Paper year
2025
Paper dossier
A music source separation method integrating time-frequency decoupling and mamba-based state space modeling
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unknown
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Run shadow-generalization-product-candidate-ranking-v1Top 50 surfaced
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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
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Signals: semantic=0.8586, 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: 0.1717)
Recent attentionused
Recent attention: low; used in final ranking (contribution to score: 0.0000)
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Bridge
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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
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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
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Topic breadth penalty: reduces score when non-zero (contribution to score: -0.2000)
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Abstract
Music source separation, as a fundamental task in intelligent audio processing, plays a critical role in enhancing the performance of music generation, editing, and understanding systems. However, existing separation models often suffer from structural limitations such as reliance on a single modeling path, entangled time-frequency representations, and difficulty in adapting to heterogeneous sound sources. Furthermore, they struggle to maintain an effective balance between long-range dependency modeling and inference efficiency. To address these challenges, this paper proposes a novel dual-path state space modeling architecture, MSNet. By introducing decoupled modeling mechanisms for temporal and frequency pathways, and incorporating Mamba-based state space units for multidimensional structural parsing of audio signals, MSNet enhances selective control and structural representation in time-frequency modeling. Experimental results demonstrate that MSNet achieves state-of-the-art performance on the MUSDB18 dataset across multiple evaluation metrics. In particular, it shows superior robustness and stability when dealing with dynamically complex sources such as vocals and drums. Additionally, the model achieves a real-time factor (RTF) below 0.1 while maintaining superior separation quality, making it suitable for deployment in practical applications. This study not only demonstrates the feasibility of state space models for complex audio modeling but also introduces a new architectural paradigm for music source separation that balances accuracy and efficiency. The implementation is publicly available at: https://github.com/NMLAB8/Mamba-S-Net.
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