We propose a fully Bayesian approach for generalized kernel models (GKMs), which are extensions of generalized linear models in the feature space induced by a reproducing kernel. ...
Zhihua Zhang, Guang Dai, Donghui Wang, Michael I. ...
We consider the problem of learning density mixture models for classification. Traditional learning of mixtures for density estimation focuses on models that correctly represent t...
This paper presents a maximum likelihood approach to multiple fundamental frequency (F0) estimation for a mixture of harmonic sound sources, where the power spectrum of a time fra...
We systematically compare five representative state-of-theart methods for estimating query language models with pseudo feedback in ad hoc information retrieval, including two var...
This paper considers the enhancement of noisy speech. Earlier studies have revealed that an approach that enhances spectral envelopes by using prior knowledge about the all-pole (...
Takuya Yoshioka, Tomohiro Nakatani, Hiroshi G. Oku...