Machine Learning Group

ICA Algorithms for High-Dimensional Data

• Supported by : POSTECH Research Fund

Summary

We will develop a new optimization technique on Riemannian manifold and apply it to ICA. In addition, we exploit a special structure of the equation for memory-efficiency in the algorithm, so that the algorithm is useful, especially for the case of high-dimensional data.

Continuous Speech Recognition System Based on Selective Attention

• Supported by : Ministry of Science and Technology

Summary

To develop the preprocessing method for artificial speech recognition system, that is, cocktail party processor. This method can localize the source sounds and have a cocktail party effect by selective attention. We will develop the preprocessing method for artificial speech recognition system, that is, cocktail party processor. This method can localize the source sounds and have a cocktail party effect by selective attention.

CA Algorithm and its VLSI Implementation for Efficient Signal Separation

• Supported by : Ministry of Science and Technology

Summary

To develope the application of BSS/ICA algorithm into the real data, for example, EEG analysis or Cocktail Party problem and DNA microarray data analysis.

EEG Signal Analysis using ICA

• Supported by : ETRI (Electronics and Telecommunications Research Institute)

Summary

To apply the ICA algorithm into EEG data analysis as preprocessor or feature extractor. And then we designed the whole system for EEG data analysis (Preprocessing, Feature Extractor, Classifier).

Technical development in blind source separation for manufacturing audio-broadcast contents based on the object

• Supported by : ETRI (Electronics and Telecommunications Research Institute)

Summary

Blind source Separation is a fundamental problem to separate mixing signals such as sounds of speech and musical instruments into original sources. In the real situations such as radio communication, telemetry, radar, sonar, and speech context, sources are often nonstationary or quasi-cyclostationary, so practically observed signals are usually convolutive mixtures. Through this project, we want to develop a new efficient algorithm for the BSS problem using relative trust-region method in the case of Joint diagonalization for Convolutive Mixture. Moreover, we will suggest a new algorithm for solving a permutation ambiguity of a frequency domain approach in the case of convolutive mixture. because a permutation ambiguity gives a large effect on the performance of convolutive blind source separation. This approach will produce new algorithms that have faster convergence rate and better performance than L. Parras algorithm for convolutive blind source separation. Those algorithms will be used to manufacture audio-broadcast contents based on the object.

Design of high density array EEG experiments and development of on-line EEG analysis software for brain computer interface

• Period : 2004.05.~ 2007.04.
• Supported by : KOSEF
• Associated Member : Hyekyoung Lee

Summary

Brain computer interface provides a new communication channel between human brain and computer. The goal of our project is the development of software for cursor movement to up, down, left or right by detecting the human intention. Human intention can be obtained by measuring and analyzing EEG. We design the stimuli to measure valid EEG and research its analysis algorithm based on machine learning technique.

Context-awareness for ubiquitous services

• Period : 2005.09. ~ 2009.08.
• Supported by : Ministry of Information and Communication Associated
• Associated Member : KyeHyeon Kim, Yongdeok Kim

Summary

The project is a service part of the next generation mobile technologies researched by CMEST(Center for Mobile Embedded Software Technology, http://cmest.postech.ac.kr). In this year (2006.10. ~ 2007.09), we study on semi-supervised sensor fusion methods to automatically integrate primitive information from sensors into meaningful contexts. Semi-supervised learning obtains a mapping from high dimensional space onto user-specified coordinates, by (1) recognizing manifolds from unlabeled data points and (2) unfolding them to desired coordinates using few labeled points.

Source Separation and Restoration Based on Human Auditory Models

• Period : 2004.07. ~ 2008.03.
• Supported by : Ministry of Commerce, Industry and Energy
• Associated Member : SunHo Park, Jiho Yoo

Summary

Human ear works well even in the case of noisy and mixed signals. So, in this project we will model the human brain system mechanism to represent and process the auditory signal which leads to the generative model and representation model for sound sources. Based on these models, we will develop new algorithms for source separation and restoration on mixed speech data with noise and reverberation environment. This approach is expected to be helpful in the other brain research and the results could be applied in the electronics industry related to sound facilities.

Identifying driver emotion based on physiological signals and developing adaptive driver interfaces

• Period : 2006.04. ~ 2008.03.
• Supported by : the Korea Science and Engineering Foundation (KOSEF) grant funded by the Korea government (MOST)
• Associated Member : Hyekyoung Lee, Sunho Lee, Yongdeok Kim

This work is a cooperative research with Professor Sung H. Han of dept. of Industrial and Management Engineering, POSTECH and Professor Gerard Jounghyun Kim of dept. of Computer Science and Engineering, Korea University.

Summary

• The main objectives of this research are three-fold (1) to computationally model the driver's emotional state using physiological signals and other sensor data, (2) to develop an emotion adaptive driving interface and vehicle control for safe driving, and (3) to develop an emotionally compelling virtual driving simulator and validate the developed model, techniques, and the overall safety effect.
• Modeling and recognizing the human's emotional state is one of the important interdisciplinary research topics drawing interests from communities of AI, HCI, cognitive science, and signal processing in the international arena. One of the natural applications is in the emotion adaptive interface which can have a huge implication in safety critical interactive systems. As an emerging area with only scattered research results, a comprehensive and consolidated research is needed. An interdisciplinary team with expertise in physiological signals, human computer interaction, artificial intelligence and signal processing, and virtual training have come together to conduct the following research in three years: (1) to computationally model driver's emotional state using physiological signals and other sensor data, (2) to develop an emotion adaptive driving interface and vehicle control for safe driving, and (3) to develop an emotionally compelling virtual driving simulator and validate the model, techniques, and the safety effect.