Audio-based event detection for sports video

In this paper, we present an audio-based event detection approach shown to be effective when applied to the Sports broadcast data. The main benefit of this approach is the ability to recognise patterns that indicate high levels of crowd response which can be correlated to key events. By applying Hidden Markov Model-based classifiers, where the predefined content classes are parameterised using Mel-Frequency Cepstral Coefficients, we were able to eliminate the need for defining a heuristic set of rules to determine event detection, thus avoiding a two-class approach shown not to be suitable for this problem. Experimentation indicated that this is an effective method for classifying crowd response in Soccer matches, thus providing a basis for automatic indexing and summarisation

Discovering Clusters in Motion Time-Series Data

A new approach is proposed for clustering time-series data. The approach can be used to discover groupings of similar object motions that were observed in a video collection. A finite mixture of hidden Markov models (HMMs) is fitted to the motion data using the expectation-maximization (EM) framework. Previous approaches for HMM-based clustering employ a k-means formulation, where each sequence is assigned to only a single HMM. In contrast, the formulation presented in this paper allows each sequence to belong to more than a single HMM with some probability, and the hard decision about the sequence class membership can be deferred until a later time when such a decision is required. Experiments with simulated data demonstrate the benefit of using this EM-based approach when there is more "overlap" in the processes generating the data. Experiments with real data show the promising potential of HMM-based motion clustering in a number of applications.Office of Naval Research (N000140310108, N000140110444); National Science Foundation (IIS-0208876, CAREER Award 0133825

A Support System for ECG Segmentation Based on Hidden Markov Models

Scheduled for presentation during the Poster Session "Signal Pattern Classification in Biomedical Signals V" (FrP2A1)International audiencePharmaceutic studies require to analyze thousands of ECGs in order to evaluate the side effects of a new drug. In this paper we present a new support system based on the use of probabilistic models for automatic ECG segmentation. We used a bayesian HMM clustering algorithm to partition the training base, and we improved the method by using a multi-channel segmentation. We present a statistical analysis of the results where we compare different automatic methods to the segmentation of the cardiologist as a gold standard

Hidden Markov Model with Information Criteria Clustering and Extreme Learning Machine Regression for Wind Forecasting

This paper proposes a procedural pipeline for wind forecasting based on clustering and regression. First, the data are clustered into groups sharing similar dynamic properties. Then, data in the same cluster are used to train the neural network that predicts wind speed. For clustering, a hidden Markov model (HMM) and the modified Bayesian information criteria (BIC) are incorporated in a new method of clustering time series data. to forecast wind, a new method for wind time series data forecasting is developed based on the extreme learning machine (ELM). the clustering results improve the accuracy of the proposed method of wind forecasting. Experiments on a real dataset collected from various locations confirm the method\u27s accuracy and capacity in the handling of a large amount of data

Clustering: Methodology, hybrid systems, visualization, validation and implementation

Unsupervised learning is one of the most important steps of machine learning applications. Besides its ability to obtain the insight of the data distribution, unsupervised learning is used as a preprocessing step for other machine learning algorithm. This dissertation investigates the application of unsupervised learning into various types of data for many machine learning tasks such as clustering, regression and classification. The dissertation is organized into three papers. In the first paper, unsupervised learning is applied to mixed categorical and numerical feature data type to transform the data objects from the mixed type feature domain into a new sparser numerical domain. By making use of the data fusion capacity of adaptive resonance theory clustering, the approach is able to reduce the distinction between the numerical and categorical features. The second paper presents a novel method to improve the performance of wind forecast by clustering the time series of the surrounding wind mills into the similar group by using hidden Markov model clustering and using the clustering information to enhance the forecast. A fast forecast method is also introduced by using extreme learning machine which can be trained by analytic form to choose the optimal value of past samples for prediction and appropriate size of the neural network. In the third paper, unsupervised learning is used to automatically learn the feature from the dataset itself without human design of sophisticated feature extractors. The paper points out that by using unsupervised feature learning with multi-quadric radial basis function extreme learning machine the performance of the classifier is better than several other supervised learning methods. The paper further improves the speed of training the neural network by presenting an algorithm that runs parallel on GPU --Abstract, page iv

Biomedical time series analysis based on bag-of-words model

This research proposes a number of new methods for biomedical time series classification and clustering based on a novel Bag-of-Words (BoW) representation. It is anticipated that the objective and automatic biomedical time series clustering and classification technologies developed in this work will potentially benefit a wide range of applications, such as biomedical data management, archiving, retrieving, and disease diagnosis and prognosis in the future

Hidden Markov Models

Hidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development. This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. I hope that the reader will find this book useful and helpful for their own research