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Related Experiment Video

Updated: Jun 16, 2026

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

Learning the dynamical system behind sensory data.

Jaehyung Lee¹, Soo-Young Lee

¹Department of Bio and Brain Engineering and Brain Science Research Center, KAIST, Daejeon 305-701, Republic of Korea. jaehyung.lee@kaist.ac.kr

Neural Computation

|January 27, 2010

Summary

This summary is machine-generated.

This study introduces a new method to analyze physical systems using sensory data. It effectively estimates system parameters and extracts features like vocal fundamental frequency (F0) even with noise.

Related Experiment Videos

Last Updated: Jun 16, 2026

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

Area of Science:

Physics
Dynamical Systems
Signal Processing

Background:

Understanding the physics of sensory data is crucial for system analysis.
Existing methods struggle with parameter estimation in complex dynamical systems.

Purpose of the Study:

To develop a novel method for parameter estimation in parameter-controlled dynamical systems.
To extend Takens's delay-embedding theorem for systems with slowly varying parameters.

Main Methods:

Utilizing delay-embedding maps to create an embedding of phase and parameter spaces.
Applying manifold learning to extract a low-dimensional coordinate system.
Employing time-based adjacency relationships to isolate the parameter space.

Main Results:

Bounded reconstruction error for slowly varying parameters.
Successful extraction of fundamental frequency (F0) contours from vowel data.
Robust performance under noise and rapid F0 changes.

Conclusions:

The proposed method offers a robust approach to parameter estimation and feature extraction from sensory data.
This technique outperforms current state-of-the-art algorithms for F0 estimation.