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About

Toma Telembici is a researcher advancing the auditory intelligence of service robots, with a focus on integrating them into everyday human environments. Their work centers on enhancing the audio capabilities of the TIAGo service robot, particularly through sound classification and emotion recognition. Telembici’s major contributions include the development and expansion of a specialized audio database for service robots, growing from 1,380 to 3,300 acoustic signals, and the creation of the first emotional audio database in Romanian for robotic applications. By employing Mel Frequency Cepstral Coefficients (MFCCs) and machine learning techniques like 5-nearest neighbors, they achieved a remarkable 99.27% correct classification rate in isolated audio event detection. Their most cited paper, “Audio Database for TIAGo Service Robot” (2021), has garnered 7 citations, reflecting its foundational role in the field. More recently, Telembici has explored deep learning-based sound classification algorithms, demonstrating the potential for service robots to better interpret complex acoustic environments. This work is pivotal for making robots more responsive and intuitive in real-world settings, bridging the gap between robotic systems and natural human interaction.