of traditional music. All model weights, source code, and audio demonstrations are publicly available at https://huggingface.co/NMLAB8/CISM and https://huggingface.co/spaces/NMLAB8/CISM .
Authors
No authors available.
Neighborhood labels
Topics
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Neighbor surface
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