# Audio-Visual Model Distillation Using Acoustic Images

arXiv: https://arxiv.org/abs/1904.07933 · HTML: https://mlsanity.com/paper/1904.07933/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2019.

## Built on by (most cited first, 8 of 8)

- [There is More than Meets the Eye: Self-Supervised Multi-Object Detection and Tracking with Sound by Distilling Multimodal Knowledge](https://mlsanity.com/paper/2103.01353/) (2021)
- [Revisiting Label Smoothing and Knowledge Distillation Compatibility: What was Missing?](https://mlsanity.com/paper/2206.14532/) (2022)
- [A Survey on Efficient Federated Learning Methods for Foundation Model Training](https://mlsanity.com/paper/2401.04472/) (2024)
- [Teacher-Student Architecture for Knowledge Distillation: A Survey](https://mlsanity.com/paper/2308.04268/) (2023)
- [Teacher-Student Architecture for Knowledge Learning: A Survey](https://mlsanity.com/paper/2210.17332/) (2022)
- [Revealing Vision-Language Integration in the Brain with Multimodal Networks](https://mlsanity.com/paper/2406.14481/) (2024)
- [What is Left After Distillation? How Knowledge Transfer Impacts Fairness and Bias](https://mlsanity.com/paper/2410.08407/) (2024)
- [View-aware Cross-modal Distillation for Multi-view Action Recognition](https://mlsanity.com/paper/2511.12870/) (2025)

## Abstract (arXiv)

In this paper, we investigate how to learn rich and robust feature representations for audio classification from visual data and acoustic images, a novel audio data modality. Former models learn audio representations from raw signals or spectral data acquired by a single microphone, with remarkable results in classification and retrieval. However, such representations are not so robust towards variable environmental sound conditions. We tackle this drawback by exploiting a new multimodal labeled action recognition dataset acquired by a hybrid audio-visual sensor that provides RGB video, raw audio signals, and spatialized acoustic data, also known as acoustic images, where the visual and acoustic images are aligned in space and synchronized in time. Using this richer information, we train audio deep learning models in a teacher-student fashion. In particular, we distill knowledge into audio networks from both visual and acoustic image teachers. Our experiments suggest that the learned representations are more powerful and have better generalization capabilities than the features learned from models trained using just single-microphone audio data.