2021/12/31 by A. Sophia Koepke, Andreea-Maria Oncescu, João F. Henriques +2 · 1 citation
Engineering · Computer Science · #eess.AS #cs.IR #cs.SD
paper · pdf · doi:10.1109/tmm.2022.3149712
published as IEEE Transactions on Multimedia 2022 · Submitted to Transactions on Multimedia. arXiv admin note: substantial text overlap with arXiv:2105.02192
arxiv created 2022/01/27 · arxiv updated 2022/02/11
The objectives of this work are cross-modal text-audio and audio-text retrieval, in which the goal is to retrieve the audio content from a pool of candidates that best matches a given written description and vice versa. Text-audio retrieval enables users to search large databases through an intuitive interface: they simply issue free-form natural language descriptions of the sound they would like to hear. To study the tasks of text-audio and audio-text retrieval, which have received limited attention in the existing literature, we introduce three challenging new benchmarks. We first construct text-audio and audio-text retrieval benchmarks from the AudioCaps and Clotho audio captioning datasets. Additionally, we introduce the SoundDescs benchmark, which consists of paired audio and natural language descriptions for a diverse collection of sounds that are complementary to those found in AudioCaps and Clotho. We employ these three benchmarks to establish baselines for cross-modal text-audio and audio-text retrieval, where we demonstrate the benefits of pre-training on diverse audio tasks. We hope that our benchmarks will inspire further research into audio retrieval with free-form text queries. Code, audio features for all datasets used, and the SoundDescs dataset are publicly available at https://github.com/akoepke/audio-retrieval-benchmark.