  Human beings can perceive a target sound type from a multi-source mixture
signal by the selective auditory attention, however, such functionality was
hardly ever explored in machine hearing. This paper addresses the target sound
detection (TSD) task, which aims to detect the target sound signal from a
mixture audio when a target sound's reference audio is given. We present a
novel target sound detection network (TSDNet) which consists of two main parts:
A conditional network which aims at generating a sound-discriminative
conditional embedding vector representing the target sound, and a detection
network which takes both the mixture audio and the conditional embedding vector
as inputs and produces the detection result of the target sound. These two
networks can be jointly optimized with a multi-task learning approach to
further improve the performance. In addition, we study both strong-supervised
and weakly-supervised strategies to train TSDNet and propose a data
augmentation method by mixing two samples. To facilitate this research, we
build a target sound detection dataset (\textit{i.e.} URBAN-TSD) based on
URBAN-SED and UrbanSound8K datasets, and experimental results indicate our
method could get the segment-based F scores of 76.3$\%$ and 56.8$\%$ on the
strongly-labelled and weakly-labelled data respectively.

Sound;Audio and Speech Processing