There is a common observation that audio event classification is easier to deal with than detection. So far, this observation has been accepted as a fact and we lack of a careful analysis. In this paper, we reason the rationale behind this fact and, more importantly, leverage them to benefit the audio event detection task. We present an improved detection pipeline in which a verification step is appended to augment a detection system. This step employs a high-quality event classifier to postprocess the benign event hypotheses outputted by the detection system and reject false alarms. To demonstrate the effectiveness of the proposed pipeline, we implement and pair up different event detectors based on the most common detection schemes and various event classifiers, ranging from the standard bag-of-words model to the state-of-the-art bank-of-regressors one. Experimental results on the ITC-Irst dataset show significant improvements to detection performance. More importantly, these improvements are consistent for all detector-classifier combinations.
Original languageEnglish
Title of host publication2017 25th European Signal Processing Conference (EUSIPCO)
Number of pages5
Publication date01.08.2017
ISBN (Print)978-1-5386-0751-0
ISBN (Electronic)978-0-9928626-7-1
Publication statusPublished - 01.08.2017
Event25th European Signal Processing Conference - Kos International Convention Center , Kos, Greece
Duration: 28.08.201702.09.2017
Conference number: 131844


Dive into the research topics of 'What makes audio event detection harder than classification?'. Together they form a unique fingerprint.

Cite this