Improving Sound Event Detection in Domestic Environments using Sound Separation

By Nicolas Turpault, Scott Wisdom, Hakan Erdogan, John R. Hershey, Romain Serizel, Eduardo Fonseca, Prem Seetharaman, Justin Salamon. 2020

54 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Signal Processing

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Citations in the corpus

FSD50K: An Open Dataset of Human-Labeled Sound Events (2020, neutral): “2 [33] and 4 [28], and DCASE 2020 Task 4 [100].”
What’s all the Fuss about Free Universal Sound Separation Data? (2020, result): “Our initial reported results on FUSS [31] used this overly optimistic measure.”
Self-Supervised Learning from Automatically Separated Sound Scenes (2021, methodology): “Sound separation has been studied as preprocessing to improve supervised sound event detection [21, 22].”