Schrödinger Bridge Mamba for One-Step Speech Enhancement

📝 Original Info

• Title: Schrödinger Bridge Mamba for One-Step Speech Enhancement
• ArXiv ID: 2510.16834
• Date: 2025-10-19
• Authors: ** 논문에 저자 정보가 제공되지 않았습니다. (정보 없음) **

📝 Abstract

We propose Schrödinger Bridge Mamba (SBM), a new concept of training-inference framework motivated by the inherent compatibility between Schrödinger Bridge (SB) training paradigm and selective state-space model Mamba. We exemplify the concept of SBM with an implementation for generative speech enhancement. Experiments on a joint denoising and dereverberation task using four benchmark datasets demonstrate that SBM, with only 1-step inference, outperforms strong baselines with 1-step or iterative inference and achieves the best real-time factor (RTF). Beyond speech enhancement, we discuss the integration of SB paradigm and selective state-space model architecture based on their underlying alignment, which indicates a promising direction for exploring new deep generative models potentially applicable to a broad range of generative tasks. Demo page: https://sbmse.github.io

💡 Deep Analysis

📄 Full Content

📸 Image Gallery

Reference