in this paper, a new method is presented for the detection of defects in random textures. in the training stage, the feature vectors of the normal textures’ images are extracted by using the optimal response of gabor wavelet filters, and their probability density is estimated by means of the gaussian mixture model (gmm). in the testing stage, similar to the previous stage,at  first, the feature...

Most techniques for speaker verification today use Gaussian Mixture Models (GMMs) and make the decision by comparing the likelihood of the speaker model to the likelihood of a universal background model (UBM). The paper proposes to replace the UBM by an individual background model (IBM) that is generated for each speaker. The IBM is created using the K-nearest cohort models and the UBM by a sim...

This paper introduces the technique of anchor modeling in the applications of speaker detection and speaker indexing. The anchor modeling algorithm is refined by pruning the number of models needed. The system is applied to the speaker detection problem where its performance is shown to fall short of the state-of-the-art Gaussian Mixture Model with Universal Background Model (GMM-UBM) system. H...

The voice conversion algorithm based on the Gaussian mixture model (GMM) has also been proposed by Stylianou et al. In this algorithm, the acoustic space of a speaker is represented continuously. In this paper, we apply this GMMbased voice conversion algorithm to STRAIGHT proposed by Kawahara et al., which is recognized as a high quality vocoder. In order to evaluate this voice conversion algor...

In this project report, we have investigated the video modeling techniques and realized a statistical video representation and modeling scheme [1], which could be used for later video retrieval and content extraction task. This method utilizes Gaussian mixture model (GMM) to segment video content into coherent space-time segments within the video frames and across frames. It treats space and ti...

Gaussian Mixture Model (GMM) is an efficient method for parametric clustering. However, traditional GMM can’t perform clustering well on data set with complex structure such as images. In this paper, kernel trick, successfully used by SVM and kernel PCA, is introduced into EM algorithm for solving parameter estimation of GMM, which is so called kernel GMM (kGMM). The basic idea of kernel GMM is...

In this paper, we improve our previous cluster model selection method for agglomerative hierarchical speaker clustering (AHSC) based on incremental Gaussian mixture models (iGMMs). In the previous work, we measured the likelihood of all the data points in a given cluster for each mixture component of the GMM modeling the cluster. Then, we selected the N -best component Gaussians with the highes...

Gaussian mixture model (GMM) techniques are popular for speaker identification. Theoretically, each Gaussian function should have a full covariance matrix. However, the diagonal covariance matrix is usually used because the inverse of diagonal covariance matrix can be easily calculated via expectation maximization (EM) algorithm. This paper proposes a new probabilistic principal component analy...

Landmarks in speech signal are regions with abrupt spectral variations. Automated detection of these regions is important for several applications in speech processing. Performance of landmark detection using parameters extracted from predefined spectral bands generally gets limited by speaker related spectral variability. This paper presents a landmark detection technique which adapts to the a...

In this paper, we present a kernel trick embedded Gaussian Mixture Model (GMM), called kernel GMM. The basic idea is to embed kernel trick into EM algorithm and deduce a parameter estimation algorithm for GMM in feature space. Kernel GMM could be viewed as a Bayesian Kernel Method. Compared with most classical kernel methods, the proposed method can solve problems in probabilistic framework. Mo...