000142803 001__ 142803
000142803 005__ 20190316234645.0
000142803 037__ $$aSTUDENT
000142803 245__ $$aAcoustic Echo Cancellation for Human-Robot Communications
000142803 269__ $$a2004
000142803 260__ $$c2004
000142803 336__ $$aStudent Projects
000142803 520__ $$aThis master thesis presents a new efficient method of acoustic echo cancellation targeted at speech recognition for robots. The proposed algorithm features a new double-talk detector, an enhanced initialization and a new noise estimation method. The DTD algorithm is based on the normalized cross-correlation method, uses noise power estimation to be more robust in noisy environment and reacts more accurately to double-talk. The new initialization method switches between two different DTD algorithms to prevent problems during filter convergence. The simple, yet robust Geigel DTD is used during adaptive filter convergence, whereas the program switches to the newly developed DTD after convergence. Finally, the new noise estimation algorithm relies on the output auto-correlation to correctly estimate the noise. To improve speech recognition performance, center clipping is applied on the output of the echo canceler, to further remove the residual echo. White noise is also added to the output signal, in order to make the signal power more stable, which helps the speech recognition engine. Evaluation of the proposed algorithm has been done on a large set of sequences and results have shown that the new algorithm can increase the word recognition rate by up to 80%.
000142803 6531_ $$aAcoustic Echo Cancellation
000142803 6531_ $$aDouble Talk Detection
000142803 6531_ $$aNoise Estimation
000142803 700__ $$aBerclaz, Jérôme
000142803 720_2 $$aSugiyama, Akihiko$$edir.
000142803 720_2 $$aViste, Harald$$edir.
000142803 720_2 $$aVetterli, Martin$$edir.$$g107537$$0240184
000142803 8564_ $$uhttps://infoscience.epfl.ch/record/142803/files/diploma.pdf$$zn/a$$s604390
000142803 909C0 $$xU10659$$0252087$$pCVLAB
000142803 909C0 $$xU10434$$0252056$$pLCAV
000142803 909CO $$qGLOBAL_SET$$pIC$$ooai:infoscience.tind.io:142803
000142803 937__ $$aCVLAB-STUDENT-2009-002
000142803 973__ $$sPUBLISHED$$aEPFL
000142803 980__ $$bMASTERS$$aSTUDENT