用联合密度比直接建模时序依赖，提升非平稳信号分类性能
Time-Series Classification with Multivariate Statistical Dependence Features
- 以交叉密度比替代相关性，捕捉信号间真实统计依赖
- 在TI-46语音数据集上超越HMM与脉冲神经网络，准确率更高
- 模型极轻量：少于10层，存储不足5MB，适合边缘部署
本文提出一种新型非平稳时间序列分析框架，将传统基于相关性的统计量替换为对输入与目标信号归一化联合密度的直接估计——交叉密度比（CDR）。该度量不依赖样本顺序，且对模式切换具有鲁棒性。方法基于函数型最大相关算法（FMCA），通过分解CDR的特征谱构建投影空间，提取多尺度特征，并使用轻量级单隐藏层感知机进行分类。在TI-46数字语音语料库上，该方法优于隐马尔可夫模型（HMM）和当前最先进的脉冲神经网络，在层数少于10层、存储占用低于5 MB的条件下实现更高准确率。
原文摘要 · Abstract (English)
In this paper, we propose a novel framework for non-stationary time-series analysis that replaces conventional correlation-based statistics with direct estimation of statistical dependence in the normalized joint density of input and target signals, the cross density ratio (CDR). Unlike windowed correlation estimates, this measure is independent of sample order and robust to regime changes. The method builds on the functional maximal correlation algorithm (FMCA), which constructs a projection space by decomposing the eigenspectrum of the CDR. Multiscale features from this eigenspace are classified using a lightweight single-hidden-layer perceptron. On the TI-46 digit speech corpus, our approach outperforms hidden Markov models (HMMs) and state-of-the-art spiking neural networks, achieving higher accuracy with fewer than 10 layers and a storage footprint under 5 MB.
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