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NIPS
2000
15 years 1 months ago
Speech Denoising and Dereverberation Using Probabilistic Models
This paper presents a unified probabilistic framework for denoising and dereverberation of speech signals. The framework transforms the denoising and dereverberation problems into...
Hagai Attias, John C. Platt, Alex Acero, Li Deng
TASLP
2002
111views more  TASLP 2002»
14 years 11 months ago
Speech enhancement using a mixture-maximum model
We present a spectral domain, speech enhancement algorithm. The new algorithm is based on a mixture model for the short time spectrum of the clean speech signal, and on a maximum a...
David Burshtein, Sharon Gannot
ICRA
2009
IEEE
100views Robotics» more  ICRA 2009»
14 years 9 months ago
Multi-robot plan adaptation by constrained minimal distortion feature mapping
We propose a novel method for multi-robot plan adaptation which can be used for adapting existing spatial plans of robotic teams to new environments or imitating collaborative spat...
Bálint Takács, Yiannis Demiris
ICASSP
2011
IEEE
14 years 3 months ago
Detection of sinusoidal signals in noise by probabilistic modelling of the spectral magnitude shape and phase continuity
This paper presents a method for detection of sinusoidal signals corrupted by an additive noise in the short-time Fourier domain. The proposed method is based on probabilistic mod...
Peter Jancovic, Münevver Köküer
CVPR
2012
IEEE
13 years 2 months ago
Robust Boltzmann Machines for recognition and denoising
While Boltzmann Machines have been successful at unsupervised learning and density modeling of images and speech data, they can be very sensitive to noise in the data. In this pap...
Yichuan Tang, Ruslan Salakhutdinov, Geoffrey E. Hi...