数学科学学院

计算与应用讨论班——Topology-enhanced machine learning for consonant recognition

来源:数学科学学院 发布时间:2025-04-16   118

报告人:朱一飞(南方科技大学)


时间:2025年4月16日,15:30-16:30


地点:海纳苑2幢203室


摘要:In artificial-intelligence-aided signal processing, existing deep learning models often exhibit a black-box structure.  The integration of topological methods serves the dual purpose of extracting structural information from time-dependent data as well as making models more interpretable.  In this talk, I will give an overview of joint work with Pingyao Feng, Qingrui Qu, et al., in which we propose a transparent methodology, TopCap, to capture the most salient topological features inherent in time series for basic machine learning.  Rooted in high-dimensional ambient spaces, TopCap is capable of capturing features rarely detected in datasets with low dimensionality.  Applying time-delay embedding and persistent homology, we obtain descriptors that encapsulate information such as the vibrations of a time series.  This information is then vectorized and fed into multiple machine learning algorithms such as k-nearest neighbors and support vector machines.  Notably, in classifying voiced and voiceless consonants, TopCap achieves an accuracy exceeding 96%, consistently standing in comparison with state-of-the-art neural networks.  Moreover, we integrate TopCap features into those neural networks, beyond direct biomimetic spectral engineering currently adopted in the field.  This has made a network more interpretable with better performance in terms of accuracy, steadiness, convergence of loss function, and robustness against noise.


联系人:蔺宏伟hwlin@zju.edu.cn


Copyright © 2023 浙江大学数学科学学院    版权所有

    浙ICP备05074421号

技术支持: 寸草心科技     管理登录

    您是第 1000 位访问者