Thierry Chonavel

MIMO communications for inhome PLC networks: Measurements and results up to 100 MHz

Power Line Communications (PLC) is used for information exchange over the lines installed for delivering the electrical power. Inhome PLC is a technology which delivers telecom services to every corner of a household through already existing electrical wiring. In recent years, PLC has emerged as a potential candidate for domestic high bit rate services. The current inhome PLC technology, based on Single-Input Single-Output (SISO) configuration, under achieves the capacity offered by the physical PLC channel. The inhome PLC channel offers multiple signal feed ports as, usually, it comprises of three wires: Phase, Neutral and Protective Earth. The measurements and results presented in this paper demonstrate that up to 90% enhancement in inhome PLC channel capacity is possible by using multiple-input multiple-output (MIMO) technique.

Voice Conversion Using Dynamic Frequency Warping With Amplitude Scaling, for Parallel or Nonparallel Corpora

In Voice Conversion (VC), the speech of a source speaker is modified to resemble that of a particular target speaker. Currently, standard VC approaches use Gaussian mixture model (GMM)-based transformations that do not generate high-quality converted speech due to “over-smoothing” resulting from weak links between individual source and target frame parameters. Dynamic Frequency Warping (DFW) offers an appealing alternative to GMM-based methods, as more spectral details are maintained in transformation; however, the speaker timbre is less successfully converted because spectral power is not adjusted explicitly. Previous work combines separate GMM- and DFW-transformed spectral envelopes for each frame. This paper proposes a more effective DFW-based approach that (1) does not rely on the baseline GMM methods, and (2) functions on the acoustic class level. To adjust spectral power, an amplitude scaling function is used that compares the average target and warped source log spectra for each acoustic class. The proposed DFW with Amplitude scaling (DFWA) outperforms standard GMM and hybrid GMM-DFW methods for VC in terms of both speech quality and timbre conversion, as is confirmed in extensive objective and subjective testing. Furthermore, by not requiring time-alignment of source and target speech, DFWA is able to perform equally well using parallel or nonparallel corpora, as is demonstrated explicitly.

Blind marine seismic deconvolution using statistical MCMC methods

In order to improve the resolution of seismic images, a blind deconvolution of seismic traces is necessary, since the source wavelet is not known and cannot be considered as a stationary signal. The reflectivity sequence is modeled as a Gaussian mixture, depending on three parameters (high and low reflector variances and reflector density), on the wavelet impulse response, and on the observation noise variance. These parameters are unknown and must be estimated from the recorded trace, which is the reflectivity convolved with the wavelet, plus noise. Two methods are compared in this paper for the parameter estimation. Since we are considering an incomplete data problem, we first consider maximum likelihood estimation by means of a stochastic expectation maximization (SEM) method. Alternatively, proper prior distributions can be specified for all unknown quantities. Then, a Bayesian strategy is applied, based on a Monte Carlo Markov Chain (MCMC) method. Having estimated the parameters, one can proceed to the deconvolution. A maximum posterior mode (MPM) criterion is optimized by means of an MCMC method. The deconvolution capability of these procedures is checked first on synthetic signals and then on the seismic data of the IFREMER ESSR4 campaign, where the wavelet duration blurs the reflectivity, and on the SMAVH high-resolution marine seismic data.

Generalized cumulative residual entropy for distributions with unrestricted supports

We consider the cumulative residual entropy (CRE) a recently introduced measure of entropy. While in previous works distributions with positive support are considered, we generalize the definition of CRE to the case of distributions with general support. We show that several interesting properties of the earlier CRE remain valid and supply further properties and insight to problems such as maximum CRE power moment problems. In addition, we show that this generalized CRE can be used as an alternative to differential entropy to derive information-based optimization criteria for system identification purpose.

A New Method for Speech Synthesis and Transformation Based on an ARX-LF Source-Filter Decomposition and HNM Modeling

In this paper a new method for speech synthesis is proposed. It relies on a source-filter decomposition of the speech signal by means of an ARX-LF model. This model allows the representation of the glottal signal as the sum of an LF waveform and a residual signal. The residual information is then analyzed by HNM. This signal representation enables high quality speech modification such as pitch, duration or even voice quality transformation. Experiments performed on a real speech database show the relevance of the proposed method as compared to other existing approaches.

Analysis of multicomponent LFM signals by Teager Huang-Hough transform

A novel detection approach of linear FM (LFM) signals, with single or multiple components, in the time-frequency plane of Teager-Huang (TH) transform is presented. The detection scheme that combines TH transform and Hough transform is referred to as Teager-Huang-Hough (THH) transform. The input signal is mapped into the time-frequency plane by using TH transform followed by the application of Hough transform to recognize time-frequency components. LFM components are detected and their parameters are estimated from peaks and their locations in the Hough space. Advantages of THH transform over Hough transform of Wigner-Ville distribution (WVD) are: 1) cross-terms free detection and estimation, and 2) good time and frequency resolutions. No assumptions are made about the number of components of the LFM signals and their models. THH transform is illustrated on multicomponent LFM signals in free and noisy environments and the results compared with WVD-Hough and pseudo-WVD-Hough transforms.

Fast adaptive eigenvalue decomposition: a maximum likelihood approach

In this paper, we address the problem of adaptive eigenvalue decomposition (EVD). We propose a new approach, based on the optimization of the log-likelihood criterion. The parameters of the log-likelihood to be estimated are the eigenvectors and the eigenvalues of the data covariance matrix. They are actualized by means of a stochastic algorithm that requires little computational cost. Furthermore, the particular structure of the algorithm, that we named MALASE, ensures the orthonormality of the estimated basis of eigenvectors at each step of the algorithm. MALASE algorithm shows strong links with many Givens rotation based update algorithms that we discuss. We consider convergence issues for MALASE algorithm and give the expression of the asymptotic covariance matrix of the estimated parameters. The practical interest of the proposed method is shown on examples.

Analysis and modeling of background noise for inhome MIMO PLC channels

In the recent years, the Multiple-Input Multiple-Output (MIMO) techniques have emerged as an important research field for enhancing the throughput of in-home Power Line Communication (PLC) systems by exploiting the additional Protective Earth wire. The development of such systems requires an accurate description of the noise in the propagation channel. This paper presents two statistical models for the background noise found in MIMO PLC channels, based on noise measurements performed in five houses. In the proposed models, the spectral characteristics of MIMO background noise are presented, in 2-150 MHz range, following two existing formalisms for Single-Input Single-Output (SISO) PLC noise.

A Time-Domain Model of Background Noise for In-Home MIMO PLC Networks

Multiple-input multiple-output techniques have recently become an important research field for enhancing the performance of in-home power-line communication (PLC) systems by exploiting the additional protective earth wire. The development of such systems requires an accurate description of the channel noise. In this paper, we have presented a model for PLC background noise based on an extensive set of measurements. We have adopted the framework of the multivariate time series to model the PLC background noise. This paper employs the vector autoregressive modeling technique to extract noise model parameters from the measured noise. We have verified the accuracy of the noise model by comparing time- and frequency-domain correlation of measured and modeled noise.

77 GHz ACC radar simulation platform

The development of a system simulation platform for adaptive cruise control (ACC) radar working at 77 GHz is presented. The simulation platform allows us to test different radar architectures, modulation formats and detection algorithms as well as to simulate different scenarios, which improves the decision-making before and during the hardware development.