Signal Processing and Speech Communication Laboratory
homestudent projects › Single Channel Source Separation applied to Polyphonic Music

Single Channel Source Separation applied to Polyphonic Music

Status
Finished
Type
Student Project
Seminar Type
- None -
Announcement date
01 Oct 2010
Student
Clara Maria Hollomey
Mentors
Research Areas

Short Description

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.

Your Tasks

  • 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)

Your Profile/Requirements

This project is suited for Master students in Telematics, Electrical Engineering, Audio Engineering, Computer Science and Software Development.

Contact

Robert Peharz (robert.peharz@tugraz.at or 0316/873 4482)

References

[1] S. Roweis, “One microphone source separation,” in Neural Information Processing Systems, 2001, pp. 793–799.

[2] ——, “Factorial models and refiltering for speech separation and denoising,” in EUROSPEECH, 2003, pp. 1009–1012.

[3] P. Smaragdis, B. Raj, and M. Shashanka, “Supervised and semi-supervised separation of sounds from single-channel mixtures,” 2007.