  This paper reports the organization and results for the 2018 community-based
Signal Separation Evaluation Campaign (SiSEC 2018). This year's edition was
focused on audio and pursued the effort towards scaling up and making it easier
to prototype audio separation software in an era of machine-learning based
systems. For this purpose, we prepared a new music separation database:
MUSDB18, featuring close to 10h of audio. Additionally, open-source software
was released to automatically load, process and report performance on MUSDB18.
Furthermore, a new official Python version for the BSSEval toolbox was
released, along with reference implementations for three oracle separation
methods: ideal binary mask, ideal ratio mask, and multichannel Wiener filter.
We finally report the results obtained by the participants.
