En décembre 2015, les détecteurs LIGO ont pour la première fois détecté une onde gravitationnelle émise lors de la coalescence d'une paire de trous noirs il y a de cela 1.3 milliards d'années. Une telle première dans la toute nouvelle astronomie gravitationnelle a été suivie par plusieurs autres observations. La dernière en date est la fusion de deux étoiles à neutron dont la contrepartie électromagnétique a pu être observée par plusieurs observatoires à travers le monde. A cette occasion, les ondes gravitationnelles se sont inscrites dans l'astronomie multi-messager. Ces observations ont été rendues possibles par des techniques avancées d'analyse de données. Grâce à elles, la faible empreinte laissée par une onde gravitationnelle dans les données de détecteurs peut être isolée. Le travail de cette thèse est dédié au développement d'une technique de détection d'ondes gravitationnelles ne reposant que sur une connaissance minimale du signal à isoler. Le développement de cette méthode consiste plus précisément à introduire une information sur la phase du signal d'onde gravitationnelle selon un contexte astrophysique déterminé. La première partie de cette thèse est consacrée à la présentation de la méthode. Dans une seconde partie cette méthode est appliquée à la recherche de signaux d'ondes gravitationnelles en provenance de systèmes binaires de trous noirs de masse stellaire dans du bruit Gaussien. Puis l'étude est répétée dans du bruit de détecteurs collecté pendant la première période de prise de données. Enfin la troisième partie est dédiée à la recherche de binaires de trous noirs dont l'orbite montre un écart à la géométrie circulaire, ce qui complexifie la morphologie du signal. De telles orbites sont qualifiées d'excentriques. Cette troisième analyse permet d'établir de premiers résultats quant à la méthode proposée lorsque le signal d'intérêt est peu connu / In december 2015 the LIGO detectors have first detected a gravitational wave emitted by a pair of coalescing black holes 1.3 billion years ago. Many more observations have been realised since then and heralded gravitational waves as a new messenger in astronomy. The latest detection is the merge of two neutron stars whose electromagnetic counterpart has been followed up by many observatories around the globe. These direct observations have been made possible by the developpement of advanced data analysis techniques. With them the weak gravitational wave inprint in detectors may be recovered. The realised work during this thesis aims at developping an existing gravitational wave detection method which relies on minimal assumptions of the targeted signal. It more precisely consists in introducing an information on the signal phase depending on the astrophysical context. The first part is dedicated to a presentation of the method. The second one presents the results obtained when applying the method to the search of stellar mass binary black holes in simulated Gaussian noise data. The study is repeated in real instrumental data collected during the first run of LIGO. Finally, the third part presents the method applied in the search for eccentric binary black holes. Their orbit exhibits a deviation from the quasi-circular orbit case considered so far and thus complicates the signal morphology. This third analysis establishes first results with the proposed method in the case of a poorly modeled signal

Visualization, processing and editing of audio, directly on a time-frequency surface, is the scope of this thesis. More precisely the scalogram produced by a Gabor Wavelet transform is used, which is a powerful alternative to traditional techinques where the wave form is the main visual aid and editting is performed by parametric filters. Reconstruction properties, scalogram design and enhancements as well audio manipulation algorithms are investigated for this audio representation.The scalogram is designed to allow a flexible choice of time-frequency ratio, while maintaining high quality reconstruction. For this mean, the Loglet is used, which is observed to be the most suitable filter choice.  Re-assignmentare tested, and a novel weighting function using partial derivatives of phase is proposed.  An audio interpolation procedure is developed and shown to perform well in listening tests.The feasibility to use the transform coefficients directly for various purposes is investigated. It is concluded that Pitch shifts are hard to describe in the framework while noise thresh holding works well. A downsampling scheme is suggested that saves on operations and memory consumption as well as it speeds up real world implementations significantly. Finally, a Scalogram 'compression' procedure is developed, allowing the caching of an approximate scalogram.

