SED-RECIPES: UNDERSTANDING THE IMPACT OF MASKING STRATEGIES, SCHEDULES AND DECODERS ON SOUND EVENT DETECTION

Recent work has shown that sound event detection (SED) benefits from in-domain, self-supervised pretraining of the decoder. Different works propose different masking strategies, decoders and curricula, but no work has systematically compared the different recipes. In this work, we conduct one of the largest systematic evaluations of different configurations. In addition, we introduce three novel masking strategies that are inspired from the inductive biases of SED and new evaluation metrics that elucidate the impact of polyphonicity and event duration. Our findings show that shorter events remain challenging and that saliency-guided masks can improve per- formance compared to standard, randomized masking.