| Paper: | MLSP-L4.4 | ||
| Session: | Blind Source Separation I | ||
| Time: | Thursday, May 18, 17:30 - 17:50 | ||
| Presentation: | Lecture | ||
| Topic: | Machine Learning for Signal Processing: Blind Signal Separation and Independent Component Analysis | ||
| Title: | A Novel Approach to Automated Source Separation in Multispeaker Environments | ||
| Authors: | Robert Nickel, Ananth Iyer, The Pennsylvania State University, United States | ||
| Abstract: | We are proposing a new approach to the solution of the cocktail party problem (CPP). The goal of the CPP is to isolate the speech signals of individuals who are concurrently talking while being recorded with a properly positioned microphone array. The new approach provides a powerful yet simple alternative to commonly used methods for the separation of speakers. It is based on the observation that the estimation of the signal transfer matrix between speakers and microphones is significantly simplified if one can assure that during certain periods of the conversation only one speaker is active while all other speakers are silent. Methods to determine such exclusive activity periods are described and a procedure to estimate the signal transfer matrix is presented. A comparison of the proposed method with other popular source separation methods is drawn. The results show an improved performance of the proposed method over earlier approaches. | ||
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