Audio Retrieval With Natural Language Queries: A Benchmark Study
A. Sophia Koepke, Andreea-Maria Oncescu, João F. Henriques, Zeynep Akata, Samuel Albanie · IEEE Transactions on Multimedia · 2022
The objectives of this work arecross-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 theAudioCapsandClothoaudio captioning datasets. Additionally, we introduce theSoundDescsbenchmark, which consists of paired audio and natural language descriptions for a diverse collection of sounds that are complementary to those found inAudioCapsandClotho. 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.