Most studies on music source separation have traditionally concentrated on separating popular music into vocals, bass, drums, and others, with fewer studies focusing on chamber ensemble audio. The task of separating sources from chamber ensemble audio presents greater challenges due to factors like timbral similarity, high synchronization, and spectral overlap. These complexities necessitate more refined and realistic datasets for effective chamber ensemble separation. Yet, the creation of such datasets faces numerous challenges, depending on the methods of dataset formation. Taking these challenges into account, our work seeks to enhance the performance of chamber ensemble separation tasks, with a particular focus on string quartets. Based on dataset generation using a neural synthesis model, we propose an approach that incorporates musical expressions into the dataset generation process using MusicXML files. Furthermore, our framework adapt reverberation to the audio to better match the target mixtures. Our objective evaluation shows an increase in performance of chamber ensemble separation, supported by a subjective listening test to demonstrate improvement in our dataset. We also release our dataset and source code for further usage.

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.