Damage detection in a RC-masonry tower equipped with a non-conventional TMD using temperature-independent damage sensitive features

Many features used in Structural Health Monitoring strategies are not just highly sensitive to failure mechanisms, but also depend on environmental or operational fluctuations. To prevent incorrect failure uncovering due to these dependencies, damage detection approaches can use robust and temperature-independent features. These indicators can be naturally insensitive to environmental dependencies or artificially made independent. This work explores both options. Cointegration theory is used to remove environmental dependencies from dynamic features to create highly sensitive parameters to detect failure mechanisms: the cointegration residuals. This paper applies the cointegration technique for damage detection of a concrete-masonry tower in Italy. Two regression models are implemented to capture temperature effects: Prophet and Long Short-Term Memory networks. Results demonstrate the advantages and limitations of this methodology for real applications. The authors suggest to combine the cointegration residuals with a secondary temperature-insensitive damage-sensitive set of features, the Cepstral Coefficients, to address the possibility of capturing undetected structural damage

WASIS - Identificação bioacústica de espécies baseada em múltiplos algoritmos de extração de descritores e de classificação

Orientador: Claudia Maria Bauzer MedeirosDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: A identificação automática de animais por meio de seus sons é um dos meios para realizar pesquisa em bioacústica. Este domínio de pesquisa fornece, por exemplo, métodos para o monitoramento de espécies raras e ameaçadas, análises de mudanças em comunidades ecológicas, ou meios para o estudo da função social de vocalizações no contexto comportamental. Mecanismos de identificação são tipicamente executados em dois estágios: extração de descritores e classificação. Ambos estágios apresentam desafios, tanto em ciência da computação quanto na bioacústica. A escolha de algoritmos de extração de descritores e técnicas de classificação eficientes é um desafio em qualquer sistema de reconhecimento de áudio, especialmente no domínio da bioacústica. Dada a grande variedade de grupos de animais estudados, algoritmos são adaptados a grupos específicos. Técnicas de classificação de áudio também são sensíveis aos descritores extraídos e condições associadas às gravações. Como resultado, muitos sistemas computacionais para bioacústica não são expansíveis, limitando os tipos de experimentos de reconhecimento que possam ser conduzidos. Baseado neste cenário, esta dissertação propõe uma arquitetura de software que acomode múltiplos algoritmos de extração de descritores, fusão entre descritores e algoritmos de classificação para auxiliar cientistas e o grande público na identificação de animais através de seus sons. Esta arquitetura foi implementada no software WASIS, gratuitamente disponível na Internet. Diversos algoritmos foram implementados, servindo como base para um estudo comparativo que recomenda conjuntos de algoritmos de extração de descritores e de classificação para três grupos de animaisAbstract: Automatic identification of animal species based on their sounds is one of the means to conduct research in bioacoustics. This research domain provides, for instance, ways to monitor rare and endangered species, to analyze changes in ecological communities, or ways to study the social meaning of the animal calls in the behavior context. Identification mechanisms are typically executed in two stages: feature extraction and classification. Both stages present challenges, in computer science and in bioacoustics. The choice of effective feature extraction and classification algorithms is a challenge on any audio recognition system, especially in bioacoustics. Considering the wide variety of animal groups studied, algorithms are tailored to specific groups. Classification techniques are also sensitive to the extracted features, and conditions surrounding the recordings. As a results, most bioacoustic softwares are not extensible, therefore limiting the kinds of recognition experiments that can be conducted. Given this scenario, this dissertation proposes a software architecture that allows multiple feature extraction, feature fusion and classification algorithms to support scientists and the general public on the identification of animal species through their recorded sounds. This architecture was implemented by the WASIS software, freely available on the Web. A number of algorithms were implemented, serving as the basis for a comparative study that recommends sets of feature extraction and classification algorithms for three animal groupsMestradoCiência da ComputaçãoMestre em Ciência da Computação132849/2015-12013/02219-0CNPQFAPES

