Common techniques and deep learning application prospects for sound event detection

Abstract

Modern techniques for employing deep learning for sound event identification (SED) challenges have improved significantly. In this paper, the author discusses the development of deep learning models for SED tasks in recent years; and the performance advantages and disadvantages shown by using different deep learning methods for the same sound event dataset. This paper also introduces a few techniques effectively increase the precision of sound detection and possible development trends of SED task methods by analyzing the entries in the 2016-2017 Acoustic Scene and Event Detection and Classification (DCASE) Challenge. Through analysis, this paper finds that the accuracy of the deep learning model used for SED to identify target events will continue to improve to be suitable for industrial and life scenarios, so this is still a valuable research.