Address book

Contacts

Staff Structures

ANTONIO RODA'

Back to the list

Position

Professore Associato

Structure

Dipartimento di Ingegneria dell'Informazione - DEI

Address

VIA G. GRADENIGO, 6/B - PADOVA

Telephone

0498277581

E-mail

antonio.roda@unipd.it

Antonio Roda' (1971) received a Master's degree in Electronic Engineering from the University of Padova (1996), and a PhD degree in Audiovisual Studies from the University of Udine (2007). In parallel, he conducted musical studies, graduating in Violin at the Conservatory of Padova (1994) and in Composition at the Conservatory of Verona (2009). He is with the Centro di Sonologia Computazionale and his main research interests are in computational models for expressive music performance, affective computing, interactive multimodal learning environments, preservation and restoration of audio documents. He has participated in 11 national and international research projects, among them: 2010-2012 DREAM (Digital Reworking/reappropriation of ElectroAcoustic Music), Culture 2007; 2009-2012 SRSnet: Smart Multi-Resource-Aware Sensor Network, Interreg IV; 2005-2006 Preservation and On-line Fruition of the Audio Documents from the European Archives of Ethnic Music, Culture 2000; 2004-2008 ENACTIVE (Enactive Interfaces), European Network of Excellence; 2000-2003 MEGA-Multisensory Expressive Gesture Applications, IST-1999-20410. He is author and co-author of more than 100 paper on national and international journals and peer reviewed conferences. He is currently Associate Professor at the Department of Information Engineering, University of Padova and he is responsible for the courses of Data and Algorithms, Sound Design and Music Technology, Gender Knowledge and Ethics in Artificial Intelligence.

Notices

http://www.dei.unipd.it/~roda/

Teachings

Thesis proposals

COMPUTATIONAL ANALYSIS OF MUSIC
Study of the evolution of the compositions of great composers of the past, checking in particular the differences between music written in youth and that written in old age (over 60 years). It will be required to perform analysis on sheet music in MIDI format using special libraries in MATLAB (mirmiditoolbox).

COMPUTATIONAL CREATIVITY
Generation of music using Bayesian models or Deep Learning. Basic knowledge of machine learning techniques and the use of Python libraries is required.

TECHNOLOGICALLY AUGMENTED ENVIRONMENTS FOR LEARNING.
Design and development of serious games to facilitate learning. In particular, it is planned to develop games for reactive floor, an interactive floor being tested in some schools in the area.

INTERACTIVE MUSEUM INSTALLATIONS
Within this project, theses may concern:
- the simulation of analog players (gramophone, multitrack magnetophone)
- the simulation of ancient musical instruments (e.g. Pan flute from ancient Egypt)

SERIOUS GAMES
Design and development of digital games (video games) to promote learning and awareness on issues of social equity and sustainability.

RESPONSIBLE ARTIFICIAL INTELLIGENCE
AI-based algorithms, such as chatGPT, can generate discriminatory results against historically disadvantaged groups. This project aims to study and test techniques for analyzing and reducing bias in AI applications.

BIAS IN SPEECH RECOGNITION TECHNOLOGIES
Speech conversational systems (so-called voice assistants, such as Alexa or Siri) may exhibit anomalous behaviors related to the user's gender or language. This project aims to study and experiment with techniques for analyzing and reducing bias in voice assistants.

AUTOMATIC MUSIC PERFORMANCE
The project involves the analysis of human musical performances in order to model the mechanisms of musical expressiveness and implement a virtual pianist capable of simulating the behavior of a human musician. Within this project, various tasks for theses and dissertations can be identified.

AFFECTIVE COMPUTING
Affective computing refers to the ability of a computer to recognize and manifest emotions. Specifically, this project aims to study how music solicits emotions and feelings in listeners in order to recognize them automatically using machine learning algorithms.