Models of statistical self-similarity for signal and image synthesis

Statistical self-similarity of random processes in continuous-domains is defined through invariance of their statistics to time or spatial scaling. In discrete-time, scaling by an arbitrary factor of signals can be accomplished through frequency warping, and statistical self-similarity is defined by the discrete-time continuous-dilation scaling operation. Unlike other self-similarity models mostly relying on characteristics of continuous self-similarity other than scaling, this model provides a way to express discrete-time statistical self-similarity using scaling of discrete-time signals. This dissertation studies the discrete-time self-similarity model based on the new scaling operation, and develops its properties, which reveals relations with other models. Furthermore, it also presents a new self-similarity definition for discrete-time vector processes, and demonstrates synthesis examples for multi-channel network traffic. In two-dimensional spaces, self-similar random fields are of interest in various areas of image processing, since they fit certain types of natural patterns and textures very well. Current treatments of self-similarity in continuous two-dimensional space use a definition that is a direct extension of the 1-D definition. However, most of current discrete-space two-dimensional approaches do not consider scaling but instead are based on ad hoc formulations, for example, digitizing continuous random fields such as fractional Brownian motion. The dissertation demonstrates that the current statistical self-similarity definition in continuous-space is restrictive, and provides an alternative, more general definition. It also provides a formalism for discrete-space statistical self-similarity that depends on a new scaling operator for discrete images. Within the new framework, it is possible to synthesize a wider class of discrete-space self-similar random fields

Identification of persons via voice imprint

Tato práce se zabývá textově závislým rozpoznáváním řečníků v systémech, kde existuje pouze omezené množství trénovacích vzorků. Pro účel rozpoznávání je navržen otisk hlasu založený na různých příznacích (např. MFCC, PLP, ACW atd.). Na začátku práce je zmíněn způsob vytváření řečového signálu. Některé charakteristiky řeči, důležité pro rozpoznávání řečníků, jsou rovněž zmíněny. Další část práce se zabývá analýzou řečového signálu. Je zde zmíněno předzpracování a také metody extrakce příznaků. Následující část popisuje proces rozpoznávání řečníků a zmiňuje způsoby ohodnocení používaných metod: identifikace a verifikace řečníků. Poslední teoreticky založená část práce se zabývá klasifikátory vhodnými pro textově závislé rozpoznávání. Jsou zmíněny klasifikátory založené na zlomkových vzdálenostech, dynamickém borcení časové osy, vyrovnávání rozptylu a vektorové kvantizaci. Tato práce pokračuje návrhem a realizací systému, který hodnotí všechny zmíněné klasifikátory pro otisk hlasu založený na různých příznacích.This work deals with the text-dependent speaker recognition in systems, where just a few training samples exist. For the purpose of this recognition, the voice imprint based on different features (e.g. MFCC, PLP, ACW etc.) is proposed. At the beginning, there is described the way, how the speech signal is produced. Some speech characteristics important for speaker recognition are also mentioned. The next part of work deals with the speech signal analysis. There is mentioned the preprocessing and also the feature extraction methods. The following part describes the process of speaker recognition and mentions the evaluation of the used methods: speaker identification and verification. Last theoretically based part of work deals with the classifiers which are suitable for the text-dependent recognition. The classifiers based on fractional distances, dynamic time warping, dispersion matching and vector quantization are mentioned. This work continues by design and realization of system, which evaluates all described classifiers for voice imprint based on different features.

