Audio Mixture

Audio mixture research focuses on separating and manipulating individual sound sources within complex audio recordings, aiming to improve audio quality, enable targeted sound editing, and enhance downstream tasks like transcription and music generation. Current research emphasizes deep learning models, particularly those leveraging large language models for source identification and separation, variational autoencoders for latent representation learning, and contrastive learning for efficient model training. These advancements are impacting various fields, including music production, sound design, and assistive technologies by offering more precise control over audio content and improved performance in tasks involving mixed audio signals.