  Target sound detection (TSD) aims to detect the target sound from mixture
audio given the reference information. Previous works have shown that TSD
models can be trained on fully-annotated (frame-level label) or
weakly-annotated (clip-level label) data. However, there are some clear
evidences show that the performance of the model trained on weakly-annotated
data is worse than that trained on fully-annotated data. To fill this gap, we
provide a mixed supervision perspective, in which learning novel categories
(target domain) using weak annotations with the help of full annotations of
existing base categories (source domain). To realize this, a mixed supervised
learning framework is proposed, which contains two mutually-helping student
models (\textit{f\_student} and \textit{w\_student}) that learn from
fully-annotated and weakly-annotated data, respectively. The motivation is that
\textit{f\_student} learned from fully-annotated data has a better ability to
capture detailed information than \textit{w\_student}. Thus, we first let
\textit{f\_student} guide \textit{w\_student} to learn the ability to capture
details, so \textit{w\_student} can perform better in the target domain. Then
we let \textit{w\_student} guide \textit{f\_student} to fine-tune on the target
domain. The process can be repeated several times so that the two students
perform very well in the target domain. To evaluate our method, we built three
TSD datasets based on UrbanSound and Audioset. Experimental results show that
our methods offer about 8\% improvement in event-based F-score as compared with
a recent baseline.
