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

## TL;DR

This paper introduces a novel few-shot audio classification method that leverages hallucinating frequency-based concepts, specifically high and low-frequency parts, to improve performance and interpretability.

## Contribution

The work proposes a new approach that exploits the audio spectrogram's structure by hallucinating frequency components, advancing few-shot audio classification techniques.

## Key findings

- Outperforms baseline methods on ESC-50 and Kaggle18 datasets
- Enhances interpretability of audio classification models
- Demonstrates the effectiveness of frequency hallucination in few-shot learning

## Abstract

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.