Dual Transformer Decoder based Features Fusion Network for Automated Audio Captioning

Author:

Jianyuan Sun, Xubo Liu, Xinhao Mei, Volkan Kılıç, Mark D. Plumbley, Wenwu Wang

Keyword:

Electrical Engineering and Systems Science, Audio and Speech Processing, Audio and Speech Processing (eess.AS), Sound (cs.SD)

journal:

date:

2023-05-29 16:00:00

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.

PDF: Dual Transformer Decoder based Features Fusion Network for Automated Audio Captioning.pdf