  The challenges of polyphonic sound event detection (PSED) stem from the
detection of multiple overlapping events in a time series. Recent efforts
exploit Deep Neural Networks (DNNs) on Time-Frequency Representations (TFRs) of
audio clips as model inputs to mitigate such issues. However, existing
solutions often rely on a single type of TFR, which causes under-utilization of
input features. To this end, we propose a novel PSED framework, which
incorporates Multi-Type-Multi-Scale TFRs. Our key insight is that: TFRs, which
are of different types or in different scales, can reveal acoustics patterns in
a complementary manner, so that the overlapped events can be best extracted by
combining different TFRs. Moreover, our framework design applies a novel
approach, to adaptively fuse different models and TFRs symbiotically. Hence,
the overall performance can be significantly improved. We quantitatively
examine the benefits of our framework by using Capsule Neural Networks, a
state-of-the-art approach for PSED. The experimental results show that our
method achieves a reduction of 7\% in error rate compared with the
state-of-the-art solutions on the TUT-SED 2016 dataset.
