# Dual Transformer Decoder based Features Fusion Network for Automated Audio Captioning

arXiv: https://arxiv.org/abs/2305.18753 · HTML: https://mlsanity.com/paper/2305.18753/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2023.

## Built on by (most cited first, 3 of 3)

- [EnCLAP: Combining Neural Audio Codec and Audio-Text Joint Embedding for Automated Audio Captioning](https://mlsanity.com/paper/2401.17690/) (2024)
- [AVCap: Leveraging Audio-Visual Features as Text Tokens for Captioning](https://mlsanity.com/paper/2407.07801/) (2024)
- [Unbiased Sliced Wasserstein Kernels for High-Quality Audio Captioning](https://mlsanity.com/paper/2502.05435/) (2025)

## Abstract (arXiv)

Automated audio captioning (AAC) which generates textual descriptions of audio content. Existing AAC models achieve good results but only use the high-dimensional representation of the encoder. There is always insufficient information learning of high-dimensional methods owing to high-dimensional representations having a large amount of information. In this paper, a new encoder-decoder model called the Low- and High-Dimensional Feature Fusion (LHDFF) is proposed. LHDFF uses a new PANNs encoder called Residual PANNs (RPANNs) to fuse low- and high-dimensional features. Low-dimensional features contain limited information about specific audio scenes. The fusion of low- and high-dimensional features can improve model performance by repeatedly emphasizing specific audio scene information. To fully exploit the fused features, LHDFF uses a dual transformer decoder structure to generate captions in parallel. Experimental results show that LHDFF outperforms existing audio captioning models.