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51	Towards a Nuanced Evaluation of Voice Activity Detection Systems : An Examination of Metrics, Sampling Rates and Noise with Deep Learning / Mot en nyanserad utvärdering av system för detektering av talaktivitet Joborn, Ludvig, Beming, Mattias January 2022 (has links) Recently, Deep Learning has revolutionized many fields, where one such area is Voice Activity Detection (VAD). This is of great interest to sectors of society concerned with detecting speech in sound signals. One such sector is the police, where criminal investigations regularly involve analysis of audio material. Convolutional Neural Networks (CNN) have recently become the state-of-the-art method of detecting speech in audio. But so far, understanding the impact of noise and sampling rates on such methods remains incomplete. Additionally, there are evaluation metrics from neighboring fields that remain unintegrated into VAD. We trained on four different sampling rates and found that changing the sampling rate could have dramatic effects on the results. As such, we recommend explicitly evaluating CNN-based VAD systems on pertinent sampling rates. Further, with increasing amounts of white Gaussian noise, we observed better performance by increasing the capacity of our Gated Recurrent Unit (GRU). Finally, we discuss how careful consideration is necessary when choosing a main evaluation metric, leading us to recommend Polyphonic Sound Detection Score (PSDS). voice activity detection VAD deep learning machine learning ML artificial intelligence AI convolutional neural network CNN deep neural network DNN sound event detection SED mel spectrogram audio processing polyphonic sound detection score PSDS signal processing signal to noise ratio SNR RCRNN sampling rate Gaussian noise Computer Sciences Datavetenskap (datalogi)
52	Modelling of Mobile Fading Channels with Fading Mitigation Techniques. Shang, Lei, lei.shang@ieee.org January 2006 (has links) This thesis aims to contribute to the developments of wireless communication systems. The work generally consists of three parts: the first part is a discussion on general digital communication systems, the second part focuses on wireless channel modelling and fading mitigation techniques, and in the third part we discuss the possible application of advanced digital signal processing, especially time-frequency representation and blind source separation, to wireless communication systems. The first part considers general digital communication systems which will be incorporated in later parts. Today's wireless communication system is a subbranch of a general digital communication system that employs various techniques of A/D (Analog to Digital) conversion, source coding, error correction, coding, modulation, and synchronization, signal detection in noise, channel estimation, and equalization. We study and develop the digital communication algorithms to enhance the performance of wireless communication systems. In the Second Part we focus on wireless channel modelling and fading mitigation techniques. A modified Jakes' method is developed for Rayleigh fading channels. We investigate the level-crossing rate (LCR), the average duration of fades (ADF), the probability density function (PDF), the cumulative distribution function (CDF) and the autocorrelation functions (ACF) of this model. The simulated results are verified against the analytical Clarke's channel model. We also construct frequency-selective geometrical-based hyperbolically distributed scatterers (GBHDS) for a macro-cell mobile environment with the proper statistical characteristics. The modified Clarke's model and the GBHDS model may be readily expanded to a MIMO channel model thus we study the MIMO fading channel, specifically we model the MIMO channel in the angular domain. A detailed analysis of Gauss-Markov approximation of the fading channel is also given. Two fading mitigation techniques are investigated: Orthogonal Frequency Division Multiplexing (OFDM) and spatial diversity. In the Third Part, we devote ourselves to the exciting fields of Time-Frequency Analysis and Blind Source Separation and investigate the application of these powerful Digital Signal Processing (DSP) tools to improve the performance of wireless communication systems. Multipath interference channel modelling fading channel capacity channel tracking channel estimation Doppler spread coherence time delay spread detection autocorrelation function (ACF) level-crossing rate (LCR) average duration of fades (ADF) diversity error correction coding MIMO (Multi-Input and Multi-Output) Frequency Division Multiplexing) MIMO (Multi-Input and Multi-Output) additive Gaussian noise signal-to-noise-ratio (SNR) Sigma-Delta quantization blind source separation time-frequency distribution (TFD).
53	Parameters Selection for Optimising Time-Frequency Distributions and Measurements of Time-Frequency Characteristics of Nonstationary Signals Sucic, Victor January 2004 (has links) The quadratic class of time-frequency distributions (TFDs) forms a set of tools which allow to effectively extract important information from a nonstationary signal. To determine which TFD best represents the given signal, it is a common practice to visually compare different TFDs' time-frequency plots, and select as best the TFD with the most appealing plot. This visual comparison is not only subjective, but also difficult and unreliable especially when signal components are closely-spaced in the time-frequency plane. To objectively compare TFDs, a quantitative performance measure should be used. Several measures of concentration/complexity have been proposed in the literature. However, those measures by being derived with certain theoretical assumptions about TFDs are generally not suitable for the TFD selection problem encountered in practical applications. The non-existence of practically-valuable measures for TFDs' resolution comparison, and hence the non-existence of methodologies for the signal optimal TFD selection, has significantly limited the use of time-frequency tools in practice. In this thesis, by extending and complementing the concept of spectral resolution to the case of nonstationary signals, and by redefining the set of TFDs' properties desirable for practical applications, we define an objective measure to quantify the quality of TFDs. This local measure of TFDs' resolution performance combines all important signal time-varying parameters, along with TFDs' characteristics that influence their resolution. Methodologies for automatically selecting a TFD which best suits a given signal, including real-life signals, are also developed. The optimisation of the resolution performances of TFDs, by modifying their kernel filter parameters to enhance the TFDs' resolution capabilities, is an important prerequisite in satisfying any additional application-specific requirements by the TFDs. The resolution performance measure and the accompanying TFDs' comparison criteria allow to improve procedures for designing high-resolution quadratic TFDs for practical time-frequency analysis. The separable kernel TFDs, designed in this way, are shown to best resolve closely-spaced components for various classes of synthetic and real-life signals that we have analysed. Time-frequency distribution nonstationary signal monocomponent signal multicomponent signal frequency modulated (FM) signal linear FM signal non-linear FM signal kernel filter separable kernel Fourier transform crossterms autoterms concentration resolution mainlobe amplitude sidelobe amplitude instantaneous frequency instantaneous bandwidth crossterms amplitude comparison criteria performance measure parameter optimisation optimal time-frequency distribution design requirements additive white Gaussian noise real-life signal Modified B distribution separable kernel TFD.
