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This blog post is about the ICASSP 2020 paper Meta-Learning Extractors for Music Source Separation.1 I will summarise and comment on the main ideas.
Samuel, D., Ganeshan, A., & Naradowsky, J. “Meta-learning Extractors for Music Source Separation.” In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020. https://arxiv.org/abs/2002.07016 ↩
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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.
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I wrote this post together with my colleague Giorgia Cantisani. It has also been published on the MIP-Frontiers blog
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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.