Dual Transformer Decoder based Features Fusion Network for Automated Audio Captioning
Abstract
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.
Cite
@article{arxiv.2305.18753,
title = {Dual Transformer Decoder based Features Fusion Network for Automated Audio Captioning},
author = {Jianyuan Sun and Xubo Liu and Xinhao Mei and Volkan Kılıç and Mark D. Plumbley and Wenwu Wang},
journal= {arXiv preprint arXiv:2305.18753},
year = {2023}
}
Comments
INTERSPEECH 2023. arXiv admin note: substantial text overlap with arXiv:2210.05037