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» Source Separation with Gaussian Process Models
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ICASSP
2010
IEEE
13 years 7 months ago
Statistical approach to enhancing esophageal speech based on Gaussian mixture models
This paper presents a novel method of enhancing esophageal speech using statistical voice conversion. Esophageal speech is one of the alternative speaking methods for laryngectome...
Hironori Doi, Keigo Nakamura, Tomoki Toda, Hiroshi...
SDM
2008
SIAM
105views Data Mining» more  SDM 2008»
13 years 9 months ago
Gaussian Process Learning for Cyber-Attack Early Warning
Network security has been a serious concern for many years. For example, firewalls often record thousands of exploit attempts on a daily basis. Network administrators could benefi...
Jian Zhang 0004, Phillip A. Porras, Johannes Ullri...
ICASSP
2010
IEEE
13 years 7 months ago
Energy efficient lossy transmission over sensor networks with feedback
The energy-distortion function (E(D)) for a network is defined as the minimum total energy required to achieve a target distortion D at the receiver without putting any restricti...
Aman Jain, Deniz Gündüz, Sanjeev R. Kulk...
ICASSP
2011
IEEE
12 years 11 months ago
Non-parallel training for voice conversion based on FT-GMM
This paper presents a non-parallel training algorithm for voice conversion based on feature transform Gaussian mixture model (FTGMM), which is a mixture model of joint density spa...
Ling-Hui Chen, Zhen-Hua Ling, Li-Rong Dai
ICA
2012
Springer
12 years 3 months ago
A Non-negative Approach to Language Informed Speech Separation
Abstract. The use of high level information in source separation algorithms can greatly constrain the problem and lead to improved results by limiting the solution space to semanti...
Gautham J. Mysore, Paris Smaragdis