Learning Representations for New Sound Classes With Continual Self-Supervised Learning

• URL: http://arxiv.org/abs/2205.07390v1
• Date: Sun, 15 May 2022 22:15:21 GMT
• Title: Learning Representations for New Sound Classes With Continual Self-Supervised Learning
• Authors: Zhepei Wang, Cem Subakan, Xilin Jiang, Junkai Wu, Efthymios Tzinis, Mirco Ravanelli, Paris Smaragdis
• Abstract summary: We present a self-supervised learning framework for continually learning representations for new sound classes. We show that representations learned with the proposed method generalize better and are less susceptible to catastrophic forgetting.
• Score: 30.35061954854764
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we present a self-supervised learning framework for continually learning representations for new sound classes. The proposed system relies on a continually trained neural encoder that is trained with similarity-based learning objectives without using labels. We show that representations learned with the proposed method generalize better and are less susceptible to catastrophic forgetting than fully-supervised approaches. Remarkably, our technique does not store past data or models and is more computationally efficient than distillation-based methods. To accurately assess the system performance, in addition to using existing protocols, we propose two realistic evaluation protocols that use only a small amount of labeled data to simulate practical use cases.

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