  Audio-text retrieval aims at retrieving a target audio clip or caption from a
pool of candidates given a query in another modality. Solving such cross-modal
retrieval task is challenging because it not only requires learning robust
feature representations for both modalities, but also requires capturing the
fine-grained alignment between these two modalities. Existing cross-modal
retrieval models are mostly optimized by metric learning objectives as both of
them attempt to map data to an embedding space, where similar data are close
together and dissimilar data are far apart. Unlike other cross-modal retrieval
tasks such as image-text and video-text retrievals, audio-text retrieval is
still an unexplored task. In this work, we aim to study the impact of different
metric learning objectives on the audio-text retrieval task. We present an
extensive evaluation of popular metric learning objectives on the AudioCaps and
Clotho datasets. We demonstrate that NT-Xent loss adapted from self-supervised
learning shows stable performance across different datasets and training
settings, and outperforms the popular triplet-based losses. Our code is
available at https://github.com/XinhaoMei/audio-text_retrieval.
