Language Based Audio Retrieval

Language-based audio retrieval focuses on developing systems that can accurately retrieve audio segments matching a given text description. Current research emphasizes improving retrieval accuracy through advanced model architectures like dual encoders and transformer-based approaches, often incorporating contrastive learning and techniques to address data imbalances, such as paraphrasing and data augmentation. These advancements are driven by the need for more robust and efficient methods for indexing and searching large audio datasets, with applications ranging from improved multimedia search to enhanced accessibility for individuals with visual impairments.