Audio separation in real-world scenarios, where mixtures contain a variable number of sources, presents significant challenges due to limitations of existing models, such as over-separation, under-separation, and dependence on predefined training sources. We propose OpenSep, a novel framework that leverages large language models (LLMs) for automated audio separation, eliminating the need for manual intervention and overcoming source limitations. OpenSep uses textual inversion to generate captions from audio mixtures with off-the-shelf audio captioning models, effectively parsing the sound sources present. It then employs few-shot LLM prompting to extract detailed audio properties of each parsed source, facilitating separation in unseen mixtures. Additionally, we introduce a multi-level extension of the mix-and-separate training framework to enhance modality alignment by separating single source sounds and mixtures simultaneously. Extensive experiments demonstrate OpenSep's superiority in precisely separating new, unseen, and variable sources in challenging mixtures, outperforming SOTA baseline methods. Code is released at https://github.com/tanvir-utexas/OpenSep.git

Since Alan Turing, the father of AI, first proposed it in a 1950 paper, the Turing Test, which measures whether a computer can fool a human into thinking it's real, has been a classic measure of whether an artificial intelligence system is successful. And, according to a new paperreleased Monday that will be presented in June at the annual conference on Computer Vision and Pattern Recognition (CVPR) in Las Vegas, MIT's Computer Science and Artificial Intelligence Laboratory has now crossed a new threshold: Passing the Turing Test for sound. How the'PayPal Mafia' redefined success in Silicon Valley A decade ago, the PayPal Mafia played a major role in revitalizing the tech industry in Silicon Valley. The story behind this group of leaders proves that their success is more than just luck. Back in December, MIT researchers created an AI system that passed the Turing Test for vision by fooling humans into thinking that characters were written by humans rather than a machine.