Model M, a novel class-based exponential language model, has been shown to significantly outperform word n-gram models in state-of-the-art machine translation and speech recognit...
In this paper, we present automated methods for estimating note intensities in music recordings. Given a MIDI file (representing the score) and an audio recording (representing a...
Spectral voice conversion is usually performed using a single model selected in order to represent a tradeoff between goodness of fit and complexity. Recently, we proposed a new ...
This paper consider the problem of how to evaluate the efficiency of a 3D continuous functional HRTF model in representing measured data. The proposed method is based on Karhunen...
Mengqiu Zhang, Rodney A. Kennedy, Thushara D. Abha...
We propose a kernelized maximal-figure-of-merit (MFoM) learning approach to efficiently training a nonlinear model using subspace distance minimization. In particular, a fixed,...
We present learning and inference algorithms for a versatile class of partially observed vector autoregressive (VAR) models for multivariate time-series data. VAR models can captu...
This paper proposes a Bayesian algorithm to estimate the parameters of a smooth transition regression model. With in this model, time series are divided into segments and a linear...
In this paper we present a new compressed sensing model and reconstruction method for multi-detector signal acquisition. We extend the concept of the famous single-pixel camera to...
Torsten Edeler, Kevin Ohliger, Stephan Hussmann, A...
— In this paper, we propose a new on-line learning algorithm for the non-linear system identification: the swarm intelligence aided multi-innovation recursive least squares (SIM...
Handling intra-personal variation is a major challenge in face recognition. It is difficult how to appropriately measure the similarity between human faces under significantly di...