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The objectives of this work are <italic>cross-modal text-audio and audio-text retrieval</italic>, 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 <sc>AudioCaps</sc> and <sc>Clotho</sc> audio captioning datasets. Additionally, we introduce the <sc>SoundDescs</sc> benchmark, which consists of paired audio and natural language descriptions for a diverse collection of sounds that are complementary to those found in <sc>AudioCaps</sc> and <sc>Clotho</sc>. 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.

Audio Retrieval With Natural Language Queries: A Benchmark Study