  The types of sound events that occur in a situation are limited, and some
sound events are likely to co-occur; for instance, ``dishes'' and ``glass
jingling.'' In this paper, we propose a technique of sound event detection
utilizing graph Laplacian regularization taking the sound event co-occurrence
into account. In the proposed method, sound event occurrences are represented
as a graph whose nodes indicate the frequency of event occurrence and whose
edges indicate the co-occurrence of sound events. This graph representation is
then utilized for sound event modeling, which is optimized under an objective
function with a regularization term considering the graph structure.
Experimental results obtained using TUT Sound Events 2016 development, 2017
development, and TUT Acoustic Scenes 2016 development indicate that the
proposed method improves the detection performance of sound events by 7.9
percentage points compared to that of the conventional CNN-BiGRU-based method
in terms of the segment-based F1-score. Moreover, the results show that the
proposed method can detect co-occurring sound events more accurately than the
conventional method.
