  We propose a simple recurrent model for detecting rare sound events, when the
time boundaries of events are available for training. Our model optimizes the
combination of an utterance-level loss, which classifies whether an event
occurs in an utterance, and a frame-level loss, which classifies whether each
frame corresponds to the event when it does occur. The two losses make use of a
shared vectorial representation the event, and are connected by an attention
mechanism. We demonstrate our model on Task 2 of the DCASE 2017 challenge, and
achieve competitive performance.
