In this report we present our method for the DCASE 2022 challenge on few-shot bioacoustic event detection. We use an ensemble of prototypical neural networks with adaptive embedding functions and show that both ensemble and adaptive embedding functions can be used to improve results from an average F-score of 41.3% to an average F-score of 60.0% on the validation dataset.

In this paper we study two major challenges in few-shot bioacoustic event detection: variable event lengths and false-positives. We use prototypical networks where the embedding function is trained using a multi-label sound event detection model instead of using episodic training as the proxy task on the provided training dataset. This is motivated by polyphonic sound events being present in the base training data. We propose a method to choose the embedding function based on the average event length of the few-shot examples and show that this makes the method more robust towards variable event lengths. Further, we show that an ensemble of prototypical neural networks trained on different training and validation splits of time-frequency images with different loudness normalizations reduces false-positives. In addition, we present an analysis on the effect that the studied loudness normalization techniques have on the performance of the prototypical network ensemble. Overall, per-channel energy normalization (PCEN) outperforms the standard log transform for this task. The method uses no data augmentation and no external data. The proposed approach achieves a F-score of 48.0% when evaluated on the hidden test set of the Detection and Classification of Acoustic Scenes and Events (DCASE) task 5

We study the problem of training personalized deep learning models in a decentralized peer-to-peer setting, focusing on the setting where data distributions differ between the clients and where different clients have different local learning tasks. We study both covariate and label shift, and our contribution is an algorithm which for each client finds beneficial collaborations based on a similarity estimate for the local task. Our method does not rely on hyperparameters which are hard to estimate, such as the number of client clusters, but rather continuously adapts to the network topology using soft cluster assignment based on a novel adaptive gossip algorithm. We test the proposed method in various settings where data is not independent and identically distributed among the clients. The experimental evaluation shows that the proposed method performs better than previous state-of-the-art algorithms for this problem setting, and handles situations well where previous methods fail