EnCLAP++: Analyzing the EnCLAP Framework for Optimizing Automated Audio Captioning Performance
This work addresses the challenge of generating accurate captions for audio data, which is important for applications like accessibility and multimedia indexing, but it is incremental as it builds upon an existing framework.
The researchers tackled the problem of optimizing automated audio captioning by analyzing and enhancing the EnCLAP framework, resulting in EnCLAP++, which significantly surpasses the original model's 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.