<ul><li>The study explores efficient annotation strategies for Sound Event Detection (SED) applications using Active Learning (AL).</li><li>A novel uncertainty aggregation strategy called Top K Entropy is introduced, which prioritizes the most uncertain segments in an audio recording for annotation.</li><li>Compared to random sampling and Mean Entropy, Top K Entropy leads to improved annotation efficiency in sparse data scenarios.</li><li>Using Top K Entropy, the study demonstrates comparable model performance with only 8% of the labels compared to training on the fully labeled dataset.</li></ul>

Aggregation Strategies for Efficient Annotation of Bioacoustic Sound Events Using Active Learning

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