用预训练模型嵌入实现音频分类的在线增量学习，减少遗忘。
Online incremental learning for audio classification using a pretrained audio model
- 在预训练音频嵌入后加非线性层扩展维度，捕捉音类特征。
- 单次前向传播即可适应新任务，旧任务遗忘极少。
- 适用于持续学习新类别或新场景的音频系统开发者。
增量学习旨在不遗忘旧任务的前提下顺序学习新任务。现有方法多从头训练模型并重复迭代以适应新任务，需采用特定策略缓解遗忘。本文提出一种基于预训练音频模型生成通用嵌入的在线增量学习方法，通过在预训练嵌入与分类器之间引入带非线性激活函数的层，扩展嵌入维度并有效捕捉声音类别的独特特征。该方法仅需一次前向传播即可完成对任意任务样本的在线适应，显著降低对旧任务的遗忘。我们在两种增量学习设置中验证了该方法：使用ESC-50进行一类增量学习，以及在TAU Urban Acoustic Scenes 2019数据集上进行不同城市的域增量学习。实验结果表明，所提方法在两类场景下均优于现有方法。
原文摘要 · Abstract (English)
Incremental learning aims to learn new tasks sequentially without forgetting the previously learned ones. Most of the existing incremental learning methods for audio focus on training the model from scratch on the initial task, and the same model is used to learn upcoming incremental tasks. The model is trained for several iterations to adapt to each new task, using some specific approaches to reduce the forgetting of old tasks. In this work, we propose a method for using generalizable audio embeddings produced by a pre-trained model to develop an online incremental learner that solves sequential audio classification tasks over time. Specifically, we inject a layer with a nonlinear activation function between the pre-trained model's audio embeddings and the classifier; this layer expands the dimensionality of the embeddings and effectively captures the distinct characteristics of sound classes. Our method adapts the model in a single forward pass (online) through the training samples of any task, with minimal forgetting of old tasks. We demonstrate the performance of the proposed method in two incremental learning setups: one class-incremental learning using ESC-50 and one domain-incremental learning of different cities from the TAU Urban Acoustic Scenes 2019 dataset; for both cases, the proposed approach outperforms other methods.
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