Single Channel Source Separation applied to Polyphonic Music
- Master Project
- Announcement date
- 01 Oct 2010
- Clara Maria Hollomey
- Robert Peharz
- Research Areas
In this project we want to apply single channel source separation (SCSS) techniques to the problem of separating musical instrument sources from a recorded piece of music. For this task the factorial max-Vector Quantization (max-VQ) and the factorial Sparse Coder (SC) models should be used, which have been successfully applied to human speech.
- Getting an overview of SCSS, especially with factorial max-VQ and factorial SC
- Apply factorial max-VQ and factorial SC to synthetic music
- Identify specific challenges which occur in connection with music
- Possibly modify the SCSS systems
- Possibly apply SCSS systems to real-world music
- Write a report (around 10 pages)
This project is suited for Master students in Telematics, Electrical Engineering, Audio Engineering, Computer Science and Software Development.
Robert Peharz (email@example.com or 0316/873 4482)
 S. Roweis, “One microphone source separation,” in Neural Information Processing Systems, 2001, pp. 793–799.
 ——, “Factorial models and reﬁltering for speech separation and denoising,” in EUROSPEECH, 2003, pp. 1009–1012.
 P. Smaragdis, B. Raj, and M. Shashanka, “Supervised and semi-supervised separation of sounds from single-channel mixtures,” 2007.