Graduate Students


Ferda Ofli
Ph.D. Koc University, 2010
Advisor: Murat Tekalp, Yucel Yemez, Engin Erzin

Ferda Ofli. Learning Statistical Music-to-Dance Mappings for Choreography Synthesis. PhD thesis, Koc University, 2010.
“We propose many-to-many statistical mappings from music measures (music segments) to dance figures (dance segments) towards generating plausible music-driven dance choreographies. We assume that dance figures (dance segment boundaries) coincide with music measures (music segment boundaries).”



Elif Bozkurt
M.S. Koc University, 2010
Advisor: Engin Erzin

Elif Bozkurt. A Formant Position based Weighted Spectral Features for Spontaneous Emotion Recognition. Master’s thesis, Koc University, 2010.
“We present formant position based weighted Mel Frequency Cepstral Coefficient (WMFCC) features for the emotion recognition problem and compare performance results with commonly used feature sets. Since, the Line Spectral Frequency (LSF) features are positioned close to each other around formant frequencies, we propose normalized inverse harmonic mean function to weight critical band energies for the extraction of MFCC features.”



Emre Öztürk
M.S. Koc University, 2010
Advisor: Engin Erzin

Emre Ozturk. Driver status identification from driving behavior signals. Master’s thesis, Koc University, 2010.
“Driving behavior signals differ in how and under which conditions the driver use vehicle control units, such as pedals, driving wheel, etc. In this study we investigate how the drivingbehavior signals differ among drivers and among different driving tasks.



Yasemin Demir
Ph.D. student at University of California, Berkeley
M.S. Koc University, 2008
Advisor: Engin Erzin

Yasemin Demir. Music - driven dance synthesis by multimodal dance performance analysis. Master’s
thesis, Koc University, 2008.
“We present a framework for evaluation of audio feature and dance figure correlation for audio - visual analysis and synthesis of dance figures. Dance figures are performed synchronously with the musical rhythm.”



Emre Sargın
MTS at Google
Ph.D. student at University of California, Santa Barbara
M.S. Koc University, 2006
Advisor: Murat Tekalp, Yucel Yemez, Engin Erzin

Emre Sargın. Audio-visual correlation modeling for speaker identification and synthesis. Mas-
ter’s thesis, Koc University, 2006.
“This thesis addresses two major problems of multimodal signal processing using audiovisual correlation modeling: speaker recognition and speaker synthesis. We address the first problem, i.e., the audiovisual speaker recognition problem within an open-set identification framework, where audio (speech) and lip texture (intensity) modalities are fused employing a combination of early and late integration techniques.”



Ulas Bagcı
Ph.D. student at University of Nottingham, UK
M.S. Koc University, 2005
Advisor: Engin Erzin

Ulas Bagcı. Boosting classifiers for automatic music genre classification. Master’s thesis, Koc
University, 2005.
“Music genre classification is an important tool for music information retrieval systems and has been finding important applications in various media platforms. Two important problems of the automatic music genre classification are feature extraction and classifier design.”



Ertan Cetingul
Ph.D. student at Johns Hopkins University, Baltimore
M.S. Koc University, 2005
Advisor: Murat Tekalp, Engin Erzin, Yucel Yemez

Ertan Cetingul. Discrimination analysis of lip motion features for multimodal speaker identification and speech-reading. Master’s thesis, Koc University, 2005.
“In this thesis a new multimodal speaker/speech recognition system that integrates audio, lip texture, lip geometry, and lip motion modalities is presented. There have been several studies that jointly use audio, lip intensity and/or lip geometry information for speaker identification and speech recognition applications.”



Alper Kanak
TUBITAK-UEKAE
M.S. Koc University, 2004
Advisor: Murat Tekalp, Engin Erzin, Yucel Yemez

Alper Kanak. Multimodal speaker identification with audio-video processing. Master’s thesis, Koc University, 2004.
“In this these we present a multimodal text=dependent speaker identification system. The objective is to improve the recognition performance over conventional unimodal or bimodal schemes.”






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