Digital processing of speech signals

Multi-appliance recognition system with hybrid SVM/GMM classifier in ubiquitous smart home

Multiple kernel learning (MKL) is a promising family of machine learning algorithms using multiple kernel functions for various challenging data mining tasks. Conventional MKL methods often formulate the problem as an optimization task of learning the optimal combinations of both kernels and classifiers, which usually results in some forms of challenging optimization tasks that are often difficult to be solved. Different from the existing MKL methods, in this paper, we investigate a boosting framework of MKL for classification tasks, i.e., we adopt boosting to solve a variant of MKL problem, which avoids solving the complicated optimization tasks. Specifically, we present a novel framework of Multiple kernel boosting (MKBoost), which applies the idea of boosting techniques to learn kernel-based classifiers with multiple kernels for classification problems. Based on the proposed framework, we propose several variants of MKBoost algorithms and extensively examine their empirical performance on a number of benchmark data sets in comparisons to various state-of-the-art MKL algorithms on classification tasks. Experimental results show that the proposed method is more effective and efficient than the existing MKL techniques.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=3280&context=sis_research

MKBoost: A Framework of Multiple Kernel Boosting

Xenobiotic chemicals and their metabolites are mainly excreted out of our bodies by the urinary tract through the urine. Chemical-induced urinary tract toxicity is one of the main reasons that cause failure during drug development, and it is a common adverse event for medications, natural supplements, and environmental chemicals. Despite its importance, there are only a few in silico models for assessing urinary tract toxicity for a large number of compounds with diverse chemical structures. Here, we developed a series of qualitative and quantitative structure–activity relationship (QSAR) models for predicting urinary tract toxicity. In our study, the recursive feature elimination method incorporated with random forests (RFE-RF) was used for dimension reduction, and then eight machine learning approaches were used for QSAR modeling, i.e., relevance vector machine (RVM), support vector machine (SVM), regularized random forest (RRF), C5.0 trees, eXtreme gradient boosting (XGBoost), AdaBoost.M1, SVM boosting...

ADMET Evaluation in Drug Discovery. 18. Reliable Prediction of Chemical-Induced Urinary Tract Toxicity by Boosting Machine Learning Approaches

In this paper we explain a fully automatic system for airplane detection and tracking based on wavelet transform and Support Vector Machine (SVM). By using 50 airplane images in different situations, models are developed to recognize airplane in the first frame of a video sequence. To train a SVM classifier for classifying pixels belong to objects and background pixels, vectors of features are built. The learned model can be used to detect the airplane in the original video and in the novel images. For original video, the system can be considered as a generalized tracker and for novel images it can be interpreted as method for learning models for object recognition. After airplane detection in the first frame, the feature vectors of this frame are used to train the SVM classifier. For new video frame, SVM is applied to test the pixels and form a confidence map. The 4th level of Daubechies's wavelet coefficients corresponding to input image are used as features. Conducting simulations, it is demonstrated that airplane detection and tracking based on wavelet transform and SVM classification result in acceptable and efficient performance. The experimental results agree with the theoretical results.

Airplane detection and tracking using wavelet features and SVM classifier

In this paper, a new Adaboosted kernel classifier algorithm is introduced for face detection application. However, most of the methods used to implement relevance vector machine (RVM), need lengthy computation time when faced with a large and complicated dataset. A new pruning method is used to reduce the computational cost. The kernel classifier parameters are adoptively chosen. In addition, using Fisher's criterion, a subset of Haar-like features is selected. As a result, our proposed algorithm with its previous counterparts i.e. support vector machine (SVM) and RVM without boosting is compared, which results in a better performance in terms of generalization, sparsity and real-time behavior for CBCL face database.

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Face Detection Using Adaboosted RVM-based Component Classifier

In pattern recognition, support vector machines (SVM) as a discriminative classifier and Gaussian mixture model as a generative model classifier are two most popular techniques. Current state-of-the-art systems try to combine them together for achieving more power of classification and improving the performance of the recognition systems. Most of recent works focus on probabilistic SVM/GMM hybrid methods but this paper presents a novel method for SVM/GMM hybrid pattern classification based on training data selection. This system uses the output of the Gaussian mixture model to choose training data for SVM classifier. Results on databases are provided to demonstrate the effectiveness of this system. We are able to achieve better error-rates that are better than the current systems.

Pattern Classiffication using SVM with GMM Data Selection Training Methode

The lack of a flexible analysis model has been introduced as an important issue in different applications like source separation In this paper, a fixed dimension modified sinusoid model (FD-MSM) is proposed for analysis of all audible signals consisting of speech, music and their mixtures Employing the peak picking in Meldomain gives rise to a fixed number of parameters in the proposed FDMSM, which is desired in clustering algorithms like VQ (vector quantization) or GMM (gaussian mixture model), commonly used for source separation scenarios Applying the proposed FD-MSM to various audible signals, it is observed that the resulting signal is perceptually indistinguishable from the original

A.R.B. Tashk

Papers

Face Detection Using Adaboosted RVM-based Component Classifier

Pattern Classiffication using SVM with GMM Data Selection Training Methode

A Fixed Dimension Modified Sinusoid Model (FD-MSM) for Single Microphone Sound Separation