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Overcomplete blind source separation by combining ICA and binary time-frequency masking (2005)
| Content Provider | CiteSeerX |
|---|---|
| Author | Pedersen, Michael Syskind Wang, Deliang Larsen, Jan Kjems, Ulrik |
| Description | A limitation in many source separation tasks is that the number of source signals has to be known in advance. Further, in order to achieve good performance, the number of sources cannot exceed the number of sensors. In many real-world applications these limitations are too strict. We propose a novel method for overcomplete blind source separation. Two powerful source separation techniques have been combined, independent component analysis and binary time-frequency masking. Hereby, it is possible to iteratively extract each speech signal from the mixture. By using merely two microphones we can separate up to six mixed speech signals under anechoic conditions. The number of source signals is not assumed to be known in advance. It is also possible to maintain the extracted signals as stereo signals. 1. |
| File Format | |
| Language | English |
| Publisher Date | 2005-01-01 |
| Publisher Institution | In: Proceedings of the MLSP workshop |
| Access Restriction | Open |
| Subject Keyword | Independent Component Analysis Source Signal Many Source Separation Task Anechoic Condition Extracted Signal Many Real-world Application Mixed Speech Signal Binary Time-frequency Masking Novel Method Overcomplete Blind Source Separation Good Performance Stereo Signal Speech Signal Powerful Source Separation Technique |
| Content Type | Text |
| Resource Type | Article |
