# COALA: Co-Aligned Autoencoders for Learning Semantically Enriched Audio Representations

arXiv: https://arxiv.org/abs/2006.08386 · HTML: https://mlsanity.com/paper/2006.08386/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2020.

## Built on by (most cited first, 13 of 13)

- [BYOL for Audio: Self-Supervised Learning for General-Purpose Audio Representation](https://mlsanity.com/paper/2103.06695/) (2021)
- [MuLan: A Joint Embedding of Music Audio and Natural Language](https://mlsanity.com/paper/2208.12415/) (2022)
- [Audio Self-supervised Learning: A Survey](https://mlsanity.com/paper/2203.01205/) (2022)
- [Foundation Models for Music: A Survey](https://mlsanity.com/paper/2408.14340/) (2024)
- [Contrastive Audio-Language Learning for Music](https://mlsanity.com/paper/2208.12208/) (2022)
- [Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Data](https://mlsanity.com/paper/2206.02353/) (2022)
- [MusCaps: Generating Captions for Music Audio](https://mlsanity.com/paper/2104.11984/) (2021)
- [COCOLA: Coherence-Oriented Contrastive Learning of Musical Audio Representations](https://mlsanity.com/paper/2404.16969/) (2024)
- [Learning music audio representations via weak language supervision](https://mlsanity.com/paper/2112.04214/) (2021)
- [One Billion Audio Sounds from GPU-enabled Modular Synthesis](https://mlsanity.com/paper/2104.12922/) (2021)
- [Weakly-supervised Automated Audio Captioning via text only training](https://mlsanity.com/paper/2309.12242/) (2023)
- [Randomized Quantization: A Generic Augmentation for Data Agnostic Self-supervised Learning](https://mlsanity.com/paper/2212.08663/) (2022)
- [Evaluating Off-the-Shelf Machine Listening and Natural Language Models for Automated Audio Captioning](https://mlsanity.com/paper/2110.07410/) (2021)

## Abstract (arXiv)

Audio representation learning based on deep neural networks (DNNs) emerged as an alternative approach to hand-crafted features. For achieving high performance, DNNs often need a large amount of annotated data which can be difficult and costly to obtain. In this paper, we propose a method for learning audio representations, aligning the learned latent representations of audio and associated tags. Aligning is done by maximizing the agreement of the latent representations of audio and tags, using a contrastive loss. The result is an audio embedding model which reflects acoustic and semantic characteristics of sounds. We evaluate the quality of our embedding model, measuring its performance as a feature extractor on three different tasks (namely, sound event recognition, and music genre and musical instrument classification), and investigate what type of characteristics the model captures. Our results are promising, sometimes in par with the state-of-the-art in the considered tasks and the embeddings produced with our method are well correlated with some acoustic descriptors.