scholarly journals The impact of non-target events in synthetic soundscapes for sound event detection

2021 ◽  
Author(s):  
Francesca Ronchini ◽  
Romain Serizel ◽  
Nicolas Turpault ◽  
Samuele Cornell

Detection and Classification Acoustic Scene and Events Challenge 2021 Task 4 uses a heterogeneous dataset that includes both recorded and synthetic soundscapes. Until recently only target sound events were considered when synthesizing the soundscapes. However, recorded soundscapes often contain a substantial amount of non-target events that may affect the performance. In this paper, we focus on the impact of these non-target events in the synthetic soundscapes. Firstly, we investigate to what extent using non-target events alternatively during the training or validation phase (or none of them) helps the system to correctly detect target events. Secondly, we analyze to what extend adjusting the signal-to-noise ratio between target and non-target events at training improves the sound event detection performance. The results show that using both target and non-target events for only one of the phases (validation or training) helps the system to properly detect sound events, outperforming the baseline (which uses non-target events in both phases).The paper also reports the results of a preliminary study on evaluating the system on clips that contain only non-target events. This opens questions for future work on non-target subset and acoustic similarity between target and non-target events which might confuse the system.

2020 ◽  
Author(s):  
Xu Zheng ◽  
Yan Song ◽  
Jie Yan ◽  
Li-Rong Dai ◽  
Ian McLoughlin ◽  
...  

Author(s):  
Gianmarco Cerutti ◽  
Rahul Prasad ◽  
Alessio Brutti ◽  
Elisabetta Farella

2020 ◽  
Vol 4 (3) ◽  
pp. 20 ◽  
Author(s):  
Giuseppe Ciaburro

Parking is a crucial element in urban mobility management. The availability of parking areas makes it easier to use a service, determining its success. Proper parking management allows economic operators located nearby to increase their business revenue. Underground parking areas during off-peak hours are uncrowded places, where user safety is guaranteed by company overseers. Due to the large size, ensuring adequate surveillance would require many operators to increase the costs of parking fees. To reduce costs, video surveillance systems are used, in which an operator monitors many areas. However, some activities are beyond the control of this technology. In this work, a procedure to identify sound events in an underground garage is developed. The aim of the work is to detect sounds identifying dangerous situations and to activate an automatic alert that draws the attention of surveillance in that area. To do this, the sounds of a parking sector were detected with the use of sound sensors. These sounds were analyzed by a sound detector based on convolutional neural networks. The procedure returned high accuracy in identifying a car crash in an underground parking area.


2020 ◽  
Author(s):  
Liujun zhang ◽  
Liyan Luo ◽  
Mei Wang ◽  
Xiyu Song ◽  
Shuting Guo ◽  
...  

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