Contrastive Language Audio Pretraining

Contrastive Language-Audio Pretraining (CLAP) aims to create robust multimodal representations by aligning audio and text data using contrastive learning techniques. Current research focuses on improving CLAP models' performance across diverse downstream tasks, including audio classification, source separation, captioning, and text-to-audio generation, often employing architectures that combine masked modeling, feature fusion, and large language models. This approach offers significant advantages by enabling zero-shot inference, reducing reliance on large labeled datasets, and facilitating more generalizable audio analysis across various domains, impacting fields like speech recognition, music information retrieval, and assistive technologies.