EnCLAP++: Analyzing the EnCLAP Framework for Optimizing Automated Audio Captioning Performance

Although EnCLAP exhibits impressive performance, the study by Kim et al. lacks sufficient experimental evaluation for determining In this work, we aim to analyze and optimize the EnCLAP framework, the optimal models for the model components. Notably, a state-of-the-art model in automated audio captioning. We the authors do not investigate alternative sequence-level acoustic investigate the impact of modifying the acoustic encoder components, features beyond CLAP. Furthermore, for timestep-level acoustic explore pretraining with different dataset scales, and study the features, while they demonstrate that discrete codec input outperforms effectiveness of a reranking scheme. Through extensive experimentation continuous input, their analysis is restricted to a single setup and quantitative analysis of generated captions, we develop using EnCodec, without exploring other options or configurations. EnCLAP++, an enhanced version that significantly surpasses the Additionally, Kim et al. acknowledge the issue of overfitting in original.