Elliptical Basis Function Networks Research Articles

Speech emotion recognition using hybrid spectral-prosodic features of speech signal/glottal waveform, metaheuristic-based dimensionality reduction, and Gaussian elliptical basis function network classifier

In this paper, a hybrid system consisting of three stages of feature extraction, dimensionality reduction, and feature classification is proposed for speech emotion recognition (SER). At feature extraction stage, an informationally-rich spectral-prosodic hybrid feature vector comprised of perceptual-spectral features; that is, mel-frequency cepstral coefficient (MFCC), perceptual linear prediction coefficient (PLPC), and perceptual minimum variance distortionless response (PMVDR) coefficient along with the prosodic feature of pitch (i.e. F0) are extracted for each frame. This feature vector is extracted from both speech signal and its glottal-waveform. The first and the second-order derivatives are then added to the above-mentioned vector to form a high-dimensional hybrid feature vector characterized by a large number of dimensions. At the next stage, i.e. dimensionality reduction, the dimensionality of this feature vector is reduced using a new proposed quantum-behaved particle swarm optimization (QPSO)-based approach. In this paper, a new QPSO algorithm (so-called, pQPSO) is presented that makes use of a truncated Laplace distribution (TLD) to generate new particles and thus to produce solutions (i.e. particles) that are all within a valid range of a problem (contrary to the standard QPSO). The contraction-expansion (CE) factor of the proposed pQPSO is also selected adaptively. Using the proposed QPSO algorithm, an optimal discriminative dimensionality reduction matrix (i.e. projection matrix) is estimated with emotion classification accuracy as a class-discriminative criterion. At the subsequent stage, vectors with reduced feature dimensionality are fed into a Gaussian elliptical basis function (GEBF)-type neural network classifier to detect their speech emotion. To accelerate the training phase of the GEBF classifier, a fast-scaled conjugate gradient (SCG) algorithm is correspondingly employed that does not need to adjust the learning rate. Finally, the proposed method is evaluated on three standard emotional speech databases of Berlin Database of Emotional Speech (EMODB), Surrey Audio-Visual Expressed Emotion (SAVEE), and Interactive Emotional Dyadic Motion Capture (IEMOCAP). The experimental results showed that the proposed method was more accurate than state-of-the-art ones in terms of detecting speech emotions.

An oblique elliptical basis function network approach for supervised learning applications

We propose a neural network architecture based on the oblique elliptical basis function for supervised learning problems. In classification, a category can be a disconnected or non-convex region involving several overlapping or disjoint sub-regions of the feature space. Other existing supervised learning methods may have the restriction that only allows decision regions to be convex. Our proposed method overcomes this restriction by employing a rotational self-constructing clustering algorithm to decompose the feature space into a collection of sub-regions which can then be combined to make up individual categories. An unseen instance is classified to a certain category if its similarity to the category exceeds a threshold. The whole framework fits in a five-layer network consisting of input, component-similarity, cluster-similarity, aggregation, and output layers. A similar idea also applies to solving regression problems. A parameter learning algorithm based on least squares estimation is used to derive the weights of the underlying network. Our approach can offer some advantages in practicality. Through the incorporation of rotation, data can be clustered more appropriately than by standard elliptical basis functions. Also, our approach is applicable to single-label classification, multi-label classification, as well as regression problems. A number of experiments are conducted to show the effectiveness of the proposed approach.

Remaining Useful Life Prediction Using Elliptical Basis Function Network and Markov Chain

This paper presents a novel method for remaining useful life prediction using the Elliptical Basis Function (EBF) network and a Markov chain. The EBF structure is trained by a modified Expectation-Maximization (EM) algorithm in order to take into account the missing covariate set. No explicit extrapolation is needed for internal covariates while a Markov chain is constructed to represent the evolution of external covariates in the study. The estimated external and the unknown internal covariates constitute an incomplete covariate set which are then used and analyzed by the EBF network to provide survival information of the asset. It is shown in the case study that the method slightly underestimates the remaining useful life of an asset which is a desirable result for early maintenance decision and resource planning.

Applying Elliptical Basis Function Neural Networks to VAD for Wireless Communication Systems

Voice activity detection (VAD) determines whether a slice of waveform is voice or silence. The proposed VAD algorithm applying Elliptical Basis Function (EBF) neural networks uses k-means clustering and least mean square for the update algorithm. The error rates achieved by the EBF network have superior performance to those of G.729 Annex B and RBF.

