A Comparison of Nature Inspired Algorithms for Multi-threshold Image Segmentation

In the field of image analysis, segmentation is one of the most important preprocessing steps. One way to achieve segmentation is by mean of threshold selection, where each pixel that belongs to a determined class islabeled according to the selected threshold, giving as a result pixel groups that share visual characteristics in the image. Several methods have been proposed in order to solve threshold selectionproblems; in this work, it is used the method based on the mixture of Gaussian functions to approximate the 1D histogram of a gray level image and whose parameters are calculated using three nature inspired algorithms (Particle Swarm Optimization, Artificial Bee Colony Optimization and Differential Evolution). Each Gaussian function approximates thehistogram, representing a pixel class and therefore a threshold point. Experimental results are shown, comparing in quantitative and qualitative fashion as well as the main advantages and drawbacks of each algorithm, applied to multi-threshold problem.Comment: 16 pages, this is a draft of the final version of the article sent to the Journa

Characterization of Soundscapes in Shallow Water Habitats of the Florida Keys (USA) and Their Influence on the Settlement of Larval Fish and Invertebrates

In recent decades, changes in climate and water quality in Florida Bay and the Florida Keys (FL, USA) caused expansive cyanobacteria blooms that in turn precipitated massive sponge die-offs that drastically altered sponge-dominated hard-bottom communities in south-central Florida Bay. This area served as a model system to explore the effect of ecosystem change and habitat restoration on underwater soundscapes and larval recruitment. I had four main objectives: (1) characterize the underwater soundscapes of three near-shore, benthic habitats: mangrove islands, seagrass meadows, and hard-bottom (Chapter 2); (2) quantify larval settlement within healthy, degraded, and restored hard-bottom areas to test whether habitat degradation altered larval settlement (Chapter 3); (3) empirically test the role of sound in promoting larval recruitment to hard-bottom habitat (Chapter 3); and (4) employ the passive sonar equation and distance sampling techniques to evaluate how the loss of large sponges affected the densities and abundances of snapping shrimp (Chapter 4). I found that near-shore habitats exhibit distinct soundscapes, that habitat degradation alters those soundscapes, and that habitat restoration can reestablish natural soundscapes. Habitat type and time of day significantly affected soundscapes, whereas lunar phase did not. Healthy hard-bottom and mangrove habitats exhibited louder spectra and more snapping shrimp snaps than did degraded hard-bottom or seagrass beds. However, four years after restoration, the acoustic spectra and numbers of snapping shrimp snaps on restored hard-bottom were similar to those of healthy hard-bottom. Habitat quality and moon phase both significantly affected larval recruitment. Overall, healthy hard-bottom habitat attracted significantly more larvae than either degraded or restored hard-bottom, particularly during full moon. Playback of healthy hard-bottom soundscapes within degraded hard-bottom areas prompted higher larval settlement, particularly during the full moon. Estimates of snapping shrimp populations within degraded areas were significantly lower than estimates within healthy areas. Shrimp abundance estimates on healthy hard-bottom sites were one to two orders of magnitude greater than those on degraded sites. These studies demonstrate that tropical coastal habitats differ in soundscape characteristics, that habitat degradation affects soundscapes and the ecological process of larval settlement and recruitment, and that restoration of hard-bottom habitat can aid in returning these functions

Wavelet methods in speech recognition

In this thesis, novel wavelet techniques are developed to improve parametrization of speech signals prior to classification. It is shown that non-linear operations carried out in the wavelet domain improve the performance of a speech classifier and consistently outperform classical Fourier methods. This is because of the localised nature of the wavelet, which captures correspondingly well-localised time-frequency features within the speech signal. Furthermore, by taking advantage of the approximation ability of wavelets, efficient representation of the non-stationarity inherent in speech can be achieved in a relatively small number of expansion coefficients. This is an attractive option when faced with the so-called 'Curse of Dimensionality' problem of multivariate classifiers such as Linear Discriminant Analysis (LDA) or Artificial Neural Networks (ANNs). Conventional time-frequency analysis methods such as the Discrete Fourier Transform either miss irregular signal structures and transients due to spectral smearing or require a large number of coefficients to represent such characteristics efficiently. Wavelet theory offers an alternative insight in the representation of these types of signals. As an extension to the standard wavelet transform, adaptive libraries of wavelet and cosine packets are introduced which increase the flexibility of the transform. This approach is observed to be yet more suitable for the highly variable nature of speech signals in that it results in a time-frequency sampled grid that is well adapted to irregularities and transients. They result in a corresponding reduction in the misclassification rate of the recognition system. However, this is necessarily at the expense of added computing time. Finally, a framework based on adaptive time-frequency libraries is developed which invokes the final classifier to choose the nature of the resolution for a given classification problem. The classifier then performs dimensionaIity reduction on the transformed signal by choosing the top few features based on their discriminant power. This approach is compared and contrasted to an existing discriminant wavelet feature extractor. The overall conclusions of the thesis are that wavelets and their relatives are capable of extracting useful features for speech classification problems. The use of adaptive wavelet transforms provides the flexibility within which powerful feature extractors can be designed for these types of application

Modelling, Simulation and Data Analysis in Acoustical Problems

Modelling and simulation in acoustics is currently gaining importance. In fact, with the development and improvement of innovative computational techniques and with the growing need for predictive models, an impressive boost has been observed in several research and application areas, such as noise control, indoor acoustics, and industrial applications. This led us to the proposal of a special issue about “Modelling, Simulation and Data Analysis in Acoustical Problems”, as we believe in the importance of these topics in modern acoustics’ studies. In total, 81 papers were submitted and 33 of them were published, with an acceptance rate of 37.5%. According to the number of papers submitted, it can be affirmed that this is a trending topic in the scientific and academic community and this special issue will try to provide a future reference for the research that will be developed in coming years

