# An Analysis of Sound Event Detection under Acoustic Degradation Using Multi-Resolution Systems
## 2 Materials and Methods  
### DCASE 2020 Challenge: \"Sound Event Detection and Separation in Domestic Environments\"  
In the 2020 edition of the DCASE Challenge, one of the tasks proposes a Sound Event Detection scenario where systems are trained using the DESED dataset [4], composed of 10-second audio recordings (Task 4). An optional preprocessing step based on sound separation is proposed in the task, however, the source separation stage is out of the scope of this work.  
The training data used for this work includes 1578 weakly labeled and 14,412 unlabeled audio clips extracted from Google AudioSet, along with 2584 strongly labeled audio segments that were synthetically generated using the Scaper toolkit [30]. Synthetic clips contain mixtures of foreground events from Freesound and background soundscapes extracted from the SINS dataset [31].  
In addition, a subset of 1168 AudioSet segments is provided (DESED Validation set) with strong, human-verified annotations, which are used to validate the performance of the systems. Finally, the YouTube subset of the DESED 2019 Evaluation set, which is called DESED Public Evaluation, is intended to act as a benchmark for SED performance.  
The set of target categories includes ten event categories which are usually found in the acoustic context of a house: Speech, Dog, Cat, Alarm/bell/ringing, Dishes, Frying, Blender, Running water, Vacuum cleaner, and Electric shaver/toothbrush. More than one target category, or several events of the same class, could be present in the same audio recording, and different categories are allowed to overlap in time. The number of occurrences of each event category is not balanced, but the distribution of events is similar across datasets, with some of the categories, like Speech or Dishes presenting larger numbers of appearances, whereas some other classes like Vacuum cleaner or Blender have less examples. Table 1 shows the number of events of each class that can be found in the strongly labeled subsets of DESED.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Event Class** & **Synth Train** & **Validation** & **Public Eval** \\ \hline Alarm/bell/ringing & 587 & 420 & 196 \\ Blender & 370 & 94 & 84 \\ Cat & 731 & 341 & 240 \\ Dishes & 1123 & 559 & 488 \\ Dog & 824 & 570 & 441 \\ Electric shaver/toothbr. & 345 & 65 & 108 \\ Frying & 229 & 94 & 90 \\ Running water & 270 & 237 & 109 \\ Speech & 2760 & 1752 & 913 \\ Vacuum cleaner & 343 & 92 & 96 \\ \hline N\({}^{\circ}\) events & 10,885 & 4224 & 2765 \\ N\({}^{\circ}\) clips & 2584 & 1168 & 692 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of events per class in the strongly labeled DESED subsets: Synth Training, Validation, and Public Evaluation, and total number of events and audio clips in each set. The total number of events and sound clips in each dataset is presented in the bottom rows.  
Systems output the predicted onset and offset times of the detected events, along with their category. To define whether a prediction is correct, a collar of 200 ms is considered for the onset times, whereas for the offset times the collar is the maximum between 200 ms and 20% of the event length, aiming to handle the difficulty to determine the offset times of long events. The system performance is measured by means of the \(F_{1}\) score metric, which is computed as a combination of the True Positive (\(TP\)), False Positive (\(FP\)) and False Negative (\(FN\)) counts [32]:  
\[F_{1}=\frac{2\times TP}{2\times TP+FP+FN}. \tag{1}\]  
Firstly, \(F_{1}\) scores are computed for each event category, then the Macro \(F_{1}\) is obtained by averaging the class-wise \(F_{1}\) scores. Macro \(F_{1}\) is used to measure the global performance of the systems.