音乐源分离训练（MSST）：训练和评估音乐分离模型的统一框架
Music-Source-Separation-Training (MSST): A Unified Framework for Training and Evaluating Music Demixing Models
- National Research University Higher School of Economics (HSE University)（俄罗斯国立研究型高等经济大学）
- AlphaChip LLC.（阿尔法芯片有限责任公司）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
本文针对音乐源分离任务提出MSST框架，统一训练、验证和推理，支持多种模型架构、数据处理方式、损失函数及评估指标，还有提升分离质量的实用技术，通过整合组件降低实验门槛，实现快速迭代。
中文摘要 AI 辅助
音乐源分离（MSS）是从多音混合中恢复单个声音成分的任务，对从卡拉OK、混音到音频恢复和内容制作等应用至关重要。分离质量取决于整个流程中的工程决策。本文提出MSST，一个用于MSS任务的通用开源框架，在单一配置驱动接口下统一训练、验证和推理。它支持多种模型架构、数据预处理和增强、损失函数及评估指标，还支持多种提升分离质量的实用技术。消融研究证明了这些技术的有效性。通过将组件整合到可重现、YAML可配置框架中，MSST降低了系统实验的门槛，实现从想法到可验证结果的快速迭代。
英文摘要
Music Source Separation (MSS), the task of recovering individual sound components (stems) from a polyphonic mixture, is central to applications ranging from karaoke and remixing to audio restoration and content production. The separation quality depends on engineering decisions across the entire pipeline: model choice, training data preparation and augmentation, loss function and metrics choice, training configuration, validation, and post-processing. This paper presents MSST (Music-Source-Separation-Training) - a universal open-source framework for MSS tasks, which unifies training, validation, and inference for a broad range of modern demixing model families under a single, configuration-driven interface. The framework supports various model architectures, data preprocessing and augmentations, multiple loss functions and evaluation metrics, which helps with fast iterations and ablation studies. Additionally, the framework supports a range of practical techniques that improve separation quality, such as sliding-window inference with cross-fading, test-time augmentation, model ensembling, and fine-tuning via Low-Rank Adaptation (LORA). Our ablation studies demonstrate improvements of MSS using the above techniques. By consolidating these components into a reproducible, YAML-configurable framework, MSST lowers the barrier to systematic experimentation and enables rapid iteration from idea to verifiable result.
