Bio
Riccardo is a computer science engineer who graduated from the University of Padua. His interest is in audio modeling and synthesis, focusing on nonlinear and time-variant phenomena that present acoustic and electronic instruments/devices. Now is pursuing a Ph.D. at the University of Oslo, where he is addressing nonlinear audio modeling using deep learning techniques. In particular, the research takes different electronic and acoustic musical devices, aiming for low-latency, interactive solutions.
Academic interests
Deep learning models applied to raw audio have rapidly gained relevance in modeling and synthesis scenarios. These architectures have proven beneficial in cases where nonlinear phenomena are present. Nonlinearities often strongly influence the real world and give unique tastes that are usually difficult to replicate. Nonlinear phenomena are usually described by complex equations, computationally expensive, challenging to solve, and sometimes difficult to formalize. Deep learning networks are universal approximators, and this characteristic makes them appealing for data-driven approaches, thus avoiding the effort of specific mathematical formulations. On the other hand, black box modeling can suffer from flexibility and interpretability.
However, recent advances have shown these techniques to overcome these problems. Different approaches have been taken to condition the networks to different dynamic scenarios and to link the model variables to the physical properties of a real-world phenomenon.
Background
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2017 - 2018: Researcher Visitor, Aalto University, Finland
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2018: MSc in Computer Science Engineering, University of Padova, Italy
- 2015: BSc in Information Engineering, University of Padova, Italy