提出受控注入机制，解决在线独立成分分析中特征丢失问题
Reservoir Subspace Injection for Online ICA under Top-n Whitening
- 设计受控注入策略，确保新特征进入保留特征空间而不干扰原有路径
- 实验显示注入增强使信号保真度下降2.2dB，而受控方案恢复至基准水平
- 适用于需要稳定保留原始信号的非线性混合场景，如语音分离
水库扩展可提升非线性混合下的在线独立成分分析（ICA）性能，但顶-n白化可能丢弃注入特征。本文将此瓶颈形式化为‘水库子空间注入’（RSI）：只有当注入特征进入保留特征空间且不挤占通过方向时才有帮助。通过IER、SSO和ρ_x等诊断工具发现，在顶-n设置下，更强注入导致IER上升，同时挤占通过能量（ρ_x从1.00降至0.77），使SI-SDR最多下降2.2dB。受控的RSI控制器能保持通过方向保留，使平均性能恢复至与1/N基线相差仅0.1dB以内。在保留通过路径的前提下，RE-OICA在非线性混合下比传统在线ICA提升1.7dB，且在测试的超高斯基准上实现了正的SI-SDR_sc（+0.6dB）。
原文摘要 · Abstract (English)
Reservoir expansion can improve online independent component analysis (ICA) under nonlinear mixing, yet top-$n$ whitening may discard injected features. We formalize this bottleneck as \emph{reservoir subspace injection} (RSI): injected features help only if they enter the retained eigenspace without displacing passthrough directions. RSI diagnostics (IER, SSO, $ρ_x$) identify a failure mode in our top-$n$ setting: stronger injection increases IER but crowds out passthrough energy ($ρ_x: 1.00\!\rightarrow\!0.77$), degrading SI-SDR by up to $2.2$\,dB. A guarded RSI controller preserves passthrough retention and recovers mean performance to within $0.1$\,dB of baseline $1/N$ scaling. With passthrough preserved, RE-OICA improves over vanilla online ICA by $+1.7$\,dB under nonlinear mixing and achieves positive SI-SDR$_{\mathrm{sc}}$ on the tested super-Gaussian benchmark ($+0.6$\,dB).
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