Alain Riou

Assistant Researcher - PhD Student


Over the last few years, computers and algorithms have been increasingly used for several tasks, including art creation. Driven by a strong interest in music, my objective at Sony CSL is to develop innovative machine learning techniques to design new tools and software to stimulate artists’ creativity and helping them composing original music. Nowadays, through the use of synthesisers, DAWs or VSTs,  the music creation process is more and more digitalised. However, most tools interact with the timbre of the sound while letting artists arrange notes manually. Therefore the purpose of my PhD is to include the machine already in the writing process, by suggesting original but relevant melodies or arrangements, based on what the artist already composed. My objective is therefore twofold: on the one hand, the tools I implement must use the most advanced deep learning techniques in the research field of music generation to produce convincing melodies, but on the other hand they must remain lightweight and easy to use by people that do not have technical skills in computer science. Dealing with these two constraints is vital for music composers to be able and willing to use these tools, and thus see the machine as an ally and not a competitor with regard to their art creation process.


Impact analysis of COVID-19 responses on energy grid dynamics in Europe

Annette Werth , Pietro Gravino , Giulio Prevedello |

Applied Energy, 281, 2021. pp.116045.

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COVID-19, Electricity consumption, Electricity generation, Governmental restrictions, Renewable share, Electricity grid”,

abstract = “When COVID-19 pandemic spread in Europe, governments imposed unprecedented confinement measures with mostly unknown repercussions on contemporary societies. In some cases, a considerable drop in energy consumption was observed, anticipating a scenario of sizable low-cost energy generation, from renewable sources, expected only for years later. In this paper, the impact of governmental restrictions on electrical load, generation and transmission was investigated in 16 European countries. Using the indices provided by the Oxford COVID-19 Government Response Tracker, precise restriction types were found to correlate with the load drop. Then the European grid was analysed to assess how the load drop was balanced by the change in generation and transmission patterns. The same restriction period from 2020 was compared to previous years, accounting for yearly variability with ad hoc statistical technique. As a result, generation was found to be heavily impacted in most countries with significant load drop. Overall, generation from nuclear, and fossil coal and gas sources was reduced, in favour of renewables and, in some countries, fossil gas. Moreover, intermittent renewables generation increased in most countries without indicating an exceptional amount of curtailments. Finally, the European grid helped balance those changes with an increase in both energy exports and imports, with some net exporting countries becoming net importers, notably Germany, and vice versa. Together, these findings show the far reaching implications of the COVID-19 crisis, and contribute to the understanding and planning of higher renewables share scenarios, which will become more prevalent in the battle against climate change.