Detailed info
WaveTransformer: An Architecture for Audio Captioning Based on Learning Temporal and Time-Frequency Information
| Authors | An Tran, Konstantinos Drossos, Tuomas Virtanen |
| Title | WaveTransformer: An Architecture for Audio Captioning Based on Learning Temporal and Time-Frequency Information |
| Abstract | Automated audio captioning (AAC) is a novel task, where a method takes as an input an audio sample and outputs a textual description (i.e. a caption) of its contents. Most AAC methods are adapted from image captioning or machine translation fields. In this work, we present a novel AAC method, explicitly focused on the exploitation of the temporal and time-frequency patterns in audio. We employ three learnable processes for audio encoding, two for extracting the temporal and time-frequency information, and one to merge the output of the previous two processes. To generate the caption, we employ the widely used Transformer decoder. We assess our method utilizing the freely available splits of the Clotho dataset. Our results increase previously reported highest SPIDEr to 17.3, from 16.2 (higher is better). |
| ISBN | 978-1-6654-0900-1 |
| Conference | 2021 29th European Signal Processing Conference (EUSIPCO) |
| Date | 23/08/2021 |
| Location | Dublin, Ireland |
| Year of Publication, Publisher | 2021 |
| Url | https://zenodo.org/record/5723160 |
| DOI | |
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Funding
This project has received funding from the European Union’s Horizon 2020 Research and Innovation program under grant agreement No 957337. The website reflects only the view of the author(s) and the Commission is not responsible for any use that may be made of the information it contains.
