ANALYSIS OF THE SOUND EVENT DETECTION METHODS AND SYSTEMS

Abstract

Detection and recognition of loud sounds and characteristic noises can significantly increase the level of safety and ensure timely response to various emergency situations. Audio event detection is the first step in recognizing audio signals in a continuous audio input stream. This article presents a number of problems that are associated with the development of sound event detection systems, such as the deviation for each environment and each sound category, overlapping audio events, unreliable training data, etc. Both methods for detecting monophonic impulsive audio event and polyphonic sound event detection methods which are used in the state-of-the-art sound event detection systems are presented. Such systems are presented in Detection and Classification of Acoustic Scenes and Events (DCASE) challenges and workshops, which take place every year. Beside a majority of works focusing on the improving overall performance in terms of accuracy many other aspects have also been studied. Several systems presented at DCASE 2021 task 4 were considered, and based on their analysis, there was a conclusion about possible future for sound event detection systems. Also the actual directions in the development of modern audio analytics systems are presented, including the study and use of various architectures of neural networks, the use of several data augmentation techniques, such as universal sound separation, etc.