A Survey of Blind Modulation Classification Techniques for OFDM Signals

Blind modulation classification (MC) is an integral part of designing an adaptive or intelligent transceiver for future wireless communications. Blind MC has several applications in the adaptive and automated systems of sixth generation (6G) communications to improve spectral efficiency and power efficiency, and reduce latency. It will become a integral part of intelligent software-defined radios (SDR) for future communication. In this paper, we provide various MC techniques for orthogonal frequency division multiplexing (OFDM) signals in a systematic way. We focus on the most widely used statistical and machine learning (ML) models and emphasize their advantages and limitations. The statistical-based blind MC includes likelihood-based (LB), maximum a posteriori (MAP) and feature-based methods (FB). The ML-based automated MC includes k-nearest neighbors (KNN), support vector machine (SVM), decision trees (DTs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) based MC methods. This survey will help the reader to understand the main characteristics of each technique, their advantages and disadvantages. We have also simulated some primary methods, i.e., statistical- and ML-based algorithms, under various constraints, which allows a fair comparison among different methodologies. The overall system performance in terms bit error rate (BER) in the presence of MC is also provided. We also provide a survey of some practical experiment works carried out through National Instrument hardware over an indoor propagation environment. In the end, open problems and possible directions for blind MC research are briefly discussed

Détection, localisation, caractérisation de transitoires acoustiques sous-marins

The underwater environment is insonified by a wide variety of acoustic sourcesthat can be monitored by autonomous passive acoustic recorders. A large number of the recordedsounds are transient signals (short-finite duration signals), among which the pulse signals that westudy in this thesis. Pulse signals have specific properties, such as a very short duration (<1ms), fewoscillations, a high directivity, which make them difficult to study by classical signal processing tools(Fourier transform, autocorrelation).In the first part of this study, we develop a method to detect sound sources emitting rhythmic pulsetrains (dolphins, sperm whales, beluga whales). This detector uses only the time of arrival of pulses atthe hydrophone to perform a rhythm analysis based on a complex autocorrelation and a time-rhythmrepresentation. This allows : i) to detect rhythmic pulse trains, ii) to know the beginning and endingtimes of pulse trains, iii) to know the value of the rhythm.In the second part of this thesis, we study the potential of a method called Recurrence Plot Analysis tocharacterize waveforms of pulse signals. After a general presentation of this method we develop threesignal processing architectures based on it, to perform the following tasks : i) transient detection, ii)transient characterization and pattern recognition, iii) estimation of time difference of arrival of thetransient on two hydrophones.All the methods developped in this thesis are validated on simulated and real data recorded at sea.Le milieu marin est insonifié par une grand variété de sources acoustiques, qui peuventêtre monitorées par des enregistreurs acoustiques passifs autonomes. Parmi les sons enregistrés, ontrouve un grand nombre de signaux transitoires (signaux éphémères de durée courte), auxquelsappartiennent notamment les signaux impulsionnels que nous étudions dans cette thèse. Les signauximpulsionnels ont des propriétés spécifiques, telles que leur durée très courte (<1ms), leur faiblenombre d’oscillations, leur forte directivité, qui les rendent difficiles à étudier avec les outils detraitement du signal traditionnels (transformée de Fourier, autocorrélation, etc.).Dans un premier temps, nous nous intéressons à la détection des sources qui émettent des sériesd’impulsions rythmées (dauphins, cachalots, bélugas). Cette détection, s’appuie uniquement surles temps d’arrivée des impulsions reçues, pour effectuer une analyse du rythme au moyen d’uneautocorrélation complexe, et construire une représentation temps-rythme, permettant : i) de détecterles rythmes, ii) de connaître les temps de début et fin des émissions rythmées, iii) de connaître lavaleur du rythme et son évolution.Dans un second temps, nous étudions le potentiel d’une technique appelée analyse par récurrence desphases, pour caractériser les formes d’onde des impulsions. Après avoir présenté le cadre général decette méthode d’analyse, nous l’utilisons dans trois chaînes de traitement répondant à chacune destâches suivantes : i) détection des transitoires, ii) caractérisation et reconnaissance des transitoires,iii) estimation des différences des temps d’arrivée des transitoires sur deux capteurs.Toutes les méthodes développées dans cette étude ont été testées et validées sur des données simuléeset sur des données réelles acquises en me

Multi-instance multi-label learning : algorithms and applications to bird bioacoustics

We consider the problem of supervised classification of bird species from audio recordings in a real-world acoustic monitoring scenario (i.e. audio data is collected in the field with an omnidirectional microphone, without human supervision). Obtaining better data about bird activity can assist conservation efforts, and improve our understanding of their interactions with the environment and other organisms. However, traditional observation methods are labor- intensive. Most prior work on machine learning for bird song is not applicable to real-world acoustic monitoring, because it assumes recordings contain only a single species of bird, while recordings typically contain multiple simultaneously vocalizing birds. We propose to use the multi-instance multi-label (MIML) framework in machine learning for the species classification problem, where the dataset is viewed as a collection of bags of instances paired with sets of labels. Furthermore, we formalize MIML instance annotation, where the goal is to predict instance labels while learning only from bag label sets. We develop the first MIML representation for audio, and several new algorithms for MIML instance annotation based on support vector machines or classifier chains. The proposed methods classify either the set of species present in a recording, or individual calls, while learning only from recordings paired with a set of species. This form of training data requires less human effort to obtain than individually labeled calls. These methods are successfully applied to audio collected in the field which included multiple simultaneously vocalizing species. The proposed algorithms for MIML classification are general, and are also applied to object recognition in images