EnCLAP++: Analyzing the EnCLAP Framework for Optimizing Automated Audio Captioning Performance
<|reference_start|>EnCLAP++: Analyzing the EnCLAP Framework for Optimizing Automated Audio Captioning Performance: In this work, we aim to analyze and optimize the EnCLAP framework, a state-of-the-art model in automated audio captioning. We investigate the impact of modifying the acoustic encoder components, explore pretraining with different dataset scales, and study the effectiveness of a reranking scheme. Through extensive experimentation and quantitative analysis of generated captions, we develop EnCLAP++, an enhanced version that significantly surpasses the original.<|reference_end|>
@article{kim2024enclap++:,
    title={EnCLAP++: Analyzing the EnCLAP Framework for Optimizing Automated Audio
  Captioning Performance},
    author={Jaeyeon Kim, Minjeon Jeon, Jaeyoon Jung, Sang Hoon Woo, Jinjoo Lee},
    journal={arXiv preprint arXiv:2409.01201},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.01201},
    primaryClass={eess.AS cs.AI cs.SD}
}