Taehwan Kim

Robust double-talk detection in the acoustic echo canceller using normalized error signal power

Double-talk detection errors can result in either large residual echo or distorting the near-end talkerpsilas input speech. Thus accurate double-talk detection is an important problem in the echo canceller to improve the speech quality. In the double-talk detection algorithm with the cross-correlation coefficient, detection errors can occur in the initial convergence period of an adaptive filter or in the noisy environment since the correlation becomes high in those situations. In this paper, we propose a new double-talk detector based on a correlation method that uses the normalized error signal power to reduce the double-talk detection errors. The experimental results show the performance improvement of the acoustic echo canceller as well as the robustness of the proposed double-talk detector.

Simulator for Active Sonar Target Recognition

Many studies in detection and classification of the targets in the underwater environments have been conducted for military purposes, as well as for non-military purpose. Due to the complicated characteristics of underwater acoustic signal reflecting multipath environments and spatio-temporal varying characteristics, active sonar target classification technique has been considered as a difficult technique. And it has a difficult in collecting actual underwater data. In this paper, we implemented the simulator to synthesize the active target signal, to extract feature and to classify the target in the underwater environment. In target signal synthesis, highlight and three-dimensional model are used and multi-aspect based hidden markov model is used for target classification.