The aim of this thesis is to define a perceptual scale for the ‘Time-Frequency’ analysis of music signals. The equal tempered ‘Bach ’ scale is a suitable scale, since it covers most of the genres of music and the error is equally distributed for each semi-tone. However, it may be necessary to allow a tolerance of around 50 cents or half the interval of the Bach scale, so that the interval can accommodate other common intonation schemes. The thesis covers the formulation of the Bach scale filter-bank as a time-varying model. It makes a comparative study with other commonly used perceptual scales. Two applications for the Bach scale filter-bank are also proposed, namely automated segmentation of speech signals and transcription of singing voice for query-by-humming applications. Even though this filter-bank is suggested with a motivation from music, it could also be applied to speech. A method for automatically segmenting continuous speech into phonetic units is proposed. The results, obtained from the proposed method, show around 82% accuracy for the English and 85% accuracy for the Hindi databases. This is an improvement of around 2 -3% when the performance is compared with other popular methods in the literature. Interestingly, the Bach scale filters perform better than the filters designed for other common perceptual scales, such as Mel and Bark scales. ‘Musical transcription’ refers to the process of converting a musical rendering or performance into a set of symbols or notations. A query in a ‘query-by-humming system’ can be made in several ways, some of which are singing with words, or with arbitrary syllables, or whistling. Two algorithms are suggested to annotate a query. The algorithms are designed to be fairly robust for these various forms of queries. The first algorithm is a frequency selection based method. It works on the basis of selecting the most likely frequency components at any given time instant. The second algorithm works on the basis of finding time-connected contours of high energy in the ‘Time-Frequency’ plane of the input signal. The time domain algorithm works better in terms of instantaneous pitch estimates. It results in an error of around 10 -15%, while the frequency domain method results in an error of around 12 -20%. A song rendered by two different people will have quite a few different properties. Their absolute pitches, rates of rendering, timbres based on voice quality and inaccuracies, may be different. The thesis discusses a method to quantify the distance between two different renderings of musical pieces. The distance function has been evaluated by attempting a search for a particular song from a database of a size of 315, made up of songs sung by both male and female singers and whistled queries. Around 90 % of the time, the correct song is found among the top five best choices picked. Thus, the Bach scale has been proposed as a suitable scale for representing the perception of music. It has been explored in two applications, namely automated segmentation of speech and transcription of singing voices. Using the transcription obtained, a measure of the distance between renderings of musical pieces has also been suggested.

Diese Dissertation ist dem Studium temperierter Distributionen mittels mikrolokaler Methoden gewidmet. Die fundamentale Größe der mikrolokalen Analysis, die Wellenfrontmenge, wird durch zwei analoge Konzepte ersetzt, die den pseudo-differentiellen SG- und Shubin-Kalkülen zugeordnet sind. Die Eigenschaften dieser globalen Wellenfrontmengen werden studiert und ferner werden unterschiedliche Möglichkeiten, diese globalen Singularitäten zu charakterisieren, untersucht, insbesondere mittels der FBI-Transformation. Zahlreiche Konstruktionen, die den klassischen Wellenfrontmengenbegriff beinhalten, werden in den globalen Kontext übersetzt, insbesondere Rechenoperationen mit temperierten Distributionen wie etwa (getwistete) Produkte, Pull-backs und Paarungen, für die mikrolokale Existenzkriterien angegeben werden. Als eine Anwendung wird eine Klasse von temperierten Oszillatorintegralen eingeführt, welche durch inhomogene Phasenfunktionen und Amplituden aus SG-Symbolklassen parametrisiert werden. Die SG-Wellenfrontmengen dieser Distributionen werden untersucht und es stellt sich heraus, dass diese durch eine Verallgemeinerung der Menge stationärer Punkte der Phasenfunktionen beschränkt werden. In diesem Kontext wird eine Verallgemeinerung des klassischen Begriffs einer konischen Lagrange-Untermannifaltigkeit des T*R^d vorgenommen und diese Objekte werden auf ihre Parametrisierungseigenschaften untersucht. Es stellt sich heraus, dass jedes solche Objekt lokal als die Menge der stationären Punkte einer SG-Phasenfunktion realisiert werden kann. Als weitere Anwendung werden einige Konstruktionen der axiomatischen Quantenfeldtheorie, die Distributionen beinhalten, im temperierten Kontext realisiert.

