  Traditional binary hard labels for sound event detection (SED) lack details
about the complexity and variability of sound event distributions. Recently, a
novel annotation workflow is proposed to generate fine-grained non-binary soft
labels, resulting in a new real-life dataset named MAESTRO Real for SED. In
this paper, we first propose an interactive dual-conformer (IDC) module, in
which a cross-interaction mechanism is applied to effectively exploit the
information from soft labels. In addition, a novel scene-inspired mask (SIM)
based on soft labels is incorporated for more precise SED predictions. The SIM
is initially generated through a statistical approach, referred as SIM-V1.
However, the fixed artificial mask may mismatch the SED model, resulting in
limited effectiveness. Therefore, we further propose SIM-V2, which employs a
word embedding model for adaptive SIM estimation. Experimental results show
that the proposed IDC module can effectively utilize the information from soft
labels, and the integration of SIM-V1 can further improve the accuracy. In
addition, the impact of different word embedding dimensions on SIM-V2 is
explored, and the results show that the appropriate dimension can enable SIM-V2
achieve superior performance than SIM-V1. In DCASE 2023 Challenge Task4B, the
proposed system achieved the top ranking performance on the evaluation dataset
of MAESTRO Real.
