Spectral Analysis for Signal Detection and Classification : Reducing Variance and Extracting Features

Spectral analysis encompasses several powerful signal processing methods. The papers in this thesis present methods for finding good spectral representations, and methods both for stationary and non-stationary signals are considered. Stationary methods can be used for real-time evaluation, analysing shorter segments of an incoming signal, while non-stationary methods can be used to analyse the instantaneous frequencies of fully recorded signals. All the presented methods aim to produce spectral representations that have high resolution and are easy to interpret. Such representations allow for detection of individual signal components in multi-component signals, as well as separation of close signal components. This makes feature extraction in the spectral representation possible, relevant features include the frequency or instantaneous frequency of components, the number of components in the signal, and the time duration of the components. Two methods that extract some of these features automatically for two types of signals are presented in this thesis. One adapted to signals with two longer duration frequency modulated components that detects the instantaneous frequencies and cross-terms in the Wigner-Ville distribution, the other for signals with an unknown number of short duration oscillations that detects the instantaneous frequencies in a reassigned spectrogram. This thesis also presents two multitaper methods that reduce the influence of noise on the spectral representations. One is designed for stationary signals and the other for non-stationary signals with multiple short duration oscillations. Applications for the methods presented in this thesis include several within medicine, e.g. diagnosis from analysis of heart rate variability, improved ultrasound resolution, and interpretation of brain activity from the electroencephalogram

Scaled reassigned spectrograms applied to linear transducer signals

This study evaluates the applicability of scaled reassigned spectrograms (ReSTS) on ultrasound radio frequency data obtained with a clinical linear array ultrasound transducer. The ReSTS's ability to resolve axially closely spaced objects in a phantom is compared to the classical cross-correlation method with respect to the ability to resolve closely spaced objects as individual reflectors using ultrasound pulses with different lengths. The results show that the axial resolution achieved with the ReSTS was superior to the cross-correlation method when the reflected pulses from two objects overlap. A novel B-mode imaging method, facilitating higher image resolution for distinct reflectors, is proposed

Time-frequency methods for coherent spectroscopy

Time-frequency decomposition techniques, borrowed from the signal-processing field, have been adapted and applied to the analysis of 2D oscillating signals. While the Fourier-analysis techniques available so far are able to interpret the information content of the oscillating signal only in terms of its frequency components, the time-frequency transforms (TFT) proposed in this work can instead provide simultaneously frequency and time resolution, unveiling the dynamics of the relevant beating components, and supplying a valuable help in their interpretation. In order to fully exploit the potentiality of this method, several TFTs have been tested in the analysis of sample 2D data. Possible artifacts and sources of misinterpretation have been identified and discussed

Analysis and decomposition of frequency modulated multicomponent signals

Frequency modulated (FM) signals are studied in many research fields, including seismology, astrophysics, biology, acoustics, animal echolocation, radar and sonar. They are referred as multicomponent signals (MCS), as they are generally composed of multiple waveforms, with specific time-dependent frequencies, known as instantaneous frequencies (IFs). Many applications require the extraction of signal characteristics (i.e. amplitudes and IFs). that is why MCS decomposition is an important topic in signal processing. It consists of the recovery of each individual mode and it is often performed by IFs separation. The task becomes very challenging if the signal modes overlap in the TF domain, i.e. they interfere with each other, at the so-called non-separability region. For this reason, a general solution to MCS decomposition is not available yet. As a matter of fact, the existing methods addressing overlapping modes share the same limitations: they are parametric, therefore they adapt only to the assumed signal class, or they rely on signal-dependent and parametric TF representations; otherwise, they are interpolation techniques, i.e. they almost ignore the information corrupted by interference and they recover IF curve by some fitting procedures, resulting in high computational cost and bad performances against noise. This thesis aims at overcoming these drawbacks, providing efficient tools for dealing with MCS with interfering modes. An extended state-of-the-art revision is provided, as well as the mathematical tools and the main definitions needed to introduce the topic. Then, the problem is addressed following two main strategies: the former is an iterative approach that aims at enhancing MCS' resolution in the TF domain; the latter is a transform-based approach, that combines TF analysis and Radon Transform for separating individual modes. As main advantage, the methods derived from both the iterative and the transform-based approaches are non-parametric, as they do not require specific assumptions on the signal class. As confirmed by the experimental results and the comparative studies, the proposed approach contributes to the current state of the-art improvement

Kombinacija vremensko-frekvencijske analize signala i strojnoga učenja uz primjer u detekciji gravitacijskih valova

