# HalluAudio: Hallucinating Frequency as Concepts for Few-Shot Audio Classification

arXiv: https://arxiv.org/abs/2302.14204 · HTML: https://mlsanity.com/paper/2302.14204/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 26 references that are in the map.
- First posted on arXiv 27 Feb 2023.

## Builds on (most cited first, 17 of 17)

- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [A Closer Look at Few-shot Classification](https://mlsanity.com/paper/1904.04232/) (2019)
- [Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning](https://mlsanity.com/paper/1805.10002/) (2018)
- [Few-Shot Image Recognition by Predicting Parameters from Activations](https://mlsanity.com/paper/1706.03466/) (2017)
- [Finding Task-Relevant Features for Few-Shot Learning by Category Traversal](https://mlsanity.com/paper/1905.11116/) (2019)
- [MetaAudio: A Few-Shot Audio Classification Benchmark](https://mlsanity.com/paper/2204.02121/) (2022)
- [Knowledge Transfer from Weakly Labeled Audio using Convolutional Neural Network for Sound Events and Scenes](https://mlsanity.com/paper/1711.01369/) (2017)
- [Learning to match transient sound events using attentional similarity for few-shot sound recognition](https://mlsanity.com/paper/1812.01269/) (2018)
- [Few-shot acoustic event detection via meta-learning](https://mlsanity.com/paper/2002.09143/) (2020)
- [Training neural audio classifiers with few data](https://mlsanity.com/paper/1810.10274/) (2018)
- [A Study of Few-Shot Audio Classification](https://mlsanity.com/paper/2012.01573/) (2020)
- [Adaptive Few-Shot Learning Algorithm for Rare Sound Event Detection](https://mlsanity.com/paper/2205.11738/) (2022)

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

- [LightHouse: A Survey of AGI Hallucination](https://mlsanity.com/paper/2401.06792/) (2024)
- [Few-shot Class-variable Incremental Audio Classification via Prototype Adaptation and Pseudo Class-variable Training](https://mlsanity.com/paper/2606.08898/) (2026)

## Abstract (arXiv)

Few-shot audio classification is an emerging topic that attracts more and more attention from the research community. Most existing work ignores the specificity of the form of the audio spectrogram and focuses largely on the embedding space borrowed from image tasks, while in this work, we aim to take advantage of this special audio format and propose a new method by hallucinating high-frequency and low-frequency parts as structured concepts. Extensive experiments on ESC-50 and our curated balanced Kaggle18 dataset show the proposed method outperforms the baseline by a notable margin. The way that our method hallucinates high-frequency and low-frequency parts also enables its interpretability and opens up new potentials for the few-shot audio classification.