A Transformer-based Audio Captioning Model with Keyword Estimation

Abstract

One of the problems with automated audio captioning (AAC) is the indeterminacy in word selection corresponding to the audio event/scene. Since one acoustic event/scene can be described with several words, it results in a combinatorial explosion of possible captions and difficulty in training. To solve this problem, we propose a Transformer-based audio-captioning model with keyword estimation called TRACKE. It simultaneously solves the word-selection indeterminacy problem with the main task of AAC while executing the sub-task of acoustic event detection/acoustic scene classification (i.e., keyword estimation). TRACKE estimates keywords, which comprise a word set corresponding to audio events/scenes in the input audio, and generates the caption while referring to the estimated keywords to reduce word-selection indeterminacy. Experimental results on a public AAC dataset indicate that TRACKE achieved state-of-the-art performance and successfully estimated both the caption and its keywords.

Accepted to Interspeech 2020

References in corpus (5)

• Sequence to Sequence Learning with Neural Networks
• Acoustic Scene Classification
• Length-controllable Abstractive Summarization by Guiding with Summary Prototype
• The NTT DCASE2020 Challenge Task 6 system: Automated Audio Captioning with Keywords and Sentence Length Estimation
• Audiovisual Transformer Architectures for Large-Scale Classification and Synchronization of Weakly Labeled Audio Events