一种音乐源分离方法,集成时间频率脱和基于mamba的状态空间建模
Chongbin Zhang1, Jiaxiang Zheng2,3, Moxi Cao2,3
1Department, Nanjing University of the Arts, Beijing West Road, Nanjing, 210008, Jiangsu, China. profchongbin@gmail.com.
Scientific reports
|October 16, 2025
概括
这项研究介绍了MSNet,这是一个新的双路径状态空间模型,用于音乐源分离. 它以高效率实现了最先进的结果,在复杂的音频任务中超越现有方法.
科学领域:
- 智能音频处理 智能音频处理
- 机器学习用于音乐信息检索
背景情况:
- 现有的音乐源分离模型在结构适应性和平衡远程依赖与推理效率方面存在局限性.
- 不同质的声音源和纠的时间频率表示对当前的分离技术构成重大挑战.
研究的目的:
- 提出一种新的双路径状态空间建模架构,MSNet,用于先进的音乐源分离.
- 为了解决当前音乐分离模型中的结构限制和效率挑战.
主要方法:
- 开发了一种双路径状态空间架构 (MSNet),其时间和频率路径脱.
- 集成基于Mamba的状态空间单元,用于多维结构分析音频信号.
- 在时间频率建模中增强选择性控制和结构表示.
主要成果:
- 在MUSDB18数据集上,MSNet在多个指标上实现了最先进的性能.
- 对于复杂的声音源,如声乐和鼓声,表现出卓越的稳定性和稳定性.
- 实现了低于0.1的实时系数 (RTF),表明实际适用性.
结论:
- 状态空间模型可用于复杂的音频建模任务,如音乐源分离.
- MSNet引入了一个新的架构范式,在音乐源分离中平衡准确性和效率.
- 拟议的模型为实时音乐源分离应用提供了一个实用的解决方案.
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