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We consider multi-dimensional data, representing a linear mixture of the hidden sources \(\textbf{s}\) . For example each component of \(\textbf{s}\) , \(s_j\) , could represent a sound source at a certain time (music, voice, noise, ...) and each component of the data \(\textbf{x}\) , \(x_k\) represents the sound reception at different spatial location, at the corresponding time. Thus, each receptor mixes the different emission sources. A different vector \(\textbf{x}_i\) , can be collected at each time instant \(t_i\) , from the associated sources \(\textbf{s}_i\) .

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Data Separation: Independent Component Analysis

  • 章节

  • 出版模式：

    开放获取

  • 发表日期：

    2025年

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  • Francisco Chinesta,
  • Elías Cueto,
  • Victor Champaney,
  • Chady Ghnatios,
  • Amine Ammar,
  • Nicolas Hascoët,
  • David González,
  • Icíar Alfaro,
  • Daniele Di Lorenzo,
  • Angelo Pasquale,
  • Dominique Baillargeat

摘要

We consider multi-dimensional data, representing a linear mixture of the hidden sources \(\textbf{s}\) . For example each component of \(\textbf{s}\) , \(s_j\) , could represent a sound source at a certain time (music, voice, noise, ...) and each component of the data \(\textbf{x}\) , \(x_k\) represents the sound reception at different spatial location, at the corresponding time. Thus, each receptor mixes the different emission sources. A different vector \(\textbf{x}_i\) , can be collected at each time instant \(t_i\) , from the associated sources \(\textbf{s}_i\) .