An investigation on selecting audio pre-trained models for audio captioning
This work addresses the challenge of efficiently evaluating pre-trained models for audio captioning, but it is incremental as it builds on existing methods without introducing a new paradigm.
The paper tackled the problem of selecting pre-trained audio models for audio captioning by investigating correlations between extracted audio features and captioning performance, proposing predictors based on kurtosis and skewness of features as indicators of system performance.
Audio captioning is a task that generates description of audio based on content. Pre-trained models are widely used in audio captioning due to high complexity. Unless a comprehensive system is re-trained, it is hard to determine how well pre-trained models contribute to audio captioning system. To prevent the time consuming and energy consuming process of retraining, it is necessary to propose a preditor of performance for the pre-trained model in audio captioning. In this paper, a series of pre-trained models are investigated for the correlation between extracted audio features and the performance of audio captioning. A couple of predictor is proposed based on the experiment results.The result demonstrates that the kurtosis and skewness of audio features extracted may act as an indicator of the performance of audio captioning systems for pre-trained audio due to the high correlation between kurtosis and skewness of audio features and the performance of audio captioning systems.