Automated Audio Captioning using Audio Event Clues

TL;DR

This paper introduces an encoder-decoder model for audio captioning that leverages both acoustic features and audio event labels, improving caption quality over existing methods.

Contribution

It proposes a novel approach combining acoustic features and audio event labels, enhancing audio captioning performance beyond prior models.

Findings

Using audio event labels improves captioning accuracy.

The model outperforms or matches state-of-the-art results.

Various feature and configuration combinations were tested.

Abstract

Audio captioning is an important research area that aims to generate meaningful descriptions for audio clips. Most of the existing research extracts acoustic features of audio clips as input to encoder-decoder and transformer architectures to produce the captions in a sequence-to-sequence manner. Due to data insufficiency and the architecture's inadequate learning capacity, additional information is needed to generate natural language sentences, as well as acoustic features. To address these problems, an encoder-decoder architecture is proposed that learns from both acoustic features and extracted audio event labels as inputs. The proposed model is based on pre-trained acoustic features and audio event detection. Various experiments used different acoustic features, word embedding models, audio event label extraction methods, and implementation configurations to show which combinations have better performance on the audio captioning task. Results of the extensive experiments on multiple datasets show that using audio event labels with the acoustic features improves the recognition performance and the proposed method either outperforms or achieves competitive results with the state-of-the-art models.