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New article: Feature-based cluster selection framework for binary classification on imbalanced acoustic data

This work shows how self supervised features and over clustering can solve a hard imbalanced binary task with simple downstream logic. It turns limited labels into high quality pixel level decisions that nearly match a supervised approach. The paper is available online at Science Direct