Results 241 to 250 of about 2,361,330 (284)

On underdetermined source separation

1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), 1999
This paper discusses some theoretical results on underdetermined source separation, i.e. when the mixing matrix is degenerate, especially when there is more sources than observations. In this case, we show that the sources can be restored up to an arbitrary additive random vector.
Anisse Taleb, Christian Jutten
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Noise source separation based on the blind source separation

2011 Chinese Control and Decision Conference (CCDC), 2011
The identification of acoustic source accurately is a fundamental problem in noise control. In the practical project, if the contribution of multi-source-noise to the whole was identified, and then the noise level can be reduced accordingly. To get the accurate noise signal, measurements should be possible while the machines are constantly in action ...
Yang Yang, Zuoli Li, Xiuqin Wang, Di Zhang   +3 more
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Geometric source separation: merging convolutive source separation with geometric beamforming

IEEE Transactions on Speech and Audio Processing, 2002
Convolutive blind source separation and adaptive beamforming have a similar goal-extracting a source of interest (or multiple sources) while reducing undesired interferences. A benefit of source separation is that it overcomes the conventional cross-talk or leakage problem of adaptive beamforming.
Lucas C. Parra, Christopher V. Alvino
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Nonsymmetrical contrasts for sources separation

IEEE Transactions on Signal Processing, 1999
In this paper, the problem of the blind separation of independent sources is considered. Our approach relies on high-order inverse criteria. After generalizing the definition of classical contrast functions, we exhibit a wide class of generally nonsymmetrical functions that will be called "generalized contrasts" and whose maximization is proved to be a
Eric Moreau, Nadège Thirion-Moreau
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Designing multichannel source separation based on single-channel source separation

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015
In this paper, an extension of independent vector analysis (IVA), model-based IVA, is proposed for multichannel source separation. For obtaining better source models, we introduce a single-channel source separation method, and utilize the outputs as source variances in time-frequency-variant Gaussian source model. The demixing matrices are estimated in
Ana Ramírez López, Nobutaka Ono, Ulpu Remes, Kalle J. Palomäki, Mikko Kurimo   +4 more
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Sound Source Separation Apparatus And Sound Source Separation Method

The Journal of the Acoustical Society of America, 2011
To shorten an output delay while a high sound source separation performance is ensured when a sound separation process based on an ICA method is performed. A second Fourier transform process execution cycle t2 for obtaining a second frequency-domain signal S1 used as an input signal of a filter process is set shorter than a first Fourier transform ...
Takashi Hiekata, Yohei Ikeda
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Separation of Unknown Number of Sources

IEEE Signal Processing Letters, 2014
We address the problem of blind source separation in acoustic applications where there is no prior knowledge about the number of mixing sources. The presented method employs a mixture of complex Watson distributions in its generative model with a sparse Dirichlet distribution over the mixture weights.
Jalil Taghia, Arne Leijon
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On the stability of source separation algorithms

Neural Networks for Signal Processing VIII. Proceedings of the 1998 IEEE Signal Processing Society Workshop (Cat. No.98TH8378), 2000
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Bayesian nonstationary source separation

Neurocomputing, 2008
A Bayesian nonstationary source separation algorithm is proposed in this paper to recover nonstationary sources from noisy mixtures. In order to exploit the temporal structure of the data, we use a time-varying autoregressive (TVAR) process to model each source signal.
Qinghua Huang, Jie Yang 0002, Yue Zhou 0005   +2 more
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Equivariant nonstationary source separation

Neural Networks, 2002
Most of source separation methods focus on stationary sources, so higher-order statistics is necessary for successful separation, unless sources are temporally correlated. For nonstationary sources, however, it was shown [Neural Networks 8 (1995) 411] that source separation could be achieved by second-order decorrelation. In this paper, we consider the
Seungjin Choi, Andrzej Cichocki, Shun-ichi Amari   +2 more
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