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• W3206649123 abstract "We introduce DECAR, a self-supervised pre-training approach for learning general-purpose audio representations. Our system is based on clustering: it utilizes an offline clustering step to provide target labels that act as pseudo-labels for solving a prediction task. We develop on top of recent advances in self-supervised learning for computer vision and design a lightweight, easy-to-use self-supervised pre-training scheme. We pre-train DECAR embeddings on a balanced subset of the large-scale Audioset dataset and transfer those representations to 9 downstream classification tasks, including speech, music, animal sounds, and acoustic scenes. Furthermore, we conduct ablation studies identifying key design choices and also make all our code and pre-trained models publicly available." @default.
• W3206649123 created "2021-10-25" @default.
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• W3206649123 date "2021-10-17" @default.
• W3206649123 modified "2023-10-14" @default.
• W3206649123 title "Deep Clustering For General-Purpose Audio Representations" @default.
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