  Few-shot learning systems for sound event recognition have gained interests
since they require only a few examples to adapt to new target classes without
fine-tuning. However, such systems have only been applied to chunks of sounds
for classification or verification. In this paper, we aim to achieve few-shot
detection of rare sound events, from query sequence that contain not only the
target events but also the other events and background noise. Therefore, it is
required to prevent false positive reactions to both the other events and
background noise. We propose metric learning with background noise class for
the few-shot detection. The contribution is to present the explicit inclusion
of background noise as an independent class, a suitable loss function that
emphasizes this additional class, and a corresponding sampling strategy that
assists training. It provides a feature space where the event classes and the
background noise class are sufficiently separated. Evaluations on few-shot
detection tasks, using DCASE 2017 task2 and ESC-50, show that our proposed
method outperforms metric learning without considering the background noise
class. The few-shot detection performance is also comparable to that of the
DCASE 2017 task2 baseline system, which requires huge amount of annotated audio
data.
