Blind separation of sources, Part II: problems statement
Signal Processing
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
A neural net for blind separation of nonstationary signals
Neural Networks
On-line Convolutive Blind Source Separation of Non-Stationary Signals
Journal of VLSI Signal Processing Systems
Second Order Nonstationary Source Separation
Journal of VLSI Signal Processing Systems
Convolutive blind separation of speech mixtures using the natural gradient
Speech Communication - Special issue on speech processing for hearing aids
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Blind source separation in frequency domain
Signal Processing
Blind Source Separation of Convolutive Mixtures of Speech in Frequency Domain
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Speech enhancement based on a priori signal to noise estimation
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 02
Blind source separation combining independent component analysis and beamforming
EURASIP Journal on Applied Signal Processing
A frequency domain blind signal separation method based ondecorrelation
IEEE Transactions on Signal Processing
Semi-blind suppression of internal noise for hands-free robot spoken dialog system
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Blind source separation algorithm based on PSO and algebraic equations of order two
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
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The present study is concerned with the blind source separation (BSS) of speech and speech-shaped noise sources. All recordings were carried out in an anechoic chamber using a dummy head (two microphones, one in each ear). The program which implements the algorithm for BSS of convolutive mixtures introduced by Parra and Spence [Parra, L., Spence, C., 2000a. Convolutive blind source separation of non-stationary sources. IEEE Trans. Speech Audio Process. 8(3), 320-327 (US Patent US6167417)] was used to separate out the signals. In the postprocessing phase two different denoising algorithms were used. The first was based on a minimum mean-square error log-spectral amplitude estimator [Ephraim, E., Malah, D., 1985. Speech enhancement using a minimum mean-square error log-spectral amplitude estimator. IEEE Trans. Speech Audio Process. ASSP-33(2), 443-445], while the second one was based on Wiener filter in which the concept of an a priori signal-to-noise estimation presented by Ephraim (as mentioned above) was applied [Scalart, P., Filho, J.V., 1996. Speech enhancement based on a priori signal to noise estimation. IEEE Internat. Conf. Acoust. Speech Signal Process. 1, 629-632]. Non-sense word tests were used as a target speech in both cases while one or two disturbing sources were used as interferences. The speech intelligibility before and after the BSS was measured for three subjects with audiologically normal hearing. Next the speech signal after BSS was denoised and presented to the same listeners. The results revealed some ambiguities caused by the insufficient number of microphones compared to the number of sound sources. For one disturbance only, the intelligibility improvement was significant. However, when there were two disturbances and the target speech, the separation was much poorer. The additional denoising, as could be expected, raises the intelligibility slightly. Although the BSS method requires more research on optimization, the results of the investigation imply that it may be applied to hearing aids in the future.