54	Contribution à la théorie des ondelettes : application à la turbulence des plasmas de bord de Tokamak et à la mesure dimensionnelle de cibles / Contribution to the wavelet theory : Application to edge plasma turbulence in tokamaks and to dimensional measurement of targets Scipioni, Angel 19 November 2010 (has links) La nécessaire représentation en échelle du monde nous amène à expliquer pourquoi la théorie des ondelettes en constitue le formalisme le mieux adapté. Ses performances sont comparées à d'autres outils : la méthode des étendues normalisées (R/S) et la méthode par décomposition empirique modale (EMD).La grande diversité des bases analysantes de la théorie des ondelettes nous conduit à proposer une approche à caractère morphologique de l'analyse. L'exposé est organisé en trois parties.Le premier chapitre est dédié aux éléments constitutifs de la théorie des ondelettes. Un lien surprenant est établi entre la notion de récurrence et l'analyse en échelle (polynômes de Daubechies) via le triangle de Pascal. Une expression analytique générale des coefficients des filtres de Daubechies à partir des racines des polynômes est ensuite proposée.Le deuxième chapitre constitue le premier domaine d'application. Il concerne les plasmas de bord des réacteurs de fusion de type tokamak. Nous exposons comment, pour la première fois sur des signaux expérimentaux, le coefficient de Hurst a pu être mesuré à partir d'un estimateur des moindres carrés à ondelettes. Nous détaillons ensuite, à partir de processus de type mouvement brownien fractionnaire (fBm), la manière dont nous avons établi un modèle (de synthèse) original reproduisant parfaitement la statistique mixte fBm et fGn qui caractérise un plasma de bord. Enfin, nous explicitons les raisons nous ayant amené à constater l'absence de lien existant entre des valeurs élevées du coefficient d'Hurst et de supposées longues corrélations.Le troisième chapitre est relatif au second domaine d'application. Il a été l'occasion de mettre en évidence comment le bien-fondé d'une approche morphologique couplée à une analyse en échelle nous ont permis d'extraire l'information relative à la taille, dans un écho rétrodiffusé d'une cible immergée et insonifiée par une onde ultrasonore / The necessary scale-based representation of the world leads us to explain why the wavelet theory is the best suited formalism. Its performances are compared to other tools: R/S analysis and empirical modal decomposition method (EMD). The great diversity of analyzing bases of wavelet theory leads us to propose a morphological approach of the analysis. The study is organized into three parts. The first chapter is dedicated to the constituent elements of wavelet theory. Then we will show the surprising link existing between recurrence concept and scale analysis (Daubechies polynomials) by using Pascal's triangle. A general analytical expression of Daubechies' filter coefficients is then proposed from the polynomial roots. The second chapter is the first application domain. It involves edge plasmas of tokamak fusion reactors. We will describe how, for the first time on experimental signals, the Hurst coefficient has been measured by a wavelet-based estimator. We will detail from fbm-like processes (fractional Brownian motion), how we have established an original model perfectly reproducing fBm and fGn joint statistics that characterizes magnetized plasmas. Finally, we will point out the reasons that show the lack of link between high values of the Hurst coefficient and possible long correlations. The third chapter is dedicated to the second application domain which is relative to the backscattered echo analysis of an immersed target insonified by an ultrasonic plane wave. We will explain how a morphological approach associated to a scale analysis can extract the diameter information Ondelettes Principe d'incertitude de Heisenberg Pavage temps-Fréquence Moments Régularité Support compact Fonction d'échelle Fonction d'ondelette Approximations Détails Résolution Filtre QMF Coefficients de Daubechies Analyse Reconstruction Précurseur Algorithme de Mallat Convolution Décimation Symétrisation Orthogonalisation Gram Schmidt Filtres frontières Complexité Décomposition modale empirique Méthode R/S Analyse des fluctuations redressées Fractal Auto-Similarité Plasma Bruit Gaussien fractionnaire Mouvement Brownien fractionnaire Ultrason Wavelets Heisenberg uncertainty principle Time-Frequency tiles Vanishing moments Regularity Compact support Scaling function Wavelet function Approximations Details Resolution QMF filters Daubechies coefficients Analysis Reconstruction Precursor Mallat algorithm Convolution Decimation Mirroring Orthogonalization Gram Schmidt Boundary filters Complexity Empirical Modal Decomposition R/S method Detrended fluctuations Analysis Fractal Self-Similarity Plasma Fractional Gaussian noise Fractional Brownian motion Ultrasound 530.44 515.243 3

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