# The NTT DCASE2020 Challenge Task 6 system: Automated Audio Captioning with Keywords and Sentence Length Estimation

arXiv: https://arxiv.org/abs/2007.00225 · HTML: https://mlsanity.com/paper/2007.00225/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2020.

## Built on by (most cited first, 8 of 8)

- [BYOL for Audio: Self-Supervised Learning for General-Purpose Audio Representation](https://mlsanity.com/paper/2103.06695/) (2021)
- [LauraGPT: Listen, Attend, Understand, and Regenerate Audio with GPT](https://mlsanity.com/paper/2310.04673/) (2023)
- [DECAR: Deep Clustering for learning general-purpose Audio Representations](https://mlsanity.com/paper/2110.08895/) (2021)
- [Audio Barlow Twins: Self-Supervised Audio Representation Learning](https://mlsanity.com/paper/2209.14345/) (2022)
- [DeLoRes: Decorrelating Latent Spaces for Low-Resource Audio Representation Learning](https://mlsanity.com/paper/2203.13628/) (2022)
- [Text-to-Audio Grounding Based Novel Metric for Evaluating Audio Caption Similarity](https://mlsanity.com/paper/2210.06354/) (2022)
- [AC/DC: LLM-based Audio Comprehension via Dialogue Continuation](https://mlsanity.com/paper/2506.10312/) (2025)
- [UNFUSED: UNsupervised Finetuning Using SElf supervised Distillation](https://mlsanity.com/paper/2303.05668/) (2023)

## Abstract (arXiv)

This technical report describes the system participating to the Detection and Classification of Acoustic Scenes and Events (DCASE) 2020 Challenge, Task 6: automated audio captioning. Our submission focuses on solving two indeterminacy problems in automated audio captioning: word selection indeterminacy and sentence length indeterminacy. We simultaneously solve the main caption generation and sub indeterminacy problems by estimating keywords and sentence length through multi-task learning. We tested a simplified model of our submission using the development-testing dataset. Our model achieved 20.7 SPIDEr score where that of the baseline system was 5.4.