Dejan G. Ciric

Dejan G. Ciric is a Full Professor at the Faculty of Electronics in Niš, University of Niš, Department of Telecommunications. He has been a central figure in acoustics and signal processing research, with a strong international presence through collaborations at Aalborg University (Denmark) and IRCAM (France). His work bridges theoretical acoustics and practical applications in industrial and automotive sound analysis.

• PhD, Master’s, and BSc in Telecommunications from Faculty of Electronics in Niš
• Specialization in Acoustics, Aalborg University, Denmark (2002–2003, 2003–2007)

His research focuses on acoustics, room impulse response, sound measurement, and signal processing, particularly using image similarity measures, wavelets, and machine learning for sound analysis. He investigates nonlinear effects in measurements, truncation correction, and air absorption in scale models. His recent work emphasizes industrial product sound, vehicle noise, and DC motor diagnostics through spectrogram analysis and automated systems.

The trend in his publications (2023–2002) shows a consistent focus on improving the accuracy and automation of acoustic measurements, especially in complex environments. He applies mathematical models, signal processing techniques, and cross-domain methods (e.g., treating sound as images) to solve real-world problems in audio engineering and diagnostics.

He has received notable recognition:

• Research in Paris 2013 award (IRCAM, France)
• Young Investigator Award, International Commission for Acoustics (2007)

Dejan Ciric has participated in 5 national and 5 international projects in the past, and is currently involved in 1 national and 1 international project. He has advised students and contributed to academic training in acoustics and telecommunications. His work has resulted in 18 publications in journals with impact factors.

He has been affiliated with research teams at Aalborg University’s Department of Acoustics and the IRCAM Institute in Paris, contributing to projects on audiometric calibration, machine learning for sound directivity control, and advanced acoustic measurement systems.