Alessandro Ragano is a Ph.D. student in the School of Computer Science at University College Dublin, a member of the Insight Centre for Data Analytics, a visiting researcher at the Centre for Digital Music at Queen Mary University of London, and an enrichment student at The Alan Turing Institute. He is supervised by Dr. Andrew Hines and Dr. Emmanouil Benetos. He holds a B.Sc. in Computer Engineering from the Università degli Studi di Salerno and an M.Sc. in Computer Science and Engineering with a specialization in Sound and Music Computing from the Politecnico di Milano. During his master's degree, he worked as a research assistant in the Audio and Multimedia Division at the Fraunhofer IIS.
His current interest lies in machine learning applied to audio quality assessment. The topics of interest include how sound recordings aside from the content factor, affect people’s listening experience and what factors are involved in the sound quality control. He is interested in understanding how machines can be used to assess sound quality in order to help to preserve audio archives. His research interest leads him to the Turing where one of its core research areas focuses on data science applied in digital humanities.
His research investigates how to develop audio quality metrics that measure end-users’ perceived quality of audio signals with a focus on objective reference-free models that can evaluate the quality of experience (QoE) of digitised and restored audio archives. He is interested in perceived quality of historical audio material that is subject to digitisation and restoration which is typically evaluated by individual judgements or using inappropriate objective quality models. His goal is to develop new quality models which can be used for assessing quality in audio archive scenarios.
He is interested in deep learning models for quality prediction, especially in the techniques that can be used to compensate for the absence of the reference signal, a typical characteristic of audio archives. His interests concern musical signals and audio enhancement/restoration techniques applied to musical signals.