This paper presents a method for classifying noisy, non-stationary signals in the time-frequency domain using artificial intelligence. The preprocessed time-series signals are transformed into time-frequency representations (TFrs) from Cohen’s class resulting in the TFr images, which are used as input to the machine learning algorithms. We have used three state-of-the-art deep-learning 2d convolutional neural network (Cnn) architectures (ResNet-101, Xception, and EfficientNet). The method was demonstrated on the challenging task of detecting gravitational-wave (gw) signals in intensive real-life, non-stationary, non-gaussian, and non-white noise. The results show excellent classification performance of the proposed approach in terms of classification accuracy, area under the receiver operating characteristic curve (roC auC), recall, precision, F1 score, and area under the precision-recall curve (PR AUC). The novel method outperforms the baseline machine learning model trained on the time-series data in terms of all considered metrics. The study indicates that the proposed technique can also be extended to various other applications dealing with non-stationary data in intensive noise.Ovaj rad predstavlja metodu klasifikacije šumom narušenih nestacionarnih signala u vremensko-frekvencijskoj domeni korištenjem umjetne inteligencije. Naime, signali u obliku vremenskih nizova transformirani su nakon predobrade u vremensko-frekvencijske prikaze (TFR) iz Cohenove klase, rezultirajući TFR slikama korištenim kao ulaz u algoritme strojnoga učenja. Korištene su tri suvremene metode dubokoga učenja u obliku 2D arhitektura konvolucijskih neuronskih mreža (CNN) (ResNet-101, Xception i EfficientNet). Metoda je demonstrirana na zahtjevnom problemu detekcije signala gravitacijskih valova (GW) u intenzivnom stvarnom i nestacionarnom šumu koji nema karakteristike ni Gaussovog ni bijelog šuma. Rezultati pokazuju izvrsne performanse klasifikacije predloženoga pristupa s obzirom na točnost klasifikacije, površinu ispod krivulje značajke djelovanja prijamnika (ROC AUC), odziv, preciznost, F1-mjeru i površinu ispod krivulje preciznost-odziv (PR AUC). Nova metoda nadmašuje osnovni model strojnoga učenja treniran na podatcima u obliku vremenskih nizova s obzirom na razmatrane metrike. Istraživanje pokazuje da se predložena tehnika može proširiti i na različite druge primjene koje uključuju nestacionarne podatke u intenzivnom šumu

Time Scale Approach for Chirp Detection

International audienceTwo different approaches for joint detection and estimation of signals embedded in stationary random noise are considered and compared, for the subclass of amplitude and frequency modulated signals. Matched filter approaches are compared to time-frequency and time scale based approaches. Particular attention is paid to the case of the so-called " power-law chirps " , characterized by monomial and polynomial amplitude and frequency functions. As target application, the problem of gravitational waves at interferometric detectors is considered

Prevention of extreme roll motion through measurements of ship's motion responses

PhD ThesisExploring the operational links between a sea state and a ship’s heading and speed provides the opportunity to continuously monitor dynamic stability behaviour; and hence to avoid significant changes of stability in adverse weather. Significant changes of stability at sea can lead to dangerous transient situations and eventually stability failure. Despite its importance, the current intact stability (IS) criteria do not evaluate or consider the dynamics of the motion responses of a vessel in a wave environment. In this thesis, the full six degrees of freedom motion responses of two models have been tested in irregular waves under intact vessel conditions. The general modelling approach for a mathematical model was based on numerical simulations at different speeds, sea conditions and angle of heading relative to the waves. In the second model, a physical model was tested in a towing tank under similar simulated environmental conditions to that employed for the first model. The investigation was limited to the effects of encountered frequency components and the associated magnitude of energy of the ship’s motion responses. An analysis of heave, pitch and roll motion confirmed the vulnerability of the model to certain wave-excited frequency ranges. This particular range of frequency results in an adverse effect on the amplitude of the responses, and these were closely related to the natural mode frequencies and related coupling effects. It was confirmed that the roll motion maintains its highest oscillation amplitude at around the natural frequency in all sea conditions regardless of ship heading angles. It was also observed that spectral analysis of the heave and pitch responses revealed the wave peak frequency. Roll is magnified when the peak frequency of the waves approaches the natural roll frequency, therefore keeping them sufficiently apart avoids potentially large motion responses. It was concluded that peak frequency and associated magnitude are the two important inherent characteristics of motion responses. Detection of the most influential parameters of encountered waves through measurements of heave and pitch responses could be utilised to provide a method to limit the large motion of a ship at sea. The measurement of waves whilst a ship is underway is a major challenge, whereas ship motion, which is relatively easily measured, is a good indirect reflection of the encountered wave characteristics and which can be measured, stored and analysed using Prevention of extreme roll motion through measurements of ship’s motion responses iv on-board equipment. Motion responses are considered as continuous signals with a time-dependent spectral content, and signal processing is a suitable technique for detection, estimation and analysis of recorded time-varying signals. The method is fast enough to be considered as an on-board real-time monitoring of dynamic stability. Signal processing techniques are used in the detection and estimation of the influential parameters of a wave environment through the analysis of motion responses. The variables of the system were detected by spectral analysis of the heave and pitch motions. These variables are the peak wave frequencies and associated magnitudes which can cause a large roll motion when reasonably close to the ship’s natural roll frequency. The instantaneous frequency (IF) present in the signal is revealed through spectral analysis of short-time Fourier transforms (STFT) in less than a minute. The IF is a parameter of practical importance which can be used in real-time on-board decision making processes to enable the vessel to take actions in order to avoid large roll motions

