Multilingual patients pose a unique challenge when planning epilepsy surgery near language cortex because the cortical representations of each language may be distinct. These distinctions may not be evident with routine electrocortical stimulation mapping (ESM). Electrocorticography (ECoG) has recently been used to detect task-related spectral perturbations associated with functional brain acti...

This paper demonstrates the potential of a two-dimensional (2D) gradient mapping technique that utilized the eigenvalue manipulating transformation (EMT) of the spectral data set. The EMT technique, by lowering the power of a set of eigenvalues associated with the original data, enhances the contributions of minor principle components (PCs). The operation converts the original spectral data set...

Accent morphing aims to modify the accent of a speaker whilst maintaining speaker identity. A textindependent approach could be based on voice conversion systems which manipulate speaker identity through spectral mapping. However, it is not clear to what extent accent changes can be captured with spectral mapping alone. In this paper we implement and evaluate a text-dependent accent morphing sy...

We describe a method of processing hyperspectral images of natural scenes that uses a combination of kmeans clustering and locally linear embedding (LLE). The primary goal is to assist anomaly detection by preserving spectral uniqueness among the pixels. In order to reduce redundancy among the pixels, adjacent pixels which are spectrally similar are grouped using the k-means clustering algorith...

In this paper, a novel and noise robust front-end based on the combined application of spectral subtraction, spectral flooring and cumulative distribution mapping is proposed. Recognition experiments with the Aurora II connected digits reveal that the proposed front-end achieves an average digit accuracy of 81.46% for a model set trained from clean data and 89.54% for a model set trained from d...

Automatic speech recognition (ASR) systems suffer from performance degradation under noisy and reverberant conditions. In this work, we explore a deep neural network (DNN) based approach for spectral feature mapping from corrupted speech to clean speech. The DNN based mapping substantially reduces interference and produces estimated clean spectral features for ASR training and decoding. We expe...

We present a method for generative modeling of audio content that performs mappings between minimum entropy hidden Markov models learnt from audio data. By training with a minimum entropy prior, compact, low-complexity models of the latent structure in audio source samples are obtained. Synthesis of new audio content is achieved by mapping the state sequence of a nominated structure model onto ...

Previous work using neutron spectrometer data and earth-based 70 cm radar has proven useful in mapping TiO2 on the lunar surface but with coarse resolution. Spectral data from the Clementine mission attempted to map TiO2 at higher resolution but accuracy was low. With new 12.5 cm Mini-RF radar data from the Lunar Reconnaissance Orbiter I attempted to build a higher resolution TiO2 map by compar...

Using a linear unconstrained least squares (LSS) method and a non-linear artificial neural network (ANN) algorithm, we conducted a spectral mixture analysis to the Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) image data in Yokohama city, Japan, for mapping the abundance of the urban surface components. ASTER is a newly developed research facility instrument. The spect...

We review the morphological and spectral energy distribution characteristics of the dust continuum emission (emitted in the 40-200μm spectral range) from normal galaxies, as revealed by detailed ISOPHOT mapping observations of nearby spirals and by ISOPHOT observations of the integrated emissions from representative statistical samples in the local universe.