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Interview with Romain Hennequin at ISMIR 2019

6 minute read


Romain Hennequin is a lead research scientist at Deezer working on music information retrieval. Prior to that, he was a researcher at Audionamix for three years. In 2011 he obtained a PhD from Télécom ParisTech for his work on musical spectrogram decomposition methods. I met him during the ISMIR conference 2019 in Delft and got the opportunity to talk with him about audio source separation.

Summer School DeepLearn 2019

10 minute read


At the end of July, I attended the 3rd International Summer School on Deep Learning, DeepLearn 2019 in Warsaw, Poland. It offered a full week of exciting talks, courses, networking and pirogi eating. In total there were 22 courses to choose from, which covered several aspects of deep learning research ranging from introductory to advanced levels. All speakers were well-known experts in their respective areas of research. Beyond that, three keynote talks and many presentations by participants about their work gave further insight into the extremely wide range of research fields where deep learning is applied. In this post, I will address some lessons I learnt at the summer school and briefly summarize some of my favorite courses.


Weakly Informed Audio Source Separation

Kilian Schulze-Forster, Clement Doire, Gaël Richard, Roland Badeau
IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 2019

Deep learning based singing voice separation exploiting non-aligned side information with attention

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