The aim of this research was to develop a method of system identification that would draw inspiration from the approach taken by human experts for simulation model tuning and validation. Human experts are able to utilize their natural pattern recognition ability to identify the various shape attributes, or signatures, of a time series from simulation model outputs. They can also intelligently and effectively perform tasks ranging from system identification to model validation. However, the feature extraction approach employed by them cannot be readily automated due to the difficulty in measuring shape attributes. In order to bridge the gap between the approach taken by human experts and those employed for traditional iterative approaches, a method to quantify the shape attributes was devised. The method presented in this dissertation, the Parameter Signature Isolation Method (PARSIM), uses continuous wavelet transformation to characterize specific aspects of the time series shape through surfaces in the time-scale domain. A salient characteristic of these surfaces is their enhanced delineation of the model outputs and/or their sensitivities. One benefit of this enhanced delineation is the capacity to isolate regions of the time-scale plane, coined as parameter signatures, wherein individual output sensitivities dominate all the others. The parameter signatures enable the estimation of each model parameter error separately with applicability to parameter estimation. The proposed parameter estimation method has unique features, one of them being the capacity for noise suppression, wherein the feature of relying entirely on the time-scale domain for parameter estimation offers direct noise compensation in this domain. Yet another utility of parameter signatures is in measurement selection, whereby the existence of parameter signatures is attributed to the identifiability of model parameters through various outputs. The effectiveness of PARSIM is demonstrated through an array of theoretical models, such as the Lorenz System and the Van der Pol oscillator, as well as through the real-world simulation models of an injection molding process and a jet engine.

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.