Max-Informative Unlabeled Sample Replay for Semi-Supervised Class-Incremental Learning in Audio Classification

Audio classification constitutes a critical task aimed at assigning meaningful labels to audio recordings. Despite commendable efforts in this field, prior endeavors have encountered notable challenges. Firstly, the majority of existing solutions operate under the assumption of a fixed vocabulary for classification, neglecting the crucial need for systems to adapt continuously to dynamic data streams. Secondly, these approaches often assume that input data is fully annotated, a presumption that frequently diverges from practical scenarios. In response to these challenges, we present a novel semi-supervised class-incremental learning framework that utilizes memory replay with unlabeled data and introduces temporal consistency regularization to better alleviate catastrophic forgetting. Additional, we employ Iterative Projection and Matching to select the most informative samples for storage, addressing the issues of reservoir sampling while maintaining a balanced buffer. We showcase the framework's efficacy through a series of comprehensive experiments conducted on both the ESC-50 and Google Speech Commands datasets. The results demonstrate the remarkable performance of our proposed approach, particularly in few-shot scenarios.