| Paper: | MLSP-P4.12 | ||
| Session: | Machine Learning Applications | ||
| Time: | Thursday, May 20, 09:30 - 11:30 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Speech and Audio Processing Applications | ||
| Title: | SEMI-BLIND SOURCE SEPARATION USING HEAD-RELATED TRANSFER FUNCTIONS | ||
| Authors: | Michael Syskind Pedersen; Oticon A/S | ||
| Lars Kai Hansen; Technical University of Denmark | |||
| Ulrik Kjems; Oticon A/S | |||
| Karsten Bo Rasmussen; Oticon A/S | |||
| Abstract: | An online blind source separation algorithm which is a special case of the geometric algorithm by Parra and Fancourt has been implemented for the purpose of separating sounds recorded at microphones placed at each side of the head. By using the assumption that the position of the two sounds are known, the source separation algorithm has been geometrically constrained. Since the separation takes place in a non free-field, a head-related transfer function (HRTF) is used to simulate the response between microphones placed at the two ears. The use of a HRTF instead of assuming free-field improves the separation with approximately 1 dB compared to when free-field is assumed. This indicates that the permutation ambiguity is solved more accurate compared to when free-field is assumed. | ||
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