Process / pipelineBlind Source Separation
独立向量分析
独立向量分析（IVA）是独立成分分析（ICA）的多变量扩展，它在保持每个数据集内部依赖性的同时，联合分离多个数据集。IVA由Lee、Lewicki和Sejnowski在21世纪初开发，用于多通道音频、脑成像和信号处理中的盲源分离。它同时利用了源之间的独立性以及频率带或时频结构内部的相关性。
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来源
- Lee, T. W., Lewicki, M. S., & Sejnowski, T. J. (2007). Independent Component Analysis for Source Localization in Biomedical Signals. In Proc. IEEE Int. Conf. Acoust. Speech Signal Process., pp. 97-100. link ↗
- Kim, T., Attias, H. T., Lee, S. Y., & Lee, T. W. (2006). Blind source separation exploiting higher-order frequency dependencies. IEEE Transactions on Audio, Speech, and Language Processing, 15(1), 70-79. DOI: 10.1109/tasl.2006.872618 ↗
- Comon, P., Jutten, C., & Herault, J. (2010). Blind Separation of Sources, Part II: Problems Statement. IEEE Transactions on Signal Processing, 59(11), 4711-4721. link ↗
如何引用本页
ScholarGate. (2026, June 3). Independent Vector Analysis for Multivariate Blind Source Separation. ScholarGate. https://scholargate.app/zh/applied-physics/independent-vector-analysis
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