  Most attention-based methods only concentrate along the time axis, which is
insufficient for Acoustic Event Detection (AED). Meanwhile, previous methods
for AED rarely considered that target events possess distinct temporal and
frequential scales. In this work, we propose a Multi-Scale Time-Frequency
Attention (MTFA) module for AED. MTFA gathers information at multiple
resolutions to generate a time-frequency attention mask which tells the model
where to focus along both time and frequency axis. With MTFA, the model could
capture the characteristics of target events with different scales. We
demonstrate the proposed method on Task 2 of Detection and Classification of
Acoustic Scenes and Events (DCASE) 2017 Challenge. Our method achieves
competitive results on both development dataset and evaluation dataset.
