# Towards Robust Few-shot Class Incremental Learning in Audio Classification using Contrastive Representation

arXiv: https://arxiv.org/abs/2407.19265 · HTML: https://mlsanity.com/paper/2407.19265/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 22 references that are in the map.
- First posted on arXiv 27 Jul 2024.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [Dynamic Few-Shot Visual Learning without Forgetting](https://mlsanity.com/paper/1804.09458/) (2018)
- [Spatiotemporal Contrastive Video Representation Learning](https://mlsanity.com/paper/2008.03800/) (2020)
- [Large-Margin Softmax Loss for Convolutional Neural Networks](https://mlsanity.com/paper/1612.02295/) (2016)
- [Few-Shot Class-Incremental Learning](https://mlsanity.com/paper/2004.10956/) (2020)
- [Learning without Memorizing](https://mlsanity.com/paper/1811.08051/) (2018)
- [Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders](https://mlsanity.com/paper/1704.01279/) (2017)
- [Few-Shot Incremental Learning with Continually Evolved Classifiers](https://mlsanity.com/paper/2104.03047/) (2021)
- [Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by Finding Flat Minima](https://mlsanity.com/paper/2111.01549/) (2021)
- [Large Margin Deep Networks for Classification](https://mlsanity.com/paper/1803.05598/) (2018)
- [An Ensemble of Convolutional Neural Networks for Audio Classification](https://mlsanity.com/paper/2007.07966/) (2020)
- [Few-shot Class-incremental Audio Classification Using Adaptively-refined Prototypes](https://mlsanity.com/paper/2305.18045/) (2023)
- [Use of speaker recognition approaches for learning and evaluating embedding representations of musical instrument sounds](https://mlsanity.com/paper/2107.11506/) (2021)
- [Few-shot Class-incremental Audio Classification Using Stochastic Classifier](https://mlsanity.com/paper/2306.02053/) (2023)

## Built on by (most cited first, 2 of 2)

- [Strategic Base Representation Learning via Feature Augmentations for Few-Shot Class Incremental Learning](https://mlsanity.com/paper/2501.09361/) (2025)
- [Improving Audio Classification by Transitioning from Zero- to Few-Shot](https://mlsanity.com/paper/2507.20036/) (2025)

## Abstract (arXiv)

In machine learning applications, gradual data ingress is common, especially in audio processing where incremental learning is vital for real-time analytics. Few-shot class-incremental learning addresses challenges arising from limited incoming data. Existing methods often integrate additional trainable components or rely on a fixed embedding extractor post-training on base sessions to mitigate concerns related to catastrophic forgetting and the dangers of model overfitting. However, using cross-entropy loss alone during base session training is suboptimal for audio data. To address this, we propose incorporating supervised contrastive learning to refine the representation space, enhancing discriminative power and leading to better generalization since it facilitates seamless integration of incremental classes, upon arrival. Experimental results on NSynth and LibriSpeech datasets with 100 classes, as well as ESC dataset with 50 and 10 classes, demonstrate state-of-the-art performance.