Application of cepstral techniques to the automated determination of the sound power absorption coefficient

Includes bibliographical references.This thesis builds on research by Bolton and Gold, who developed the theory of using cepstral analysis to determine the absorption coefficient of elastic porous materials. Jongens, in his Masters thesis, applied this technique to determine the absorption coefficient of asphalt samples mounted in a sample holder at the end of a tube. Jongens and others identified numerous factors that introduced uncertainties into the measurement. These uncertainties fall into two main categories. The first deals with the influences that the links of the measurement chain have on the ability to separate the incident and reflected signal. The second deals with the influence of the air leakage between the tube and the surface under measurement in-situ. This thesis deals with the first category. The objectives of this project are to continue the work of Jongens, to produce an apparatus that can rapidly determine the sound power absorption coefficient by a non-skilled operator in a noisy environment. The results should correlate closely with the standardised impedance tube method, within 0.05 over the range 200 Hz to 2000 Hz. The constraint that the apparatus be usable by a non-skilled operator means that little or no calibration should be required, nor should the microphone need to be handled. This thesis presents a survey of related methods used to determine the sound power absorption coefficient. Theory of the cepstral technique is discussed, along with methods that could be used to improve the accuracy of the technique. Excitation signals that could be used with the cepstral method are put forward. The Inverse Repeat Sequence (IRS) was used to excite the system. It was chosen for its high noise immunity, as well as its complete odd-order non-linearity immunity. Sources of uncertainties from the links of the measurement chain are considered and methods to overcome them are presented. Issues that arise from liftering - cepstral equivalent of windowing - are then highlighted. The apparatus for the cepstral technique and method of standing wave ratios used to determine the absorption coefficient is given. The results obtained using the cepstral technique are correlated with the impedance tube results. It was found that the cepstral method correlates closely with the impedance tube over the range of 200 Hz to 2000 Hz for a wide variety of samples. The apparatus was developed to be used by a non-skilled operator, only requiring the press of a button to perform the measurement. With the high noise immunity of the IRS signal, the measurement could be carried out in a noisy environment

Development of a sensory substitution API

2018 Summer.Includes bibliographical references.Sensory substitution – or the practice of mapping information from one sensory modality to another – has been shown to be a viable technique for non-invasive sensory replacement and augmentation. With the rise in popularity, ubiquity, and capability of mobile devices and wearable electronics, sensory substitution research has seen a resurgence in recent years. Due to the standard features of mobile/wearable electronics such as Bluetooth, multicore processing, and audio recording, these devices can be used to drive sensory substitution systems. Therefore, there exists a need for a flexible, extensible software package capable of performing the required real-time data processing for sensory substitution, on modern mobile devices. The primary contribution of this thesis is the development and release of an Open Source Application Programming Interface (API) capable of managing an audio stream from the source of sound to a sensory stimulus interface on the body. The API (named Tactile Waves) is written in the Java programming language and packaged as both a Java library (JAR) and Android library (AAR). The development and design of the library is presented, and its primary functions are explained. Implementation details for each primary function are discussed. Performance evaluation of all processing routines is performed to ensure real-time capability, and the results are summarized. Finally, future improvements to the library and additional applications of sensory substitution are proposed

Cepstral methods for image feature extraction

Ankara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent University, 2010.Thesis (Master's) -- Bilkent University, 2010.Includes bibliographical references leaves 49-57.Image feature extraction is one of the most vital tasks in computer vision and pattern recognition applications due to its importance in the preparation of data extracted from images. In this thesis, 2D cepstrum based methods (2D mel- and Mellin-cepstrum) are proposed for image feature extraction. The proposed feature extraction schemes are used in face recognition and target detection applications. The cepstral features are invariant to amplitude and translation changes. In addition, the features extracted using 2D Mellin-cepstrum method are rotation invariant. Due to these merits, the proposed techniques can be used in various feature extraction problems. The feature matrices extracted using the cepstral methods are classified by Common Matrix Approach (CMA) and multi-class Support Vector Machine (SVM). Experimental results show that the success rates obtained using cepstral feature extraction algorithms are higher than the rates obtained using standard baselines (PCA, Fourier-Mellin Transform, Fourier LDA approach). Moreover, it is observed that the features extracted by cepstral methods are computationally more efficient than the standard baselines. In target detection task, the proposed feature extraction methods are used in the detection and discrimination stages of a typical Automatic Target Recognition (ATR) system. The feature matrices obtained from the cepstral techniques are applied to the SVM classifier. The simulation results show that 2D cepstral feature extraction techniques can be used in the target detection in SAR images.Çakır, SerdarM.S