Understanding Audio-Text Retrieval Through Singular Value Decomposition

Audio-language retrieval faces a fundamental challenge due to many-to-one mappings, where multiple audio instances correspond to the same or similar textual descriptions. This ambiguity leads to overlapping representations, making it difficult for models to learn discriminative features between samples. Queue-based contrastive learning helps mitigate this ambiguity by maintaining a large pool of negatives, improving retrieval performance. However, selecting appropriate hyperparameters, such as the queue size for optimal performance and the number of epochs to prevent overfitting, remains a challenge. In this study, we propose a LAtent Space Embedding Rank (LASER) analysis. Specifically, we analyze that a low-rank property of a queue indicates the presence of informative negative samples, and demonstrate that the best performance is achieved when the queue has the lowest rank. Furthermore, the convergence of rank variation can be interpreted as the model parameters reaching an optimal state during training. Thanks to the proposed rank-based analysis, the model hyperparameters can be selected analytically rather than through trial and error. Experimental results confirm that retrieval performance depends on queue rank, with lower ranks yielding better performance. These findings suggest that our LASER analysis can provide insight into how rank-based analysis can improve retrieval performance in audio-language tasks.