ISMIR 2017 highlights

This has been my first ISMIR ever, and I am thrilled for being part of this amazing community. It was fun to put faces (and hight, and weight) to these names I respect so much!

All awarded papers were amazing, and these are definitely in my list of highlights:
  • Choi et al. – every time I re-read this paper I am more impressed about the efforts they put in assessing the generalization capabilities of deep learning models. This work defines a high evaluation standard for those working in deep auto-tagging models!
  • Bittner et al. proposes a fully-convolutional model for tracking f0 contours in polyphonic music. The article has a brilliant introduction drawing parallelisms between their proposed fully-convolutional architecture and previous traditional models – making clear that it is worth building bridges between deep learning works and previous signal processing literature.
  • Oramas et al. – deep learning enables to easily combine information from many sources, such as: audio, text or images. They do so by combining representations extracted from audio-spectrograms, word-embeddings and ImageNet-based features. Moreover, they released a new dataset: MuMu, with 147,295 songs belonging to 31,471 albums.
  • Jansson et al.‘s work proposes a U-net model for singing voice separation. It seems that adding connections between layers at the same hierarchical level in the encoder and decoder for reconstructing masked audio signals is a good idea since several papers already reported good results using this setup.

But there were many other inspiring papers.. Continue reading