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Access to large corpora with strongly labelled sound events is expensive anddifficult in engineering applications. Much research turns to address theproblem of how to detect both the types and the timestamps of sound events withweak labels that only specify the types. This task can be treated as a multipleinstance learning (MIL) problem, and the key to it is the design of a poolingfunction. In this paper, we propose an adaptive power pooling function whichcan automatically adapt to various sound sources. On two public datasets, theproposed power pooling function outperforms the state-of-the-art linear softmaxpooling on both coarsegrained and fine-grained metrics. Notably, it improvesthe event-based F1 score (which evaluates the detection of event onsets andoffsets) by 11.4% and 10.2% relative on the two datasets. While this paperfocuses on sound event detection applications, the proposed method can beapplied to MIL tasks in other domains.

Power pooling: An adaptive pooling function for weakly labelled sound event detection