Existing audio search engines use one of two approaches: matching text-text or audio-audio
pairs. In the former, text queries are matched to semantically similar words in an index of …

We consider the task of retrieving audio using free-form natural language queries. To study
this problem, which has received limited attention in the existing literature, we introduce …

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 …

Content-based audio retrieval including query-by-example (QBE) and query-by-vocal
imitation (QBV) is useful when search-relevant text labels for the audio are unavailable, or …

This technical report presents a language-based audio retrieval system that we submitted to
Detection and Classification of Acoustic Scenes and Events (DCASE) Challenge 2022 Task …

Most existing audio-text retrieval (ATR) methods focus on constructing contrastive pairs
between whole audio clips and complete caption sentences, while ignoring fine-grained …

Audio-text retrieval based on natural language descriptions is a challenging task. It involves
learning cross-modality alignments between long sequences under inadequate data …

Despite recent progress in text-to-audio (TTA) generation, we show that the state-of-the-art
models, such as AudioLDM, trained on datasets with an imbalanced class distribution, such …

In text-audio retrieval (TAR) tasks, due to the heterogeneity of contents between text and
audio, the semantic information contained in the text is only similar to certain frames within …

As one of the most intuitive interfaces known to humans, natural language has the potential
to mediate many tasks that involve human-computer interaction, especially in application …