In applications such as audio denoising, music transcription, music remixing, and audio-based forensics, it is desirable to decompose a single- or stereo-channel recording into its respective sources. To perform such tasks, we present ISSE - an interactive source separation editor (pronounced "ice").
ISSE is an open-source, freely available, cross-platform audio editing tool that allows a user to perform source separation by painting on time-frequency visualizations of sound. The software leverages both a new user interaction paradigm and machine learning-based separation algorithm that "learns" from human feedback (e.g. painting annotations) to perform separation. For more information, please see the about and demos sections of the website and the demo video below.