# Computer Audition: From Task-Specific Machine Learning to Foundation Models

arXiv: https://arxiv.org/abs/2407.15672 · HTML: https://mlsanity.com/paper/2407.15672/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2024.

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

- [Foundation Models for Music: A Survey](https://mlsanity.com/paper/2408.14340/) (2024)
- [Mellow: a small audio language model for reasoning](https://mlsanity.com/paper/2503.08540/) (2025)
- [Affective Computing Has Changed: The Foundation Model Disruption](https://mlsanity.com/paper/2409.08907/) (2024)
- [autrainer: A Modular and Extensible Deep Learning Toolkit for Computer Audition Tasks](https://mlsanity.com/paper/2412.11943/) (2024)
- [How Class Ontology and Data Scale Affect Audio Transfer Learning](https://mlsanity.com/paper/2603.25476/) (2026)
- [CoarseSoundNet: Building a reliable model for ecological soundscape analysis](https://mlsanity.com/paper/2605.21143/) (2026)
- [A Pilot Study on Curator-Guided Multilingual Art Description for Blind and Low-Vision Audiences with Small Vision-Language Models](https://mlsanity.com/paper/2605.31080/) (2026)
- [Acoustic Cue Alignment in Audio Language Models for Speech Emotion Recognition](https://mlsanity.com/paper/2606.07309/) (2026)

## Abstract (arXiv)

Foundation models (FMs) are increasingly spearheading recent advances on a variety of tasks that fall under the purview of computer audition -- the use of machines to understand sounds. They feature several advantages over traditional pipelines: among others, the ability to consolidate multiple tasks in a single model, the option to leverage knowledge from other modalities, and the readily-available interaction with human users. Naturally, these promises have created substantial excitement in the audio community, and have led to a wave of early attempts to build new, general-purpose foundation models for audio. In the present contribution, we give an overview of computational audio analysis as it transitions from traditional pipelines towards auditory foundation models. Our work highlights the key operating principles that underpin those models, and showcases how they can accommodate multiple tasks that the audio community previously tackled separately.