Combined-channel instantaneous frequency analysis for audio source separation based on comodulation

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2008.Includes bibliographical references (p. 295-303).Normal human listeners have a remarkable ability to focus on a single sound or speaker of interest and to block out competing sound sources. Individuals with hearing impairments, on the other hand, often experience great difficulty in noisy environments. The goal of our research is to develop novel signal processing methods inspired by neural auditory processing that can improve current speech separation systems. These could potentially be of use as assistive devices for the hearing impaired, and in many other communications applications. Our focus is the monaural case where spatial information is not available. Much perceptual evidence indicates that detecting common amplitude and frequency variation in acoustic signals plays an important role in the separation process. The physical mechanisms of sound generation in many sources cause common onsets/offsets and correlated increases/decreases in both amplitude and frequency among the spectral components of an individual source, which can potentially serve as a distinct signature. However, harnessing these common modulation patterns is difficult because when spectral components of competing sources overlap within the bandwidth of a single auditory filter, the modulation envelope of the resultant waveform resembles that of neither source. To overcome this, for the coherent, constant-frequency AM case, we derive a set of matrix equations which describes the mixture, and we prove that there exists a unique factorization under certain constraints. These constraints provide insight into the importance of onset cues in source separation. We develop algorithms for solving the system in those cases in which a unique solution exists. This work has direct bearing on the general theory of non-negative matrix factorization which has recently been applied to various problems in biology and learning. For the general, incoherent, AM and FM case, the situation is far more complex because constructive and destructive interference between sources causes amplitude fluctuations within channels that obscures the modulation patterns of individual sources.(cont.) Motivated by the importance of temporal processing in the auditory system, and specifically, the use of extrema, we explore novel methods for estimating instantaneous amplitude, frequency, and phase of mixtures of sinusoids by comparing the location of local maxima of waveforms from various frequency channels. By using an overlapping exponential filter bank model with properties resembling the cochlea, and combining information from multiple frequency bands, we are able to achieve extremely high frequency and time resolution. This allows us to isolate and track the behavior of individual spectral components which can be compared and grouped with others of like type. Our work includes both computational and analytic approaches to the general problem. Two suites of tests were performed. The first were comparative evaluations of three filter-bank-based algorithms on sets of harmonic-like signals with constant frequencies. One of these algorithms was selected for further performance tests on more complex waveforms, including AM and FM signals of various types, harmonic sets in noise, and actual recordings of male and female speakers, both individual and mixed. For the frequency-varying case, initial results of signal analysis with our methods appear to resolve individual sidebands of single harmonics on short time scales, and raise interesting conceptual questions on how to define, use and interpret the concept of instantaneous frequency. Based on our results, we revisit a number of questions in current auditory research, including the need for both rate and place coding, the asymmetrical shapes of auditory filters, and a possible explanation for the deficit of the hearing impaired in noise.by Barry David Jacobson.Ph.D

The Hilbert-Huang Transform for Damage Detection in Plate Structures

This thesis investigates the detection of structural damage in plate structures using the empirical mode decomposition method along with the Hilbert spectral analysis. In recent years there have been an extensive amount of research associated with the development of health monitoring methods for aerospace systems, such as aging aircraft and Health and Usage Monitoring Systems (HUMS) for rotorcraft. The method developed here exploits a new time-frequency signal processing analysis tool, the Hilbert-Huang transform, along with the Lamb wave propagation for thin plates. With the use of the wave reflections from discontinuities, damage identification methods were developed to determine the presence, location and extent of damage in isotropic and composite plate structures. The ability of the empirical mode decomposition to extract embedded oscillations, to reveal hidden reflections in the data and to provide a high-resolution energy-time-frequency spectrum is used to describe the Lamb waves interactions with various damaged regions