Sound Event Detection Using Graph Laplacian Regularization Based on Event Co-occurrence
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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 introduce a technique of sound event detection utilizing graph Laplacian regularization taking the sound event co-occurrence into account. To consider the co-occurrence of sound events in a sound event detection system, we first represent sound event occurrences as a graph whose nodes indicate the frequency of event occurrence and whose edges indicate the co-occurrence of sound events. We then utilize this graph structure for sound event modeling, which is optimized under an objective function with a regularization term considering the graph structure. Experimental results obtained using TUT Acoustic Scenes 2016 development and 2017 development datasets 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.
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