An elliptical basis function network for classification of remote sensing images

An elliptical basis function (EBF) network is employed in this study for the classification of remotely sensed images. Though similar in structure, the EBF network differs from the well-known radial basis function (RBF) network by incorporating full covariance matrices and employing the expectation-maximization (EM) algorithm to estimate the basis functions. Since remotely sensed data often take on mixture-density distributions in the feature space, the network not only possesses the advantage of the RBF mechanism, but also utilizes the EM algorithm to compute the maximum likelihood estimates of the mean vectors and covariance matrices of a Gaussian mixture distribution in the training phase. Experimental results show that the EM-based EBF network is more effective in training and simpler in structure than an RBF network constructed for the same task.

Adaptive forecasting of irregular demand processes

The paper presents some comparative results from different approaches for solving the problem of forecasting irregularly spaced time series, including those representing irregular demand processes in inventory management or production planning problems. The best-known methods for that purpose are based on simple exponential smoothing of the time and magnitude of occurrence of demand. It is shown how supervised neural networks can be used as suitable filtering and forecasting models for many of those processes. In particular, we discuss the identification and estimation issues, as well as the limitations associated to the application of Gaussian radial or elliptical basis function networks.

Feature mapping and recuperation by using elliptical basis function networks for robust speaker verification

The performance of speaker verification systems is often compromised under real-world environments. For example, variations in handset characteristics could cause severe performance degradation. This paper presents a novel method to overcome this problem by using a non-linear handset mapper. Under this method, a mapper is constructed by training an elliptical basis function network using distorted speech features as inputs and the corresponding clean features as the desired outputs. During feature recuperation, clean features are recovered by feeding the distorted features to the feature mapper. The recovered features are then presented to a speaker model as if they were derived from clean speech. Experimental evaluations based on 258 speakers of the TIMIT and NTIMIT corpuses suggest that the feature mappers improve the verification performance remarkably.

Estimation of elliptical basis function parameters by the EM algorithm with application to speaker verification

This paper proposes to incorporate full covariance matrices into the radial basis function (RBF) networks and to use the expectation-maximization (EM) algorithm to estimate the basis function parameters. The resulting networks, referred to as elliptical basis function (EBF) networks, are evaluated through a series of text-independent speaker verification experiments involving 258 speakers from a phonetically balanced, continuous speech corpus (TIMIT).We propose a verification procedure using RBF and EBF networks as speaker models and show that the networks are readily applicable to verifying speakers using LP-derived cepstral coefficients as features. Experimental results show that small EBF networks with basis function parameters estimated by the EM algorithm outperform the large RBF networks trained in the conventional approach. The results also show that the equal error rate achieved by the EBF networks is about two-third of that achieved by the vetor quantization (VQ)-based speaker models.

Determining the Optimal Number of Clusters by an Extended RPCL Algorithm

Determining an appropriate number of clusters is a difficult yet important problem that the rival penalized competitive learning (RPCL) algorithm was designed to solve, but its performance is not satifactory with overlapping clusters or cases where input vectors contain dependent components. We address this problem by incorporating full covariance matrices into the original RPCL algorithm. The resulting extended RPCL algorithm progressively eliminates units whose clusters contain only a small amount of training data. The algorithm is used to determine the number of clusters in a Gaussian distribution. It is also used to optimize the architecture of elliptical basis function networks for speaker verification and vowel classification. We found that covariance matrices obtained by the extended RPCL algorithm have a better representation of clusters than those obtained by the original RPCL algorithm, resulting in a lower verification error rate in speaker verification and a higher recognition accuracy in vowel classification.

Probability density estimation using elliptical basis functions

AbstractElliptical basis function (EBF) networks are introduced as a new nonparametric method of estimating probability density functions for process data. Unlike Parzen window density estimators that use identical hyperspherical basis functions, the EBF method uses elliptical basis functions adapted to the local character of the data. This technique overcomes the spikiness problem associated with Parzen windows, where in high dimension, they can fail to produce smooth probability density estimates. The EBF estimator produces valid density functions that converage to the underlying distribution of the data in the limit of an infinite number of training examples. A technique based on statistical cross validation is introduced for evaluating different density estimators. The criterion is a measure of how well the density estimator estimates the density of data not used in the training. The EBF density estimation method and the evaluation technique are demonstrated using several examples of fault diagnosis.