398 research outputs found
Enhanced Living by Assessing Voice Pathology Using a Co-Occurrence Matrix.
A large number of the population around the world suffers from various disabilities. Disabilities affect not only children but also adults of different professions. Smart technology can assist the disabled population and lead to a comfortable life in an enhanced living environment (ELE). In this paper, we propose an effective voice pathology assessment system that works in a smart home framework. The proposed system takes input from various sensors, and processes the acquired voice signals and electroglottography (EGG) signals. Co-occurrence matrices in different directions and neighborhoods from the spectrograms of these signals were obtained. Several features such as energy, entropy, contrast, and homogeneity from these matrices were calculated and fed into a Gaussian mixture model-based classifier. Experiments were performed with a publicly available database, namely, the Saarbrucken voice database. The results demonstrate the feasibility of the proposed system in light of its high accuracy and speed. The proposed system can be extended to assess other disabilities in an ELE
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Novel Damage Assessment Framework for Dynamic Systems through Transfer Learning from Audio Domains
Nowadays, damage detection strategies built on the application of Artificial Neural Network tools to define models that mimic the dynamic behavior of structural systems are viral. However, a fundamental issue in developing these strategies for damage assessment is given by the unbalanced nature of the available databases for civil, mechanical, or aerospace applications, which commonly do not contain sufficient information from all the different classes that need to be identified.
Unfortunately, when the aim is to classify between the healthy and damaged conditions in a structure or a generic dynamic system, it is extremely rare to have sufficient data for the unhealthy state since the system has already failed. At the same time, it is common to have plenty of data coming from the system under operational conditions. Consequently, the learning task, carried on with deep learning approaches, becomes case-dependent and tends to be specialized for a particular case and a very limited number of damage scenarios.
This doctoral research presents a framework for damage classification in dynamic systems intended to overcome the limitations imposed by unbalanced datasets. In this methodology, the model's classification ability is enriched by using lower-level features derived through an improved extraction strategy that learns from a rich audio dataset how to characterize vibration traits starting from human voice recordings. This knowledge is then transferred to a target domain with much less data points, such as a structural system where the same discrimination approach is employed to classify and differentiate different health conditions. The goal is to enrich the model's ability to discriminate between classes on the audio records, presenting multiple different categories with more information to learn.
The proposed methodology is validated both numerically and experimentally
Models and Analysis of Vocal Emissions for Biomedical Applications
The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies
Advances in Vibration Analysis Research
Vibrations are extremely important in all areas of human activities, for all sciences, technologies and industrial applications. Sometimes these Vibrations are useful but other times they are undesirable. In any case, understanding and analysis of vibrations are crucial. This book reports on the state of the art research and development findings on this very broad matter through 22 original and innovative research studies exhibiting various investigation directions. The present book is a result of contributions of experts from international scientific community working in different aspects of vibration analysis. The text is addressed not only to researchers, but also to professional engineers, students and other experts in a variety of disciplines, both academic and industrial seeking to gain a better understanding of what has been done in the field recently, and what kind of open problems are in this area
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
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