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In this paper, we tackle the new Language-Based Audio Retrieval task proposedin DCASE 2022. Firstly, we introduce a simple, scalable architecture which tiesboth the audio and text encoder together. Secondly, we show that using thisarchitecture along with contrastive loss allows the model to significantly beatthe performance of the baseline model. Finally, in addition to having anextremely low training memory requirement, we are able to use pretrained modelsas it is without needing to finetune them. We test our methods and show thatusing a combination of our methods beats the baseline scores significantly.

Language-Based Audio Retrieval with Converging Tied Layers and Contrastive Loss