My favourite ISMIR 2019 papers!6 min read

This year’s ISMIR was in Delft, the Netherlands. It seems like the community is starting to realise that the technologies developed by the ISMIR community can have an impact to our society – because they are starting to work! During the first days of the conference, many conversations were focusing on exploring ways to positively impact society. On the other side, technology-wise, we have seen (i) many people studying how to use musical domain knowledge to disentangle/structure/learn useful neural representations for many music applications, and (ii) many attention-based neural architectures.

This year I was in ISMIR to present..

To start, I would like to highlight that the proceedings are in a very nice format – that makes them accessible to everyone. The organisers compiled the papers in a website, together with a short summary of the papers and a link to the code. I encourage everyone to take a look into the proceedings website, because it is a very nice way to navigate the scientific program of the conference.

[Disclaimer: the summaries I attach below, are copy-pasted from the proceedings]

This is my top-10 list of ISMIR papers, with twelve entries:

It seems like the ISMIR community generously embraced attention-based models:

Interestingly, cover-song identification is a hot topic again:

  • Cover Detection Using Dominant Melody Embeddings
    • ISMIR summary: “We propose a cover detection method based on vector embedding extraction out of audio dominant melody. This architecture improves state-of-the-art accuracy on large datasets, and scales to query collections of thousands of tracks in a few seconds.”
  • Da-TACOS: A Dataset for Cover Song Identification and Understanding
    • ISMIR summary: “This work aims to understand the links among cover songs with computational approaches and to improve reproducibility of Cover Song Identification task by providing a benchmark dataset and frameworks for comparative algorithm evaluation.”

Finally, some interesting works on music source separation that do not assume a model per source: