Publications

Improving Sound Event Detection in Domestic Environments using Sound Separation

Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE)

Publication date: November 2, 2020

Nicolas Turpault, Scott Wisdom, Hakan Erdogan, John Hershey, Romain Serizel, Eduardo Fonseca, Prem Seetharaman, Justin Salamon

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

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Research Areas:  Adobe Research iconAI & Machine Learning Adobe Research iconAudio