# An Analysis of Sound Event Detection under Acoustic Degradation Using Multi-Resolution Systems
## 4 Conclusions  
In this paper, we studied the performance of several Sound Event Detection systems over a public dataset when diverse acoustic perturbations are applied. Five of these systems are convolutional neural networks with a common structure, but employing mel-spectrogram features extracted using different time-frequency resolutions. Two more systems are considered, which combine the previous systems into multiresolution models by means of an average fusion, increasing the performance over the evaluation subsets of the DESED dataset. According to the results, the proposed acoustic scenarios have a clearly negative impact on the performance of our systems, as expected. Although our multiresolution approach is robust to slight degradations, the average fusion is unable to improve performance when facing adverse conditions. However, an extra robustness against these adverse scenarios is observed when using multiple resolutions. Along with the generation of an artificially overlapped subset, we introduce a way to measure the level of overlap in sound event detection datasets, defining the metric \(\rho\) as the ratio between overlapped time and total duration of an event. This metric allows to compare the amount of event coincidences that are found in different datasets. Possible lines of future research include the extension of the analysis to consider other acoustic conditions, such as additional background noise or reverberation, and other evaluation metrics like the Polyphonic Sound Detection Score [45]; the exploration of other methods to combine the information from different resolution points, and the development of algorithms to tackle specific problems like event overlap. Furthermore, the data generated and the results obtained through this study will serve as a benchmark to evaluate the performance of future Sound Event Detection approaches and their robustness to diverse acoustic settings. Conceptualization, D.d.B.-G., D.T.T. and D.R. ; methodology, D.d.B.-G., D.T.T. and D.R. ; software, D.d.B.-G., validation, D.d.B.-G., D.T.T. and D.R. ; formal analysis, D.d.B.-G., D.T.T. and D.R. ; investigation, D.d.B.-G., D.T.T. and D.R. ; resources, D.T.T. and D.R. ; data curation, D.d.B.-G.; writing--original draft preparation, D.d.B.-G.; writing--review and editing, D.d.B.-G., D.T.T. and D.R. ; visualization, D.d.B.-G., D.T.T. and D.R. ; supervision, D.T.T. and D.R. ; project administration, D.T.T. and D.R. ; funding acquisition, D.T.T. and D.R. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & \(F_{++}\) & \(F_{+}\) & \(BS\) & \(T_{+}\) & \(T_{++}\) & **Avg-S** & **3 res** & **5 res** \\ \hline Clean & 36.26 & 36.74 & 37.62 & 37.12 & 35.15 & 36.58 & 41.81 & **43.05** \\ \hline HPF @ 500 Hz & 28.71 & 32.29 & 30.38 & 35.15 & 28.12 & 30.93 & 35.72 & **36.20** \\ HPF @ 1000 Hz & 17.53 & 17.09 & **22.73** & 22.43 & 19.10 & 19.78 & 21.41 & 22.72 \\ HPF @ 2000 Hz & 11.17 & 8.62 & 9.09 & **11.50** & 6.80 & 9.44 & 10.71 & 11.04 \\ \hline LPF @ 2000 Hz & 32.11 & 32.87 & 33.44 & 35.38 & 33.72 & 33.51 & 38.14 & **39.18** \\ LPF @ 1000 Hz & 22.44 & 24.41 & 26.90 & 29.45 & **30.58** & 26.76 & 30.55 & 30.16 \\ LPF @ 500 Hz & 18.64 & 19.56 & 20.71 & 25.89 & **26.13** & 22.19 & 25.22 & 25.26 \\ \hline Compression & 27.80 & 31.74 & 31.88 & 33.55 & 32.44 & 31.48 & 35.01 & **37.45** \\ Clipping & 29.88 & 29.58 & 31.95 & 30.51 & 29.47 & 30.28 & 35.53 & **36.74** \\ Overlap & 20.15 & 19.85 & 18.55 & 19.66 & 19.36 & 19.51 & **22.35** & 22.14 \\ \hline Overall & 23.16 & 24.00 & 25.07 & 27.06 & 25.08 & 24.87 & 28.29 & **28.99** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Macro-averaged, event-based \(F_{1}\) scores of single-resolution and multiresolution systems over DESED Public evaluation set (“Clean”) and proposed degradation scenarios. “Avg-S” column presents average performance of single-resolution systems (\(F_{++},F_{+},BS,T_{+},T_{++}\)). “Overall” row presents averaged results across all degradation scenarios.