Models and analysis of vocal emissions for biomedical applications

This book of Proceedings collects the papers presented at the 3rd International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2003, held 10-12 December 2003, Firenze, Italy. The workshop is organised every two years, and aims to stimulate contacts between specialists active in research and industrial developments, in the area of voice analysis for biomedical applications. The scope of the Workshop includes all aspects of voice modelling and analysis, ranging from fundamental research to all kinds of biomedical applications and related established and advanced technologies

Pedestal looseness extent recognition method for rotating machinery based on vibration sensitive time-frequency feature and manifold learning

To realize automation and high accuracy of pedestal looseness extent recognition for rotating machinery, a novel pedestal looseness extent recognition method for rotating machinery based on vibration sensitive time-frequency feature and manifold learning dimension reduction is proposed. Firstly, the pedestal looseness extent of rotating machinery is characterized by vibration signal of rotating machinery and its spectrum, then the time-frequency features are extracted from vibration signal to construct the origin looseness extent feature set. Secondly, the algorithm of looseness sensitivity index is designed to filter out the non-sensitive feature and poor sensitivity feature from the origin looseness extent feature set, avoiding the interference of non-sensitive and poor sensitivity feature. The sensitive features are selected to construct the looseness extent sensitive feature set, which has stronger characterization capabilities than the origin looseness extent feature set. Moreover, an effective manifold learning method called linear local tangent space alignment (LLTSA) is introduced to compress the looseness extent sensitive feature set into the low-dimensional looseness extent sensitive feature set. Finally, the low-dimensional looseness extent sensitive feature set is inputted into weight K nearest neighbor classifier (WKNNC) to recognize the different pedestal looseness extents of rotating machinery, the WKNNC’s recognition accuracy is more stable compared with that of a k nearest neighbor classification (KNNC). At the same time, the pedestal looseness extent recognition of rotating machinery is realized. The feasibility and validity of the present method are verified by successful pedestal looseness extent recognition application in a rotating machinery

Heterogeneous recognition of bioacoustic signals for human-machine interfaces

Human-machine interfaces (HMI) provide a communication pathway between man and machine. Not only do they augment existing pathways, they can substitute or even bypass these pathways where functional motor loss prevents the use of standard interfaces. This is especially important for individuals who rely on assistive technology in their everyday life. By utilising bioacoustic activity, it can lead to an assistive HMI concept which is unobtrusive, minimally disruptive and cosmetically appealing to the user. However, due to the complexity of the signals it remains relatively underexplored in the HMI field. This thesis investigates extracting and decoding volition from bioacoustic activity with the aim of generating real-time commands. The developed framework is a systemisation of various processing blocks enabling the mapping of continuous signals into M discrete classes. Class independent extraction efficiently detects and segments the continuous signals while class-specific extraction exemplifies each pattern set using a novel template creation process stable to permutations of the data set. These templates are utilised by a generalised single channel discrimination model, whereby each signal is template aligned prior to classification. The real-time decoding subsystem uses a multichannel heterogeneous ensemble architecture which fuses the output from a diverse set of these individual discrimination models. This enhances the classification performance by elevating both the sensitivity and specificity, with the increased specificity due to a natural rejection capacity based on a non-parametric majority vote. Such a strategy is useful when analysing signals which have diverse characteristics, false positives are prevalent and have strong consequences, and when there is limited training data available. The framework has been developed with generality in mind with wide applicability to a broad spectrum of biosignals. The processing system has been demonstrated on real-time decoding of tongue-movement ear pressure signals using both single and dual channel setups. This has included in-depth evaluation of these methods in both offline and online scenarios. During online evaluation, a stimulus based test methodology was devised, while representative interference was used to contaminate the decoding process in a relevant and real fashion. The results of this research provide a strong case for the utility of such techniques in real world applications of human-machine communication using impulsive bioacoustic signals and biosignals in general