Ce manuscrit porte sur la modélisation et l'estimation de certaines non-stationnarités dans les signaux audio. Nous nous intéressons particulièrement à une classe de modèles de sons que nous nommons timbre*dynamique dans lesquels un signal stationnaire, associé au phénomène physique à l'origine du son, est déformé au cours du temps par un opérateur linéaire unitaire, appelé opérateur de déformation, associé à l'évolution temporelle des caractéristiques de ce phénomène physique. Les signaux audio sont modélisés comme des processus gaussiens généralisés et nous donnons dans un premier temps un ensemble d'outils mathématiques qui étendent certaines notions utilisées en traitement du signal au cas des processus stochastiques généralisés.Nous introduisons ensuite les opérateurs de déformations étudiés dans ce manuscrit. L'opérateur de modulation fréquentielle qui est l'opérateur de multiplication par une fonction à valeurs complexes de module unité, et l'opérateur de changement d'horloge qui est la version unitaire de l'opérateur de composition.Lorsque ces opérateurs agissent sur des processus stationnaires les processus déformés possèdent localement des propriétés de stationnarité et les opérateurs de déformation peuvent être approximés par des opérateurs de translation dans les plans temps-fréquence et temps-échelle. Nous donnons alors des bornes pour les erreurs d'approximation correspondantes. Nous développons ensuite un estimateur de maximum de vraisemblance approché des fonctions de dilatation et de modulation. L'algorithme proposé est testé et validé sur des signaux synthétiques et des sons naurels. / This manuscript deals with the modeling and estimation of certain non-stationarities in audio signals. We are particularly interested in a sound class models which we call dynamic*timbre in which a stationary signal, associated with the physical phenomenon causing the sound, is deformed over time by a linear unitary operator, called deformation operator, associated with the temporal evolution of the characteristics of this physical phenomenon.Audio signals are modeled as generalized Gaussian processes. We give first a set of mathematical tools that extend some classical notions used in signal processing in case of generalized stochastic processes.We then introduce the two deformations operators studied in this manuscript. The frequency modulation operator is the multiplication operator by a complex-valued function of unit module and the time-warping operator is the unit version of the composition operator by a bijective function.When these operators act on generalized stationary processes, deformed process are non-stationary generalized process which locally have stationarity properties and deformation operators can be approximated by translation operators in the time-frequency plans and time-scale.We give accurate versions of these approximations, as well as bounds for the corresponding approximation errors.Based on these approximations, we develop an approximated maximum likelihood estimator of the warping and modulation functions. The proposed algorithm is tested and validated on synthetic signals. Its application to natural sounds confirm the validity of the timbre*dynamic model in this context.

This work explores the topic of digital audio signal processing for modular synthesizers and the design of digital oscillator for modular standard known as Eurorack. Introduction of the theoretical part is dedicated to basic terms and blocks used in modular synthesizers. The thesis also characterizes and presents the methods of sound synthesis. The second part of the theory concerns analog and digital signal conversion made by digital signal processors DSP, focusing on ARM with focus on ARM architecture. The practical part of the thesis concerns design and construction of the digital oscillator which generates periodic waveforms without aliasing distortion. The oscillator also allows several types of modulations and waveforming and the module has several inputs for connecting control voltages or external audio signals.

Several applications have been proposed for wireless sensor networks, including habitat monitoring, structural health monitoring, pipeline monitoring, and precision agriculture. Among the desirable features of wireless sensor networks, one is the ease of deployment. Since the nodes are capable of self-organization, they can be placed easily in areas that are otherwise inaccessible to or impractical for other types of sensing systems. In fact, some have proposed the deployment of wireless sensor networks by dropping nodes from a plane, delivering them in an artillery shell, or launching them via a catapult from onboard a ship. There are also reports of actual aerial deployments, for example the one carried out using an unmanned aerial vehicle (UAV) at a Marine Corps combat centre in California -- the nodes were able to establish a time-synchronized, multi-hop communication network for tracking vehicles that passed along a dirt road. While this has a practical relevance for some civil applications (such as rescue operations), a more realistic deployment involves the careful planning and placement of sensors. Even then, nodes may not be placed optimally to ensure that the network is fully connected and high-quality data pertaining to the phenomena being monitored can be extracted from the network. This work aims to address the problem of random deployment through two complementary approaches: The first approach aims to address the problem of random deployment from a communication perspective. It begins by establishing a comprehensive mathematical model to quantify the energy cost of various concerns of a fully operational wireless sensor network. Based on the analytic model, an energy-efficient topology control protocol is developed. The protocol sets eligibility metric to establish and maintain a multi-hop communication path and to ensure that all nodes exhaust their energy in a uniform manner. The second approach focuses on addressing the problem of imperfect sensing from a signal processing perspective. It investigates the impact of deployment errors (calibration, placement, and orientation errors) on the quality of the sensed data and attempts to identify robust and error-agnostic features. If random placement is unavoidable and dense deployment cannot be supported, robust and error-agnostic features enable one to recognize interesting events from erroneous or imperfect data.

