Audio Source Separation

Audio source separation aims to isolate individual sound sources from a mixture, a crucial task with applications in music production, speech enhancement, and assistive listening. Current research emphasizes developing robust models, including generative approaches like diffusion models and variational autoencoders, as well as discriminative methods using convolutional and recurrent neural networks, often incorporating language queries or spatial information for improved separation. These advancements are driving progress in various fields, improving the quality of audio experiences and enabling more sophisticated audio analysis techniques.