Results 151 to 160 of about 191,485 (185)

Blind Separation of Acoustic Signals

2001
This chapter presents an overview of criteria and algorithms for the blind separation of linearly mixed acoustic signals. Particular attention is paid to the underlying statistical formulations of various approaches to the convolutive blind signal separation task, and comparisons to other blind inverse problems are made.
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Simultaneous blind signal separation and denoising

2008 International Conference on Computer Engineering & Systems, 2008
This paper deals with the problem of blind separation of denoising. It proposes the application of the discrete wavelet transform as a preprocessing step of denoising prior to the blind separation algorithm. The separation of sinusoidal signals as well as speech signals in a noisy environment is studied with and without the use of the wavelet denoising
H. Hammam, A. E. Abu El-Azm, M. E. Elhalawany, F. E. Abd El-Samie   +3 more
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Blind Signal Separation Algorithm Evaluation

2001
Recently, many new Blind Signal Separation (BSS) algorithms have been introduced. Authors evaluate the performance of their algorithms in various ways. Among these are speech recognition rates, plots of separated signals, plots of cascaded mixinglunmixing impulse responses and signal to noise ratios.
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Blind Signal Separation with Speech Enhancement

2013
A new speech enhancement architecture using convolutive blind signal separation (CBSS) and subspace-based speech enhancement is presented. The spatial and spectral information are integrated to enhance the target speech signal and suppress both interference noise and background noise.
Chang-Hong Lin, Hsiao-Ping Lee, Jyun-Hong Li, Chih-Wei Su, Yu-Hao Chin, Jhing-Fa Wang, Jia-Ching Wang   +6 more
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Blind Signal Separation

2004
This thesis addresses the blind signal separation (BSS) problem. The essence of the BSS problem is to recover a set of source signals from a group of sensor observations. These observations can be modeled as instantaneous or convolutive mixtures of the sources.
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‘Signal Subspace’ Blind Source Separation Applied to Fetal Magnetocardiographic Signals Extraction

2004
In this paper we apply Independent Component Analysis to magnetocardiographic data recorded from the abdomen of pregnant women. In particular, we include a dimensionality reduction in the ’Cumulant Based Iterative Inversion’ algorithm to achieve a ’signal subspace’ subdivision, which enhances the algorithm’s efficacy in resolving the signals of ...
Barbati G, Porcaro C, Salustri C
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Blind separation of signal sources

2008
Blind source separation (BSS) is a field developed in signal processing and neural network communities over last 15-20 years. It found numerous applications in science and engineering such as acoustics, biomedical signal analysis, communications, image segmentation and deconvolution, spectroscopy, bioinformatics, chemometrics, etc.
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Recovery of Peripheral Nerve Signals Through Blind Separation

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007
Patients with neurological disorders can regain lost motor function through functional electrical stimulation (FES). In closed-loop prosthetic devices, neural signals can be obtained using cuff electrodes which have been shown to be stable for long-term recordings.
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9 Blind Signal Separation

2022
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Blind Signal Separation II

2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, 2006
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