Sound and Music Computing conference

Paper on generating training data for deep learning source separation methods...

During July 5th and 9th I have attended the Sound and Music Computing Conference in Helsinki, Finland. I presented a paper on generating training data for deep learning source separation method, particularly in classical music, where you have the score but no multi-track data. The slides can be found online and the code is on the source separation github repository DeepConvSep.

I had the opportunity to visit the acoustics lab at Aalto University and attend a few demos. I’ve been in the anechoic rooms where they recorded the orchestra dataset which I have annotated and used in my paper on score-informed orchestral separation. Interestingly, in one of the demos, Jukka Patynen convolved close-microphone recordings with impulse responses taken from famous concert venues, to demonstrate how different the same recording can sound in varius halls.

There were quite a few interesting posters, from which I mention:

From the presented papers, I was mostly interested in the MIR ones using deep learning, but there were also some other interesting ones:

The proceedings can be found online.

I really liked the keynote from Anssi Klapuri. A few years ago he moved from academia to Yousician, a company that develops apps for music learning. He presented the app and the architecture of their audio engine (they use Unity for graphics but for audio they use their own audio engine). Interestingly, most of the audio part is minimally processed and the audio is rooted as soon as possible to the output. The cross-cancelling seemed quite robust. Also, I really liked the testing techniques: they record the impulse responses of different phones and instruments so they can use this data afterwards for testing.