Topographic Independent Component Analysis
Neural Computation
IEEE Transactions on Signal Processing
Estimation of the information by an adaptive partitioning of the observation space
IEEE Transactions on Information Theory
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Determining mixing parameters from multispeaker data using speech-specific information
IEEE Transactions on Audio, Speech, and Language Processing
Blind audio source separation using sparsity based criterion for convolutive mixture case
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Noise speech wavelet analyzing in special time ranges
ICACT'10 Proceedings of the 12th international conference on Advanced communication technology
Noise wavelet evaluating near to zero by thresholding method analyzing
ICCC'11 Proceedings of the 2011 international conference on Computers and computing
Hi-index | 0.08 |
Blind Signal Separation (BSS) techniques are commonly employed in the separation of speech signals, using Independent Component Analysis (ICA) as the criterion for separation. This paper investigates the viability of employing ICA for real-time speech separation (where short frame sizes are the norm). The relationship between the statistics of speech and the assumption of statistical independence (at the core of ICA) is examined over a range of frame sizes. The investigation confirms that statistical independence is not a valid assumption for speech when divided into the short frames appropriate to real-time separation. This is primarily due to the quasi-stationary nature of speech over the temporal short term. We conclude that employing ICA for real-time speech separation will always result in limited performance due to a fundamental failure to meet the strict assumptions of ICA.