EnCLAP: Combining Neural Audio Codec and Audio-Text Joint Embedding for Automated Audio Captioning

Jaeyeon Kim; Jaeyoon Jung; Jinjoo Lee; Sang Hoon Woo

ICASSP 2024accepted0 citations

EnCLAP: Combining Neural Audio Codec and Audio-Text Joint Embedding for Automated Audio Captioning

Jaeyeon Kim, Jaeyoon Jung, Jinjoo Lee, Sang Hoon Woo

Abstract

We propose EnCLAP, a novel framework for automated audio captioning. EnCLAP employs two acoustic representation models, EnCodec and CLAP, along with a pretrained language model, BART. We also introduce a new training objective called masked codec modeling that improves acoustic awareness of the pretrained language model. Experimental results on AudioCaps and Clotho demonstrate that our model surpasses the performance of baseline models. Source code will be available at https://github.com/jaeyeonkim99/EnCLAP. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>

BibTeX

@inproceedings{icassp2024_enclapcombiningn,
  title = {EnCLAP: Combining Neural Audio Codec and Audio-Text Joint Embedding for Automated Audio Captioning},
  author = {Jaeyeon Kim and Jaeyoon Jung and Jinjoo Lee and Sang Hoon Woo},
  booktitle = {ICASSP 2024},
  year = {2024}
}