A survey on the feasibility of sound classification on wireless sensor nodes

Wireless sensor networks are suitable to gain context awareness for indoor environments. As sound waves form a rich source of context information, equipping the nodes with microphones can be of great benefit. The algorithms to extract features from sound waves are often highly computationally intensive. This can be problematic as wireless nodes are usually restricted in resources. In order to be able to make a proper decision about which features to use, we survey how sound is used in the literature for global sound classification, age and gender classification, emotion recognition, person verification and identification and indoor and outdoor environmental sound classification. The results of the surveyed algorithms are compared with respect to accuracy and computational load. The accuracies are taken from the surveyed papers; the computational loads are determined by benchmarking the algorithms on an actual sensor node. We conclude that for indoor context awareness, the low-cost algorithms for feature extraction perform equally well as the more computationally-intensive variants. As the feature extraction still requires a large amount of processing time, we present four possible strategies to deal with this problem

Studies on noise robust automatic speech recognition

Noise in everyday acoustic environments such as cars, traffic environments, and cafeterias remains one of the main challenges in automatic speech recognition (ASR). As a research theme, it has received wide attention in conferences and scientific journals focused on speech technology. This article collection reviews both the classic and novel approaches suggested for noise robust ASR. The articles are literature reviews written for the spring 2009 seminar course on noise robust automatic speech recognition (course code T-61.6060) held at TKK

Human Identity Verification based on Heart Sounds: Recent Advances and Future Directions

Identity verification is an increasingly important process in our daily lives, and biometric recognition is a natural solution to the authentication problem. One of the most important research directions in the field of biometrics is the characterization of novel biometric traits that can be used in conjunction with other traits, to limit their shortcomings or to enhance their performance. The aim of this work is to introduce the reader to the usage of heart sounds for biometric recognition, describing the strengths and the weaknesses of this novel trait and analyzing in detail the methods developed so far by different research groups and their performance.Comment: 18 pages, chapter to be published in the book "Biometrics / Book 1", ISBN 978-953-307-618-8, by InTec

Robust speaker identification using artificial neural networks

This research mainly focuses on recognizing the speakers through their speech samples. Numerous Text-Dependent or Text-Independent algorithms have been developed by people so far, to recognize the speaker from his/her speech. In this thesis, we concentrate on the recognition of the speaker from the fixed text i.e. Text-Dependent . Possibility of extending this method to variable text i.e. Text-Independent is also analyzed. Different feature extraction algorithms are employed and their performance with Artificial Neural Networks as a Data Classifier on a fixed training set is analyzed. We find a way to combine all these individual feature extraction algorithms by incorporating their interdependence. The efficiency of these algorithms is determined after the input speech is classified using Back Propagation Algorithm of Artificial Neural Networks. A special case of Back Propagation Algorithm which improves the efficiency of the classification is also discussed

Acoustic Features for Environmental Sound Analysis

International audienceMost of the time it is nearly impossible to differentiate between particular type of sound events from a waveform only. Therefore, frequency domain and time-frequency domain representations have been used for years providing representations of the sound signals that are more inline with the human perception. However, these representations are usually too generic and often fail to describe specific content that is present in a sound recording. A lot of work have been devoted to design features that could allow extracting such specific information leading to a wide variety of hand-crafted features. During the past years, owing to the increasing availability of medium scale and large scale sound datasets, an alternative approach to feature extraction has become popular, the so-called feature learning. Finally, processing the amount of data that is at hand nowadays can quickly become overwhelming. It is therefore of paramount importance to be able to reduce the size of the dataset in the feature space. The general processing chain to convert an sound signal to a feature vector that can be efficiently exploited by a classifier and the relation to features used for speech and music processing are described is this chapter

SPEECH RECOGNITION FOR CONNECTED WORD USING CEPSTRAL AND DYNAMIC TIME WARPING ALGORITHMS

Speech Recognition or Speech Recognizer (SR) has become an important tool for people with physical disabilities when handling Home Automation (HA) appliances. This technology is expected to improve the daily life of the elderly and the disabled so that they are always in control over their lives, and continue to live independently, to learn and stay involved in social life. The goal of the research is to solve the constraints of current Malay SR that is still in its infancy stage where there is limited research in Malay words, especially for HA applications. Since, most of the previous works were confined to wired microphone; this limitation of using wireless microphone type makes it an important area of the research. Research was carried out to develop SR word model for five (5) Malay words and five (5) English words as commands to activate and deactivate home appliances