Contrastive Audio

Contrastive audio learning focuses on creating robust audio representations by comparing and contrasting audio segments with corresponding text descriptions or other audio views. Current research emphasizes developing improved model architectures, such as contrastive learning frameworks and masked autoencoders, to better capture temporal information and handle diverse audio data, including speech and music. This approach is proving valuable for various applications, including speaker verification, text-to-speech synthesis, and audio retrieval, by enabling more effective zero-shot and few-shot learning capabilities and improving the performance of downstream tasks. The development of large-scale datasets and refined negative sampling strategies are also key areas of ongoing investigation.