Single Channel Mixture

Single-channel source separation aims to isolate individual sounds from a single audio recording containing multiple overlapping sources, a challenging problem with significant practical implications. Current research focuses on developing neural network architectures, such as autoencoders and other deep learning models, to achieve this separation, often leveraging techniques like unsupervised learning with innovative loss functions or incorporating textual or audio cues to guide the separation process. These advancements are improving the accuracy of sound separation in various applications, including speech enhancement, biosignal processing, and audio editing, by offering solutions that previously relied on multi-channel recordings or strong prior assumptions.