Open AccessProceedings Article
Source Separation with a Sensor Array using Graphical Models and Subband Filtering
Hagai Attias
- 01 Jan 2002
- Vol. 15, pp 1229-1236
TL;DR: New separation algorithms which are based on probabilistic graphical models with latent variables, which use subband filtering ideas to model the reverberant environment, and employ an explicit model for background and sensor noise are described.
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Abstract: Source separation is an important problem at the intersection of several fields, including machine learning, signal processing, and speech technology. Here we describe new separation algorithms which are based on probabilistic graphical models with latent variables. In contrast with existing methods, these algorithms exploit detailed models to describe source properties. They also use subband filtering ideas to model the reverberant environment, and employ an explicit model for background and sensor noise. We leverage variational techniques to keep the computational complexity per EM iteration linear in the number of frames.
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Citations
A survey of convolutive blind source separation methods
Michael Syskind Pedersen,Jan Larsen,Ulrik Kjems,Lucas C. Parra +3 more
- 01 Jan 2007
TL;DR: A taxonomy is provided, wherein many of the existing algorithms for blind source separation of convolutive audio mixtures can be organized, and results from those algorithms that have been applied to real-world audio separation tasks are presented.
Blind source separation in mobile environments using a priori knowledge
Erik Visser,Te-Won Lee +1 more
- 17 May 2004
TL;DR: A speech enhancement scheme including blind source separation and background denoising based on minimum statistics is studied in mobile environments and shown to outperform conventional beamforming and single channelDenoising techniques as well as an equivalent scheme with fixed output channel selection.
19
Variational Probabilistic Speech Separation Using Microphone Arrays
TL;DR: For the problem of separating delayed additive noise corrupted speech mixtures, the algorithm is able to improve upon the signal-to-noise ratio (SNR) gain performance of existing state-of-the-art probabilistic and TDOA-based speech separation algorithms by over 10 dB.
18
Source Separation for Hearing Aid Applications
Michael Syskind Pedersen,Jan Larsen +1 more
- 01 Nov 2006
TL;DR: Experiments have shown that the performance of instantaneous gradient flow beamforming by Cauwenberghs et al. is reduced significantly in reverberant conditions, so by expanding the gradient flow principle to convolutive mixtures, separation in a reverberant environment is possible.
13
•Dissertation
Graphical models for robust speech recognition in adverse environments
Steven J. Rennie
- 01 Jan 2008
TL;DR: A collection of new graphical models and inference algorithms for robust speech recognition and the Iroquois system for multi-talker speech separation and recognition is presented, marking a significant first in automatic speech recognition, and a milestone in computing.
6
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Adaptive blind signal and image processing
Andrzej Cichocki,Shun-ichi Amari +1 more
- 01 Jan 2002
TL;DR: Find the secret to improve the quality of life by reading this adaptive blind signal and image processing and make the words as your good value to your life.
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Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Andrzej Cichocki,Shun-ichi Amari +1 more
- 01 Sep 2002
TL;DR: This volume unifies and extends the theories of adaptive blind signal and image processing and provides practical and efficient algorithms for blind source separation, Independent, Principal, Minor Component Analysis, and Multichannel Blind Deconvolution (MBD) and Equalization.
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Independent factor analysis
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