Paper year
2025
Paper dossier
Chinese instrument music source separation with frequency-attentive multi-band neural networks
Detail viewSimilarity handoff
Review source metadata, abstract, authors, topics, and local similarity context before moving into explanation and ranking views.
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
Ranking details could not be loaded (API 503).
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
0 labels
Topic labels are imported metadata and can be noisy; use them as coarse navigation hints, not authoritative classifications.
Neighbor surface
Similar papers
Similar papers use a separately configured neighbor embedding; it may differ from the embedding version used by the current ranked run.
No embedding-backed neighbors available for this paper/version yet.
Next handoff
Best next moves from here
01
Check recommendation families
Use Recommended to see whether this paper behaves like an emerging or undercited signal in the current ranked feed, or how it appears on the bridge preview / diagnostics view.
02
Inspect nearby topics
Use Trends to understand whether its attached labels are heating up or cooling down inside the curated corpus.
03
Cross-check evaluation baselines
Use Evaluation to compare the dossier readout against citation and recency baselines for the same resolved family run.