The NTT DCASE2020 Challenge Task 6 system: Automated Audio Captioning with Keywords and Sentence Length Estimation
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Audio captioningCaption GenerationMulti-Task LearningSentenceSimilar Papers 제목 키워드 기반
THE DCASE 2021 CHALLENGE TASK 6 SYSTEM: AUTOMATED AUDIO CAPTIONING WITH WEAKLY SUPERVISED PRE-TRAING AND WORD SELECTION METHODS
This technical report describes the system participating to the De- tection and Classification of Acoustic Scenes and Events (DCASE) 2021 Challenge, Task 6: automated audio captioning. We use encoder-decoder modeling …
Audio captioningCaption GenerationDecoderExpanding on EnCLAP with Auxiliary Retrieval Model for Automated Audio Captioning
In this technical report, we describe our submission to DCASE2024 Challenge Task6 (Automated Audio Captioning) and Task8 (Language-based Audio Retrieval). We develop our approach building upon the EnCLAP audio captioning…
Audio captioningRerankingRetrievalLanguage-based Audio Retrieval Task in DCASE 2022 Challenge
Language-based audio retrieval is a task, where natural language textual captions are used as queries to retrieve audio signals from a dataset. It has been first introduced into DCASE 2022 Challenge as Subtask 6B of task…
Audio captioningRetrievalLanguage-based Audio Retrieval Task in DCASE 2022 Challenge
Language-based audio retrieval is a task, where natural language textual captions are used as queries to retrieve audio signals from a dataset. It has been first introduced into DCASE 2022 Challenge as Subtask 6B of task…
Audio captioningRetrievalGeneral audio tagging with ensembling convolutional neural network and statistical features
Audio tagging aims to infer descriptive labels from audio clips. Audio tagging is challenging due to the limited size of data and noisy labels. In this paper, we describe our solution for the DCASE 2018 Task 2 general au…
Audio TaggingDescriptiveEnsemble LearningTask 2