  While multitask and transfer learning has shown to improve the performance of
neural networks in limited data settings, they require pretraining of the model
on large datasets beforehand. In this paper, we focus on improving the
performance of weakly supervised sound event detection in low data and noisy
settings simultaneously without requiring any pretraining task. To that extent,
we propose a shared encoder architecture with sound event detection as a
primary task and an additional secondary decoder for a self-supervised
auxiliary task. We empirically evaluate the proposed framework for weakly
supervised sound event detection on a remix dataset of the DCASE 2019 task 1
acoustic scene data with DCASE 2018 Task 2 sounds event data under 0, 10 and 20
dB SNR. To ensure we retain the localisation information of multiple sound
events, we propose a two-step attention pooling mechanism that provides a
time-frequency localisation of multiple audio events in the clip. The proposed
framework with two-step attention outperforms existing benchmark models by
22.3%, 12.8%, 5.9% on 0, 10 and 20 dB SNR respectively. We carry out an
ablation study to determine the contribution of the auxiliary task and two-step
attention pooling to the SED performance improvement.
