Audio Retrieval

Audio retrieval focuses on efficiently matching audio recordings with textual descriptions, enabling powerful search and indexing capabilities for audio databases. Current research emphasizes improving retrieval accuracy through advanced model architectures like dual encoders and transformers, often incorporating contrastive learning and techniques to leverage metadata or large language models for richer semantic understanding. These advancements are driving progress in various applications, including automated audio captioning, sound effect retrieval for video production, and enhancing accessibility for multimodal design documents. The development of robust benchmarks and large-scale datasets is crucial for continued progress in this rapidly evolving field.