This project investigates contrastive learning techniques for aligning audio and text representations in the music domain, focusing on scenarios with limited data and computational resources. We provide a comprehensive review of existing methods relevant to music-text contrastive learning. Two audio encoders, HTSAT and MAEST, initialized with pretrained weights, are integrated with a frozen RoBERTa text encoder within the LAION-AI CLAP framework and fine-tuned on the MTGJamendo dataset. Model performance is evaluated on three tasks: zero-shot genre classification on the GTZAN dataset, multi-label tag classification on the MagnaTagATune dataset, and text-to-music retrieval on the Song Describer dataset. Results show that HTSAT generalizes better in low-data settings, while MAEST tends to overfit, highlighting the impact of encoder complexity in resource-constrained environments. Attempts to mitigate MAEST’s overfitting with weight decay and learning rate decay were unsuccessful. Additionally, the study highlights the critical role of data volume and batch size in contrastive learning effectiveness. The source code for this work is publicly available at https://github.com/SerX610/smc-master-thesis

Treball fi de màster de: Master in Sound and Music Computing

Supervisor: Pablo Alonso Jiménez

Co-Supervisor: Dmitry Bogdanov