Les océans subissent des pressions d'origine anthropique particulièrement fortes comme la surpêche, la pollution physico-chimique, et le bruit rayonné par les activités industrielles et militaires. Cette thèse se place dans un contexte de compréhension de l'impact du bruit rayonné dans les océans sur les mammifères marins. L'acoustique passive joue donc un rôle fondamental dans ce problème. Ce travail aborde la tâche de détection de signatures acoustiques de mammifères marins dans le spectrogramme. Cette tâche est difficile pour deux raisons : 1. le bruit océanique a une structure complexe (non-stationnaire, coloré), 2. les signaux de mammifères marins sont inconnus et possèdent eux aussi une structure complexe (non-stationnaires bande étroite et/ou impulsionnels). Le problème doit donc être résolu de manière locale en temps-fréquence, et ne pas faire d'hypothèse a priori sur le signal. Des détecteurs statistiques basés uniquement sur la connaissance des statistiques du bruit dans le spectrogramme existent, mais souffrent deux lacunes : 1. leurs performances en terme de probabilité de fausse alarme/ probabilité de détection se dégradent fortement à faible rapport signal à bruit, et 2. ils ne sont pas capables de séparer les signaux à bande étroite des signaux impulsionnels. Ce travail apporte des pistes de réflexion sur ces problèmes.L'originalité de ce travail de thèse repose dans la formulation d'un test d'hypothèse binaire prenant explicitement en compte l'organisation spatiale des pics temps-fréquence. Nous introduisons une méthode d'Analyse de la Densité des Fausses Alarmes (FADA) qui permet de discriminer les régions temps-fréquence abritant le signal de celles n'abritant que du bruit. Plus précisément,le nombre de fausses alarmes dans une région du plan est d'abord modélisé par une loi binomiale, puis par une loi binomiale corrélée, afin de prendre en considération la redondance du spectrogramme. Le test d'hypothèse binaire est résolu par une approche de Neyman-Pearson. Nous démontrons numériquement la pertinence de cette approche et nous la validons sur données réelles de mammifères marins disposant d'une grande variété de signaux et de conditions de bruit. En particulier, nous illustrons la capacité de FADA à discriminer efficacement le signal du bruit en milieu fortement impulsionnel. / The oceans experience heavy anthropogenic pressure due to overfishing, physico-chemical pollution, and noise radiated by industrial and military activities. This work focuses on the use of passive acoustic monitoring of the oceans, as a tool to understand the impact of radiated noise on marine ecosystems, and particularly on marine mammals. This work tackles the task of detection of acoustical signals of marine mammals using the spectrogram. This task is uneasy for two reasons : 1. the ocean noise structure is complex (non-stationary and colored) and 2. the signals of interest are unknown and also shows a complex structure (non-stationary narrow band and/or impulsive). The problem therefore must be solved locally without making a priori hypothesis on the signal. Statistical detectors only based on the local analysis of the noise spectrogram coefficients are available, making them suitable for this problem. However, these detectors suffer two disadvantages : 1. the trade-offs false alarm probability/ detection probability that are available for low signal tonoise ratio are not satisfactory and 2. the separation between narrow-band and impulsive signals is not possible. This work brings some answers to these problems.The main contribution of this work is to formulate a binary hypothesis test taking explicitly in account the spatial organization of time-frequency peaks. We introduce the False Alarm Density Analysis (FADA) framework that efficiently discriminates time-frequency regions hosting signal from the ones hosting noise only. In particular the number of false alarms in regions of the binary spectrogram is first modeled by a binomial distribution, and then by a correlated binomial distribution to take in account the spectrogram redundancy. The binary hypothesis test is solved using a Neyman-Pearson criterion.We demonstrate the relevance of this approach on simulated data and validate the FADA detector on a wide variety of real signals. In particular we show the capability of the proposed method to efficiently detect signals in highly impulsive environment.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2016-02-16T09:52:46Z No. of bitstreams: 1 alamsilvamenezes.pdf: 8301590 bytes, checksum: aed618e30f38206da4bf4f329924f87e (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2016-02-26T12:30:53Z (GMT) No. of bitstreams: 1 alamsilvamenezes.pdf: 8301590 bytes, checksum: aed618e30f38206da4bf4f329924f87e (MD5) / Made available in DSpace on 2016-02-26T12:30:53Z (GMT). No. of bitstreams: 1 alamsilvamenezes.pdf: 8301590 bytes, checksum: aed618e30f38206da4bf4f329924f87e (MD5) Previous issue date: 2014-09-01 / A presente tese propõe soluções ao problema da explicitação do conteúdo espectral de processos estacionários e não estacionários, com aplicações na estimação de frequência, estimação da densidade espectral de potência e no monitoramento do espectro. A técnica de estimação de frequência proposta nesta tese, baseada na warped discrete Fourier transform, apresenta, de acordo com as simulações computacionais, o melhor desempenho frente às demais técnicas comparadas, atingindo o Cramer-Rao bound para uma ampla faixa de relação sinal ruído. Em relação a estimação da densidade espectral de potência, a Hartley Multitaper method, proposta nesta tese, apresenta desempenho similar à multitaper method, em termos da variância de estimação e da polarização do espectro, mas simpli cação de implementação. Uma técnica para monitoramento do espectro para sistemas power line communication é proposta, levando em consideração o conceito de quanta e a diversidade observada quando os sinais são aquisitados a partir da rede de energia elétrica e do ar. Baseando-se em sinais sintéticos, gerados em computador, assim como dados de medição do espectro, obtidos utilizando uma antena e o cabo de energia elétrica como elementos sensores, veri fica-se que o desempenho da técnica proposta supera a monitoração padrão, sobretudo quando a diversidade gerada pelo cabo e pela antena sobre o sinal monitorado é explorada na detecção. / This dissertation aims at discussing solutions to deal with spectral analysis of stationary and non-stationary processes for frequency estimation, power spectral density estimation and spectral monitoring applications. The frequency estimation techniques are assessed through computer simulations. The proposed technique for frequency estimation is based on warped discrete Fourier transform outperforms other techniques, achieving the Cramer-Rao Bound for a wide range of signal to noise ratio. Regarding the power spectral density estimation, the proposed Hartley Multitaper Method shows similar performance, in terms of variance of estimates and polarization spectrum; however, it can simplify the implementation complexity. The introduced spectrum sensing technique is based on quanta de nition and the diversity o ered by the signals acquired from the electric power grids and the air. Based on computer-generation data and those one obtained during a measurement campaign, which one in this thesis is evaluated using synthetic signals, generated by computer, as well as measurement data of the spectrum. The numerical results show that the proposed technique outperforms a previous technique and can attain the very detection ratio and the very low false alarm when the diversity yielded by electric power grid and air is exploited.

