解决音频分类中少量样本下的持续学习问题，无需大量训练数据。
Fully Few-shot Class-incremental Audio Classification Using Multi-level Embedding Extractor and Ridge Regression Classifier
- 分离嵌入提取器与岭回归分类器，前者冻结后者持续更新。
- 在三个公开数据集上准确率优于现有方法，计算开销更低。
- 适合低资源场景下的音频分类持续学习任务。
在少样本持续音频分类（FCAC）任务中，通常要求每个基础类有充足训练样本以训练模型。然而，由于数据稀缺和采集成本高，许多基础类难以获得足够样本。本文探讨更现实的问题：完全少样本持续音频分类（FFCAC），即基础类与增量类的训练样本均极少。为此，提出一种基于多层级嵌入提取器与岭回归分类器的FFCAC方法。嵌入提取器由音频频谱图Transformer编码器与融合模块组成，在基础阶段训练后冻结于所有增量阶段；分类器则在每个增量阶段持续更新。在三个公开数据集上的实验表明，该方法在准确率上超越当前主流方法，且复杂度低于多数对比模型。代码已开源：https://github.com/YongjieSi/MAR。
原文摘要 · Abstract (English)
In the task of Few-shot Class-incremental Audio Classification (FCAC), training samples of each base class are required to be abundant to train model. However, it is not easy to collect abundant training samples for many base classes due to data scarcity and high collection cost. We discuss a more realistic issue, Fully FCAC (FFCAC), in which training samples of both base and incremental classes are only a few. Furthermore, we propose a FFCAC method using a model which is decoupled into a multi-level embedding extractor and a ridge regression classifier. The embedding extractor consists of an encoder of audio spectrogram Transformer and a fusion module, and is trained in the base session but frozen in all incremental sessions. The classifier is updated continually in each incremental session. Results on three public datasets show that our method exceeds current methods in accuracy, and has advantage over most of them in complexity. The code is at https://github.com/YongjieSi/MAR.
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