| Paper: | MLSP-L4.1 | ||
| Session: | Blind Source Separation I | ||
| Time: | Thursday, May 18, 16:30 - 16:50 | ||
| Presentation: | Lecture | ||
| Topic: | Machine Learning for Signal Processing: Blind Signal Separation and Independent Component Analysis | ||
| Title: | Statistical Inference of Missing Speech Data in the ICA Domain | ||
| Authors: | Justinian Rosca, Siemens Corporate Research, United States; Timo Gerkmann, Bochum University, Germany; Doru-Cristian Balcan, Carnegie Mellon University, United States | ||
| Abstract: | We address the problem of speech estimation as statistical estimation with ``missing'' data in the independent component analysis (ICA) domain. Missing components are substituted by values drawn from ``similar'' data in a multi-faceted ICA representation of the complete data. The paper presents the algorithm for the inference of missing data in the case of a fixed pattern of missing data. We apply our approach to the problem of bandwidth extension, or where speech is degraded by a fixed filtering process and show the capability of the algorithm to reconstruct fine missing details of the original data with little artifacts. The evaluation is done using objective distortion measures on speech samples from the NTT database. | ||
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