Blind separation and deconvolution of MIMO system driven by colored inputs using SIMO-model-based ICA with information-geometric learning
- Resource Type
- Conference
- Authors
- Saruwatari, H.; Yamajo, H.; Takatani, T.; Nishikawa, T.; Shikano, K.
- Source
- 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718) Neural networks for signal processing Neural Networks for Signal Processing, 2003. NNSP'03. 2003 IEEE 13th Workshop on. :379-388 2003
- Subject
- Computing and Processing
Components, Circuits, Devices and Systems
Signal Processing and Analysis
Deconvolution
MIMO
Independent component analysis
Speech
Microphones
Finite impulse response filter
Filtering
Signal processing
Information science
Electronic mail
- Language
- ISSN
- 1089-3555
We propose a new two-stage blind separation and deconvolution algorithm for multiple-input multiple-output (MIMO)- FIR system driven by colored sound sources, in which a new single-input multiple-output (SIMO)-model-based ICA (SIMO-ICA) and blind multichannel inverse filtering are combined. SIMO-ICA can separate the mixed signals, not into monaural source signals but into SIMO-model-based signals from independent sources. After SIMO-ICA, a simple blind deconvolution technique for the SIMO model can be applied even when each source signal is temporally correlated. The simulation results reveal that the proposed algorithm can successfully achieve the separation and deconvolution for a convolutive mixture of speech.