# An Analysis of Sound Event Detection under Acoustic Degradation Using Multi-Resolution Systems
## 3 Results and Discussion
### Discussion  
The proposed adverse settings have a negative impact on the performance of both single-resolution and multiresolution Sound Event Detection systems. Among frequency filtering, dynamic range compression, and clipping, the most critical scenario is high-pass filtering, especially with \(f_{c}\) values of 1000 Hz and above. This suggests that the information of low frequencies is essential for this task, especially when considering categories like Blender, Speech, Running water, or Vacuum cleaner. On the other hand, low-pass filtering is the most adverse condition for the class Frying, implying that high frequencies are particularly relevant for this event. In the case of an increased proportion of event overlap, SED performance is noticeably decreased with respect to the original data. This behavior is explained by the low level of event overlap found in the strongly labeled training data, more similar to that of the Public evaluation set than the studied Overlapped set. Nevertheless, the multiresolution systems offer better results in most categories. To compare the levels of overlap in different datasets, we compute a value, \(\rho\), for each event in the ground truth annotations. We define \(\rho\) as the ratio between the overlapped time of an event (i.e., the duration of the intervals of time in which such event coincides with one or more other events) and the total length of the event:  
\[\rho=\frac{t_{overlap}}{t_{event}}. \tag{2}\]  
Therefore, the value of \(\rho\) can range from 0, if the event does not overlap with any other target event, to 1, if the entirety of the event sounds at the same time than other events. Nontarget events are not included in the ground truth annotations, and thus are not considered for this measure. The global overlap level \(\rho\) of a dataset is computed as the average \(\rho\) across all the events in its ground truth annotations. Whereas the Synthetic training set has an overlap level of \(\rho=0.24\) and the Validation set has \(\rho=0.26\), the Public Evaluation set shows a slightly superior, yet similar, overlap level of \(\rho=0.31\). In contrast, in the Overlapped set this value rises to \(\rho=0.80\). The global and class-wise values of \(\rho\) in the Public Evaluation and Overlapped sets are presented in Table 4.  
Figure 7: \(F_{1}\) scores of single-resolution systems (\(F_{++},F_{+},BS,T_{+},T_{++}\)) and multiresolution systems (_3 res_, _5 res_) over Overlapped set. “Macro F1” shows average score over 10 target categories. Best viewed in color. Although we already stated the problem of event overlap in SED, as well as analyzed its impact in single-resolution and multiresolution systems [39], the scarcity of highly overlapped events in the Validation data was a limiting factor for the study of the behavior of our systems in such a situation. Nonetheless, in this work the generation of artificially overlapped data helped to overcome that limitation. Despite not being a very frequent problem in current evaluation data, event overlap could cause a severe degradation of performance in certain applications in which events tend to overlap, such as event detection in crowded areas. To combat the negative effects of event overlap, some existing strategies could be used. On the one hand, Mixup training [40] is a form of data augmentation that provides the deep learning algorithms with linear combinations of training examples, thus helping them to learn to confront this type of data. On the other hand, source separation algorithms aim to extract the underlying events or different audio sources in a segment [41; 42], thus reducing event overlap before the event detection system. Both Mixup and source separation have already been explored to increase the performance of SED in the DCASE evaluation [43; 44], however, the impact of these techniques in highly overlapped scenarios has not been explicitly assessed yet.