# Audio-text Retrieval with Transformer-based Hierarchical Alignment and Disentangled Cross-modal Representation

arXiv: https://arxiv.org/abs/2409.09256 · HTML: https://mlsanity.com/paper/2409.09256/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2024.

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

- [TAIL: Text-Audio Incremental Learning](https://mlsanity.com/paper/2503.04258/) (2025)
- [Learning Normal Patterns in Musical Loops](https://mlsanity.com/paper/2505.23784/) (2025)

## Abstract (arXiv)

Most existing audio-text retrieval (ATR) approaches typically rely on a single-level interaction to associate audio and text, limiting their ability to align different modalities and leading to suboptimal matches. In this work, we present a novel ATR framework that leverages two-stream Transformers in conjunction with a Hierarchical Alignment (THA) module to identify multi-level correspondences of different Transformer blocks between audio and text. Moreover, current ATR methods mainly focus on learning a global-level representation, missing out on intricate details to capture audio occurrences that correspond to textual semantics. To bridge this gap, we introduce a Disentangled Cross-modal Representation (DCR) approach that disentangles high-dimensional features into compact latent factors to grasp fine-grained audio-text semantic correlations. Additionally, we develop a confidence-aware (CA) module to estimate the confidence of each latent factor pair and adaptively aggregate cross-modal latent factors to achieve local semantic alignment. Experiments show that our THA effectively boosts ATR performance, with the DCR approach further contributing to consistent performance gains.