Several applications have been proposed for wireless sensor networks, including habitat monitoring, structural health monitoring, pipeline monitoring, and precision agriculture. Among the desirable features of wireless sensor networks, one is the ease of deployment. Since the nodes are capable of self-organization, they can be placed easily in areas that are otherwise inaccessible to or impractical for other types of sensing systems. In fact, some have proposed the deployment of wireless sensor networks by dropping nodes from a plane, delivering them in an artillery shell, or launching them via a catapult from onboard a ship. There are also reports of actual aerial deployments, for example the one carried out using an unmanned aerial vehicle (UAV) at a Marine Corps combat centre in California -- the nodes were able to establish a time-synchronized, multi-hop communication network for tracking vehicles that passed along a dirt road. While this has a practical relevance for some civil applications (such as rescue operations), a more realistic deployment involves the careful planning and placement of sensors. Even then, nodes may not be placed optimally to ensure that the network is fully connected and high-quality data pertaining to the phenomena being monitored can be extracted from the network. This work aims to address the problem of random deployment through two complementary approaches: The first approach aims to address the problem of random deployment from a communication perspective. It begins by establishing a comprehensive mathematical model to quantify the energy cost of various concerns of a fully operational wireless sensor network. Based on the analytic model, an energy-efficient topology control protocol is developed. The protocol sets eligibility metric to establish and maintain a multi-hop communication path and to ensure that all nodes exhaust their energy in a uniform manner. The second approach focuses on addressing the problem of imperfect sensing from a signal processing perspective. It investigates the impact of deployment errors (calibration, placement, and orientation errors) on the quality of the sensed data and attempts to identify robust and error-agnostic features. If random placement is unavoidable and dense deployment cannot be supported, robust and error-agnostic features enable one to recognize interesting events from erroneous or imperfect data.

Dialogue Enhancement (DE) is a process used in audio delivery systems to improve the clarity, intelligibility, and overall quality of the spoken dialogue in audio content. It is primarily used when dialogue is masked by music, surrounding noise, or other audio sources. This thesis project involves experiments to find the optimal time and frequency resolution needed for a DE system. The time resolution focuses on experimenting with various attack/release times for a DE system. The frequency domain analysis investigates whether people prefer a noise spectrum-dependent gain over a conventional full-band gain. The research methodology comprises three main parts. The first part focuses on system setup and choosing content/vectors to be used for the experiments. Next, the experiments are designed for time and frequency resolution. An exponential smoothing model is used to amplify/attenuate the dialogue stream at various times of attack/release. For the frequency counterpart, a banded gain model is designed which uses banded noise levels as input. Subsequently, a modified subjective listening test is designed to evaluate the experiments designed. The responses recorded for various types of content-noise combinations from the listeners are recorded and analyzed. Finally, the main outcome of this research emphasizes the advantages of a DE system. Further, it paves the way for further exploration of DE models and rigorous testing schemes with expert listeners. / Dialogue Enhancement (DE) är en process som används i ljudleveranssystem för att förbättra tydligheten, förståeligheten och den övergripande kvaliteten på den talade dialogen i ljudinnehåll. Det används främst när dialog maskeras av musik, omgivande brus eller andra ljudkällor. Detta examensarbete omfattar experiment för att hitta den optimala tids- och frekvensupplösningen för ett DEsystem. Tidsupplösningsexperimenten fokuserar på olika attack- och releasetider för ett DE-system. Frekvensdomänanalysen undersöker om människor föredrar en brusspektrumberoende förstärkning framför en konventionell fullbandsförstärkning. Forskningsmetodiken består av tre huvuddelar. Den första delen fokuserar på systeminställning och val av innehåll/vektorer som ska användas för experimenten. Därefter designas tids- och frekvensupplösningsexperimenten. En exponentiell tidsenvelopp används för att förstärka/dämpa dialogen vid olika tider för attack/release. För frekvensdomänexperimenten används en bandad förstärkningsmodell som använder bandade brusnivåer som insignal. I den tredje delen utformas ett subjektivt lyssningstest för att utvärdera experimenten. Lyssnarnas svar för olika typer av innehåll-bruskombinationer registreras och analyseras. Det huvudsakliga resultatet av denna forskning betonar fördelarna med ett DEsystem. Vidare banar det väg för utforskning av fler DE-modeller och rigorösa testscheman med expertlyssnare.