Estimación de la matriz de mezclas en separación ciega de fuentes indeterminada con un número arbitrario de fuentes

Blind source separation consists on estimating n source signals from m measurements generated through an unknown mixing process of the sources. In the underdetermined case where we have more sources than measurements, we divide the problem into two stages: estimation of the mixing matrix and inversion of the linear problem. This paper deals with the first stage. It is well known that when the sparsity of the sources premise is true, measurements tend to align with the columns of the mixing matrix, so the problem can be formulated as estimating the peaks of multidimensional probability density functions (PDF). In this paper we analyze two different techniques to estimate this peaks: one is to convert the multidimensional PDF into the power spectral density (PSD) of multiple complex sinusoidal signals and use different multidimensional espectral estimation techniques to detect the peaks. The other is to convert the (m − 1)- multidimensional PDF to m − 1 unidimensional projections and estimate the peaks of these

Design of an embedded speech-centric interface for applications in handheld terminals

The embedded speech-centric interface for handheld wireless devices has been implemented on a commercially available PDA as a part of an application that allows real-time access to stock prices through GPRS. In this article, we have focused mainly in the optimization of the ASR subsystem for minimizing the use of the handheld computational resources. This optimization has been accomplished through the fixed-point implementation of all the algorithms involved in the ASR subsystem and the use of PCA to reduce the feature vector dimensionality. The influence of several parameters, such as the Qn resolution in the fixed-point implementation and the number of PCA components retained, have been studied and evaluated in the ASR subsystem, obtaining word recognition rates of around 96% for the best configuration. Finally, a field evaluation of the system has been performed showing that our design of the speech centric interface achieved good results in a real-life scenario.This work was supported in part by the Spanish Government grants TSI-020110-2009-103, IPT-120000-2010-24, and TEC2011-26807 and the Spanish Regional grant CCG08-UC3M/TIC-4457

FINDING MAXIMUM MARGIN SEGMENTS IN SPEECH

Maximum margin clustering (MMC) is a relatively new and promising kernel method. In this paper, we apply MMC to the task of unsupervised speech segmentation. We present three automatic speech segmentation methods based on MMC, which are tested on TIMIT and evaluated on the level of phoneme boundary detection. The results show that MMC is highly competitive with existing unsupervised methods for the automatic detection of phoneme boundaries. Furthermore, initial analyses show that MMC is a promising method for the automatic detection of sub-phonetic information in the speech signal. Index Terms — speech processing, clustering methods, unsupervised learning. 1