Abstract:
In this paper, we propose a multi-resolution analysis for feature extraction in Sound Event Detection. Because of the specific temporal and spectral characteristics of the different acoustic events, we hypothesize that different time-frequency resolutions can be more appropriate to locate each sound category. We carry out our experiments using the DESED dataset in the context of the DCASE 2020 Task 4 challenge, where the combination of up to five different time-frequency resolutions via model fusion is able to outperform the baseline results. In addition, we propose class-specific thresholds for the <i>F</i><sub>1</sub>-score metric, further improving the results over the Validation and Public Evaluation sets.
