A novel dynamic asset allocation system using Feature Saliency Hidden Markov models for smart beta investing

Hidden Markov models for fault detection in dynamic system

Song Retrieval System Using Hidden Markov Models

This project proposes a novel method to retrieve the song corresponding to a recording of a snippet of the same. The method used converts the song into a string of music phonemes, which are MFCC Feature Vectors using Hidden Markov Models. The sequence of these phones are then stored. A similar procedure is performed upon the snippets. The snippet sequence and song sequence are then compared usi...

Smart Annotation of Cyclic Data Using Hierarchical Hidden Markov Models

Cyclic signals are an intrinsic part of daily life, such as human motion and heart activity. The detailed analysis of them is important for clinical applications such as pathological gait analysis and for sports applications such as performance analysis. Labeled training data for algorithms that analyze these cyclic data come at a high annotation cost due to only limited annotations available u...

A Novel Gait Recognition System Based on Hidden Markov Models

The advances in computing power, availability of large-capacity storage devices and research in computer vision have contributed to recent developments in gait recognition. The ease of acquiring human videos by low cost equipments makes gait recognition much easier and less intrusive than other biometric systems. In this paper, a gait recognition system using a space-model-based approach is pro...

Hidden feature models for speech recognition using dynamic Bayesian networks

In this paper, we investigate the use of dynamic Bayesian networks (DBNs) to explicitly represent models of hidden features, such as articulatory or other phonological features, for automatic speech recognition. In previous work using the idea of hidden features, the representation has typically been implicit, relying on a single hidden state to represent a combination of features. We present a...