A neural net for blind separation of nonstationary signals
Neural Networks
Convolutive blind separation of speech mixtures using the natural gradient
Speech Communication - Special issue on speech processing for hearing aids
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
Filter-and-sum beamformer with adjustable filter characteristics
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 05
EURASIP Journal on Applied Signal Processing
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Non-negative hidden Markov modeling of audio with application to source separation
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Blind separation of speech mixtures via time-frequency masking
IEEE Transactions on Signal Processing
Speech Enhancement, Gain, and Noise Spectrum Adaptation Using Approximate Bayesian Estimation
IEEE Transactions on Audio, Speech, and Language Processing
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Blind source separation (BSS) method is one of the newest multisensorial methods that exploits statistical properties of simultaneously recorded independent signals to separate them out. The objective of this method is similar to that of beamforming, namely a set of spatial filters that separate source signals are calculated. Thus, it seems to be reasonable to investigate the spatial efficiency of BSS that is reported in this study. A dummy head with two microphones was used to record two signals in an anechoic chamber: target speech and babble noise in different spatial configurations. Then the speech reception thresholds (SRTs, i.e. signal-to-noise ratio, SNR yielding 50% speech intelligibility) before and after BSS algorithm (Parra and Spence, 2000) were determined for audiologically normal subjects. A significant speech intelligibility improvement was noticed after the BSS was applied. This happened in most cases when the target and masker sources were spatially separated. Moreover, the comparison of objective (SNR enhancement) and subjective (intelligibility improvement) assessment methods is reported here. It must be emphasized that these measures give different results.