부산대학교 도서관

There are many ways to evaluate rare sound event detection (SED) approaches, e.g., the DCASE 2017 challenge provides a widely employed framework. This paper proposes a rare SED training and test framework, which is reflecting an SED application in a more realistic way. Our setup gets rid of too much prior knowledge on the test data, and assumes additional unknown acoustic events both in training and test data, which in practice have to be identified as background. Taking this into account during training, the robustness in real-world scenarios can be significantly increased, with an average event-based error rate reduction of an absolute 34 percentage points. Further we show and compare the performance of multi-event (polyphonic) classifiers vs. single-event classifiers while outlining the benefits of multi-event training.