The thesis deals with EEG signals, their description, methods of quantitative analysis and the processes in time-frequency domains, or power spectrums. The relationsheep between brain electrical activity and acustic stimuli (Mozart´s “Sonata K448”) was studied using EEG analysis in relation to sound impulses from replayed extracts of. The proposed experiment protocol included recording of EEG of volunteers. In order to visualize and analyze the data, the software with the graphic user interface was created, which enables topological mapping of brain activity and its vizualization in the time-frequency domain.

The doctoral thesis focuses on signal representation in the time-frequency domain with constant resolution. In theoretical introduction the possibilities of displaying a signal in time and frequency are summarized. Attention is concentrated on comparison of short-time Fourier Transform (STFT) and Wigner-Ville Distribution (WVD). The latter achieves a significantly better resolution, especially for a linearly modulated signal. The disadvantage of WVD, which is the presence of interferences resulting from the calculation of the instantaneous autocorrelation function, is described in detail. These interferences are due to the presence of multiple components in the signal or its non-linear modulation. Subsequently, several methods are discussed, which can suppress these interferences, but at the cost of resolution loss. One of the interference suppression methods is smoothed pseudo Wigner-Ville distribution. It is further used in this thesis for the analysis of interference suppression when various filtrations in the time-frequency plane are applied. Several signals with multiple components or various non-linear modulations are used. Based on the analysis, a method using a set of variously smoothed pseudo Wigner-Ville distributions is designed to estimate the time-frequency representation with high resolution and minimal interferences. To compare the results to other methods, the quantitative metrics used in the literature are compared. To select the appropriate one a new metric is suggested. It is applicable to simulated signals and uses mean square error. Based on the comparison, the Stankovi\' measure is selected as the most appropriate for comparing results. The selected metric is used to determine the appropriate minimal number of differently smoothed pseudo Wigner-Ville distributions. Using the selected metric, the proposed method is compared with other methods. These are STFT with optimized window length, S-method with optimized parameter and optimization method using radial Gaussian kernel (RGK). These methods are compared based on the set of signals previously used for interference suppression analysis. In addition, noises are added to the signals. Finally, the methods are also compared based on the real bat echo signal. In conclusion, the proposed method outperforms the compared methods in suppressing interference and resolution.

The separation of independent sources from mixed observed data is a fundamental problem in many practical situations. A typical example is speech recordings made in an acoustic environment in the presence of background noise or other speakers. Problems of signal separation are explored by a group of methods called Blind Source Separation. Blind Source Separation (BSS) consists on estimating a set of N unknown sources from P observations resulting from the mixture of these sources and unknown background. Some existing solutions for instantaneous mixtures are reviewed and in Matlab implemented , i.e Independent Componnent Analysis (ICA) and Time-Frequency Analysis (TF). The acoustic signals recorded in real environment are not instantaneous, but convolutive mixtures. In this case, an ICA algorithm for separation of convolutive mixtures in frequency domain is introduced and in Matlab implemented. This diploma thesis examines the useability and comparisn of proposed separation algorithms.

This thesis deals with the study of contactless and non-invasive methods for estimating heart and respiratory rate. Non-contact measurement involves sensing persons by using camera and the values of the physiological parameters are then assessed from the sets of image sequences by using suitable approaches. The theoretical part is devoted to description of the various methods and their implementation. The practical part describes the design and realization of the experiment for contactless detection of heart and respiratory rate. The experiment was carried out on 10 volunteers with a known heart and respiratory rate, which was covered by using of a sophisticated system BIOPAC. Processing and analysis of the measured data was conducted in software environment Matlab. Finally, results from contactless detection were compared with the reference from measurement system BIOPAC. Experiment results are statistically evaluated and discussed.