Wavelet-based voice morphing

This paper presents a new multi-scale voice morphing algorithm. This algorithm enables a user to transform one person's speech pattern into another person's pattern with distinct characteristics, giving it a new identity, while preserving the original content. The voice morphing algorithm performs the morphing at different subbands by using the theory of wavelets and models the spectral conversion using the theory of Radial Basis Function Neural Networks. The results obtained on the TIMIT speech database demonstrate effective transformation of the speaker identity

Voice morphing using the generative topographic mapping

In this paper we address the problem of Voice Morphing. We attempt to transform the spectral characteristics of a source speaker's speech signal so that the listener would believe that the speech was uttered by a target speaker. The voice morphing system transforms the spectral envelope as represented by a Linear Prediction model. The transformation is achieved by codebook mapping using the Generative Topographic Mapping, a non-linear, latent variable, parametrically constrained, Gaussian Mixture Model

Quality Assessment of Ambulatory ECG Using Wavelet Entropy of the HRV signal

Data in recordings obtained from ambulatory patients using wearable sensors are often corrupted by motion artefact and are, in general noisier, than data obtained from non-mobile patients. Identifying and ignoring erroneous measurements from these data is very important, if wearable sensors are to be incorporated into clinical practice. In this paper we propose a novel Signal Quality Index (SQI), intended to assess whether reliable heart rates (HR) can be obtained from a single channel of ECG collected from ambulatory patients, using wearable sensors. The proposed system is based on wavelet entropy measurements of the heart rate variability (HRV) signal. The system was trained and tested on expertly labeled data from a particular wearable sensor and was also tested on labeled data from a different sensor. The sensitivities and specificities achieved were 94% and 98%, respectively, on data from the same sensor as the training set, and 91% and 97%, respectively, on data from a different sensor, indicating the potential of the system to generalize across different sensors. Because the system relies on a single channel of ECG it has the potential for inclusion in applications using wearable sensors and in the most basic clinical environments

Ethnic differences in body fat distribution among Asian pre-pubertal children: A cross-sectional multicenter study

Background Ethnic differences in body fat distribution contribute to ethnic differences in cardiovascular morbidities and diabetes. However few data are available on differences in fat distribution in Asian children from various backgrounds. Therefore, the current study aimed to explore ethnic differences in body fat distribution among Asian children from four countries. Methods A total of 758 children aged 8-10 y from China, Lebanon, Malaysia and Thailand were recruited using a non-random purposive sampling approach to enrol children encompassing a wide BMI range. Height, weight, waist circumference (WC), fat mass (FM, derived from total body water [TBW] estimation using the deuterium dilution technique) and skinfold thickness (SFT) at biceps, triceps, subscapular, supraspinale and medial calf were collected. Results After controlling for height and weight, Chinese and Thai children had a significantly higher WC than their Lebanese and Malay counterparts. Chinese and Thais tended to have higher trunk fat deposits than Lebanese and Malays reflected in trunk SFT, trunk/upper extremity ratio or supraspinale/upper extremity ratio after adjustment for age and total body fat. The subscapular/supraspinale skinfold ratio was lower in Chinese and Thais compared with Lebanese and Malays after correcting for trunk SFT. Conclusions Asian pre-pubertal children from different origins vary in body fat distribution. These results indicate the importance of population-specific WC cut-off points or other fat distribution indices to identify the population at risk of obesity-related health problems

Metabolite profiling in retinoblastoma identifies novel clinicopathological subgroups

BACKGROUND: Tumour classification, based on histopathology or molecular pathology, is of value to predict tumour behaviour and to select appropriate treatment. In retinoblastoma, pathology information is not available at diagnosis and only exists for enucleated tumours. Alternative methods of tumour classification, using noninvasive techniques such as magnetic resonance spectroscopy, are urgently required to guide treatment decisions at the time of diagnosis. METHODS: High-resolution magic-angle spinning magnetic resonance spectroscopy (HR-MAS MRS) was undertaken on enucleated retinoblastomas. Principal component analysis and cluster analysis of the HR-MAS MRS data was used to identify tumour subgroups. Individual metabolite concentrations were determined and were correlated with histopathological risk factors for each group. RESULTS: Multivariate analysis identified three metabolic subgroups of retinoblastoma, with the most discriminatory metabolites being taurine, hypotaurine, total-choline and creatine. Metabolite concentrations correlated with specific histopathological features: taurine was correlated with differentiation, total-choline and phosphocholine with retrolaminar optic nerve invasion, and total lipids with necrosis. CONCLUSIONS: We have demonstrated that a metabolite-based classification of retinoblastoma can be obtained using ex vivo magnetic resonance spectroscopy, and that the subgroups identified correlate with histopathological features. This result justifies future studies to validate the clinical relevance of these subgroups and highlights the potential of in vivo MRS as a noninvasive diagnostic tool for retinoblastoma patient stratification

Multiscale voice morphing using radial basis function analysis

A new multiscale voice morphing algorithm using radial basis function (RBF) analysis is presented in this paper. The approach copes well with small training sets of high dimension, which is a problem often encountered in voice morphing. The aim of this algorithm is to transform one person's speech pattern so that it is perceived as if it was spoken by another speaker. The voice morphing system we propose assumes parallel training data from source and target speakers and uses the theory of wavelets in order to extract speaker feature information. The spectral conversion is modelled using RBF analysis. Independent listener tests demonstrate effective transformation of the perceived speaker identity

Data fusion for estimating respiratory rate from a single-lead ECG

Respiratory rate, an important antecedent of patient deterioration, is inadequately recorded by hospital staff, partially due to the absence of a reliable automated technique for measuring it. The ECG has been proposed by several authors in recent years as a source of reliable respiratory information. Most algorithms proposed use either respiratory sinus arrhythmia (RSA) or the R-peak amplitude (RPA) modulation of the ECG. In this paper, we propose a novel method for estimating respiratory rate from the ECG which fuses frequency information from the two methods. The method was evaluated on data from 40 young and elderly subjects and validated against a "gold standard" respiratory rate obtained from simultaneously recorded respiration data. The fusion method outperformed the RSA and RPA methods, giving a mean absolute error of 0.81 bpm for the young subject population and 0.84 bpm for the elderly, using 1-min windows of data. Unlike other algorithms, the technique does not underperform at the lower or higher respiratory rates. © 2012 Elsevier Ltd