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Lifelong audio feature extraction involves learning new sound classesincrementally, which is essential for adapting to new data distributions overtime. However, optimizing the model only on new data can lead to catastrophicforgetting of previously learned tasks, which undermines the model's ability toperform well over the long term. This paper introduces a new approach tocontinual audio representation learning called DeCoR. Unlike other methods thatstore previous data, features, or models, DeCoR indirectly distills knowledgefrom an earlier model to the latest by predicting quantization indices from adelayed codebook. We demonstrate that DeCoR improves acoustic sceneclassification accuracy and integrates well with continual self-supervisedrepresentation learning. Our approach introduces minimal storage andcomputation overhead, making it a lightweight and efficient solution forcontinual learning.

DeCoR: Defy Knowledge Forgetting by Predicting Earlier Audio Codes