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Performing sound event detection on real-world recordings often impliesdealing with overlapping target sound events and non-target sounds, alsoreferred to as interference or noise. Until now these problems were mainlytackled at the classifier level. We propose to use sound separation as apre-processing for sound event detection. In this paper we start from a soundseparation model trained on the Free Universal Sound Separation dataset and theDCASE 2020 task 4 sound event detection baseline. We explore different methodsto combine separated sound sources and the original mixture within the soundevent detection. Furthermore, we investigate the impact of adapting the soundseparation model to the sound event detection data on both the sound separationand the sound event detection.

Improving Sound Event Detection In Domestic Environments Using Sound Separation