108 research outputs found
Blind channel deconvolution of real world signals using source separation techniques
In this paper we present a method for blind deconvolution of linear
channels based on source separation techniques, for real word signals. This
technique applied to blind deconvolution problems is based in exploiting not
the spatial independence between signals but the temporal independence between
samples of the signal. Our objective is to minimize the mutual information
between samples of the output in order to retrieve the original signal. In
order to make use of use this idea the input signal must be a non-Gaussian i.i.d.
signal. Because most real world signals do not have this i.i.d. nature, we will
need to preprocess the original signal before the transmission into the channel.
Likewise we should assure that the transmitted signal has non-Gaussian statistics
in order to achieve the correct function of the algorithm. The strategy used
for this preprocessing will be presented in this paper. If the receiver has the inverse
of the preprocess, the original signal can be reconstructed without the
convolutive distortion
Nonlinear prediction based on score function
The linear prediction coding of speech is based in the
assumption that the generation model is autoregresive. In
this paper we propose a structure to cope with the
nonlinear effects presents in the generation of the speech
signal. This structure will consist of two stages, the first
one will be a classical linear prediction filter, and the
second one will model the residual signal by means of
two nonlinearities between a linear filter. The coefficients
of this filter are computed by means of a gradient search
on the score function. This is done in order to deal with
the fact that the probability distribution of the residual
signal still is not gaussian. This fact is taken into account
when the coefficients are computed by a ML estimate.
The algorithm based on the minimization of a high-order
statistics criterion, uses on-line estimation of the residue
statistics and is based on blind deconvolution of Wiener
systems [1]. Improvements in the experimental results
with speech signals emphasize on the interest of this
approach
A Robust Multiple Feature Approach To Endpoint Detection In Car Environment Based On Advanced Classifiers
In this paper we propose an endpoint detection system based on the
use of several features extracted from each speech frame, followed by a robust
classifier (i.e Adaboost and Bagging of decision trees, and a multilayer perceptron)
and a finite state automata (FSA). We present results for four different
classifiers. The FSA module consisted of a 4-state decision logic that filtered
false alarms and false positives. We compare the use of four different classifiers
in this task. The look ahead of the method that we propose was of 7 frames,
which are the number of frames that maximized the accuracy of the system.
The system was tested with real signals recorded inside a car, with signal to
noise ratio that ranged from 6 dB to 30dB. Finally we present experimental results
demonstrating that the system yields robust endpoint detection
Non-Linear and Non-Conventional Speech Processing: Alternative Techniques
This special issue aims to cover some problems related to non-linear and nonconventional
speech processing. The origin of this volume is in the ISCA Tutorial and
Research Workshop on Non-Linear Speech Processing, NOLISP’09, held at the
Universitat de Vic (Catalonia, Spain) on June 25–27, 2009. The series of NOLISP
workshops started in 2003 has become a biannual event whose aim is to discuss
alternative techniques for speech processing that, in a sense, do not fit into
mainstream approaches. A selected choice of papers based on the presentations
delivered at NOLISP’09 has given rise to this issue of Cognitive Computation
A regional perspective on the accuracy of machine learning forecasts of tourism demand based on data characteristics
In this work we assess the role of data characteristics in the accuracy of machine learning (ML) tourism forecasts from a spatial perspective. First, we apply a seasonal-trend decomposition procedure based on non-parametric regression to isolate the different components of the time series of international tourism demand to all Spanish regions. This approach allows us to compute a set of measures to describe the features of the data. Second, we analyse the performance of several ML models in a recursive multiple-step-ahead forecasting experiment. In a third step, we rank all seventeen regions according to their characteristics and the obtained forecasting performance, and use the rankings as the input for a multivariate analysis to evaluate the interactions between time series features and the accuracy of the predictions. By means of dimensionality reduction techniques we summarise all the information into two components and project all Spanish regions into perceptual maps. We find that entropy and dispersion show a negative relation with accuracy, while the effect of other data characteristics on forecast accuracy is heavily dependent on the forecast horizon
Self-Organizing map analysis of agents’ expectations. Different patterns of anticipation of the 2008 financial crisis
By means of Self-Organizing Maps we cluster fourteen European countries according to the most suitable way to model their agents’ expectations. Using the financial crisis of 2008 as a benchmark, we distinguish between those countries that show a progressive anticipation of the crisis and those where sudden changes in expectations occur. By mapping the trajectory of economic experts’ expectations prior to the recession we find that when there are brisk changes in expectations before impending shocks, Artificial Neural Networks are more suitable than time series models for modelling expectations. Conversely, in countries where expectations show a smooth transition towards recession, ARIMA models show the best forecasting performance. This result demonstrates the usefulness of clustering techniques for selecting the most appropriate method to model and forecast expectations according to their behaviour
Data pre-processing for neural network-based forecasting: does it really matter?
This study aims to analyze the effects of data pre-processing on the forecasting performance of neural network models. We use three different Artificial Neural Networks techniques to predict tourist demand: multi-layer perceptron, radial basis function and Elman neural networks. The structure of the networks is based on a multiple-output approach. We use official statistical data of inbound international tourism demand to Catalonia (Spain) and compare the forecasting accuracy of four processing methods for the input vector of the networks: levels, growth rates, seasonally adjusted levels and seasonally adjusted growth rates. When comparing the forecasting accuracy of the different inputs for each visitor market and for different forecasting horizons, we obtain significantly better forecasts with levels than with growth rates. We also find that seasonally adjusted series significantly improve the forecasting performance of the networks, which hints at the significance of deseasonalizing the time series when using neural networks with forecasting purposes. These results reveal that, when using seasonal data, neural networks performance can be significantly improved by working directly with seasonally adjusted levels
A new approach for the quantification of qualitative measures of economic expectations
In this study a new approach to quantify qualitative survey data about the direction of change is presented. We propose a data-driven procedure based on evolutionary computation that avoids making any assumption about agents' expectations. The research focuses on experts' expectations about the state of the economy from the World Economic Survey in twenty eight countries of the Organisation for Economic Co-operation and Development. The proposed method is used to transform qualitative responses into estimates of economic growth. In a first experiment, we combine agents' expectations about the future to construct a leading indicator of economic activity. In a second experiment, agents' judgements about the present are combined to generate a coincident indicator. Then, we use index tracking to derive the optimal combination of weights for both indicators that best replicates the evolution of economic activity in each country. Finally, we compute several accuracy measures to assess the performance of these estimates in tracking economic growth. The different results across countries have led us to use multidimensional scaling analysis in order to group all economies in four clusters according to their performance
Let the data do the talking: Empirical modelling of survey-based expectations by means of genetic programming
In this study we use agents’ expectations about the state of the economy to generate indicators of economic activity in twenty-six European countries grouped in five regions (Western, Eastern, and Southern Europe, and Baltic and Scandinavian countries). We apply a data-driven procedure based on evolutionary computation to transform survey variables in economic growth rates. In a first step, we design five independent experiments to derive the optimal combination of expectations that best replicates the evolution of economic growth in each region by means of genetic programming, limiting the integration schemes to the main mathematical operations. We then rank survey variables according to their performance in tracking economic activity, finding that agents’ “perception about the overall economy compared to last year” is the survey variable with the highest predictive power. In a second step, we assess the out-of-sample forecast accuracy of the evolved indicators. Although we obtain different results across regions, Austria, Slovakia, Portugal, Lithuania and Sweden are the economies of each region that show the best forecast results. We also find evidence that the forecasting performance of the survey-based indicators improves during periods of higher growth
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