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[2510.10509] MARS-Sep: Multimodal-Aligned Reinforced Sound Separation
Summary
The paper presents MARS-Sep, a novel reinforcement learning framework for sound separation that enhances semantic consistency by aligning audio processing with multimodal inputs.
Why It Matters
This research addresses the challenge of sound separation by introducing a preference alignment approach, which improves the quality of separated sounds in various contexts. It has implications for applications in audio processing, machine learning, and AI systems, particularly in enhancing user experience through better sound clarity.
Key Takeaways
- MARS-Sep reformulates sound separation as a decision-making process using reinforcement learning.
- The framework utilizes a preference reward model to enhance semantic consistency in sound outputs.
- Extensive experiments show significant improvements in sound separation across multiple benchmarks.
Computer Science > Sound arXiv:2510.10509 (cs) [Submitted on 12 Oct 2025 (v1), last revised 17 Feb 2026 (this version, v2)] Title:MARS-Sep: Multimodal-Aligned Reinforced Sound Separation Authors:Zihan Zhang, Xize Cheng, Zhennan Jiang, Dongjie Fu, Jingyuan Chen, Zhou Zhao, Tao Jin View a PDF of the paper titled MARS-Sep: Multimodal-Aligned Reinforced Sound Separation, by Zihan Zhang and 6 other authors View PDF HTML (experimental) Abstract:Universal sound separation faces a fundamental misalignment: models optimized for low-level signal metrics often produce semantically contaminated outputs, failing to suppress perceptually salient interference from acoustically similar sources. We introduce a preference alignment perspective, analogous to aligning LLMs with human intent. To address this, we introduce MARS-Sep, a reinforcement learning framework that reformulates separation as decision making. Instead of simply regressing ground-truth masks, MARS-Sep learns a factorized Beta mask policy that is steered by a preference reward model and optimized by a stable, clipped trust-region surrogate. The reward, derived from a progressively-aligned audio-text-vision encoder, directly incentivizes semantic consistency with query prompts. Extensive experiments on multiple benchmarks demonstrate consistent gains in Text-, Audio-, and Image-Queried separation, with notable improvements in signal metrics and semantic quality. Our code is available at this https URL. Sound separation sample...
