  Weakly Labelled learning has garnered lot of attention in recent years due to
its potential to scale Sound Event Detection (SED) and is formulated as
Multiple Instance Learning (MIL) problem. This paper proposes a Multi-Task
Learning (MTL) framework for learning from Weakly Labelled Audio data which
encompasses the traditional MIL setup. To show the utility of proposed
framework, we use the input TimeFrequency representation (T-F) reconstruction
as the auxiliary task. We show that the chosen auxiliary task de-noises
internal T-F representation and improves SED performance under noisy
recordings. Our second contribution is introducing two step Attention Pooling
mechanism. By having 2-steps in attention mechanism, the network retains better
T-F level information without compromising SED performance. The visualisation
of first step and second step attention weights helps in localising the
audio-event in T-F domain. For evaluating the proposed framework, we remix the
DCASE 2019 task 1 acoustic scene data with DCASE 2018 Task 2 sounds event data
under 0, 10 and 20 db SNR resulting in a multi-class Weakly labelled SED
problem. The proposed total framework outperforms existing benchmark models
over all SNRs, specifically 22.3 %, 12.8 %, 5.9 % improvement over benchmark
model on 0, 10 and 20 dB SNR respectively. We carry out ablation study to
determine the contribution of each auxiliary task and 2-step Attention Pooling
to the SED performance improvement. The code is publicly released
