05. Harjoitustyöt ja kurssitutkielmat / Coursework and Term papers, Final projects

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Yliopistossa suoritettujen opintojen harjoitus- ja lopputöitä / Coursework, term papers and final projects completed at the university

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Browsing 05. Harjoitustyöt ja kurssitutkielmat / Coursework and Term papers, Final projects by Degree programme/Major subject "CCIS - Master’s Programme in Computer, Communication and Information Sciences (TS2013)"

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  • Adaptive loudness compensation in audio reproduction
    (2019-05-06) Fierro, Leonardo
     Sähkötekniikan korkeakoulu |
    This work involves the study of the psychoacoustic phenomenon of nonlinear and frequency dependent loudness perception, its modeling, and the use of digital filters to introduce an adaptive compensation based on the reproduction level. Music and sound are mixed and mastered at a particular loudness level, which is usually louder than the level they are commonly played at. This implies a change in the perceived spectral balance of the audio source, which is largest in the bass and sub-bass ranges. As the volume setting in music reproduction is decreased, a loudness compensation filter can be designed to introduce an appropriate boost, so that the low frequencies are still heard well and the perceived spectral balance is preserved. This thesis describes a loudness compensation function derived from the standard equal-loudness level contours and its implementation via a digital first-order shelving filter, and it documents a formal listening test, designed and conducted to validate the accuracy of such a method. The research work was carried out between October 2018 and January 2019, during a visiting period at the Aalto University, Espoo, Finland.
  • Spatial Inference in Large-Scale Sensor Networks using Multiple Hypothesis Testing and Bayesian Clustering
    (2019-05-06) Gölz, Martin
     Sähkötekniikan korkeakoulu |
    In this thesis, we address the problem of statistical inference in large-scale sensor networks observing spatially varying fields. First, we revisit traditional single-sensor hypothesis testing. We then present a multiple hypothesis framework to model spatial fields occurring in a multitude of practical signal processing applications. Observing and monitoring phenomena that occur within a spatial field is essential to a variety of applications. This includes tasks, such as, detecting occupied radio spectrum in shared spectrum environments, identifying regions of poor air quality in environmental monitoring, smart buildings and different Internet of Things (IoT) applications. Many of these practical problems can be modeled using a multiple hypothesis testing framework, with the goal of identifying homogeneous spatial regions within which a defined null hypothesis (e.g. pollution remaining at tolerable level, radio spectrum being unoccupied) is in place, and regions where alternative hypotheses are true. These regions can be formed assessing observations made by multiple sensors placed at distinct locations. To be scalable for largescale sensor networks, we suggest to compute local test statistics, such as, p-values at each individual sensor to avoid communication overhead due to a large number of sensors exchanging their raw measurement data. Individual test statistics are fed to a Fusion Center (FC), which performs the inference. At the FC, statistical inference is performed with a propose a method referred to as “Spatial Inference based on Clustering of p-values (SPACE-COP)” that uses multiple hypothesis testing and Bayesian clustering to detect occurring phenomena of interest within the spatial field. The method identifies homogeneous regions in a field based on similarity in decision statistics and locations of the sensors. The number of clusters, each of which is associated to a hypothesis, is determined by a newly derived Bayesian cluster enumeration criterion that is based on the statistical model that has been derived in this project. An EM-algorithm is developed to compute the probabilities that associate sensors with clusters. We present two different decision criteria, for maximum performance (SPACE-COP) and control of false discoveries (FDR SPACE-COP). The performance of the proposed methods is studied in a series of simulation examples and compared to competitors from the literature. Simulation results demonstrate the validity of proposed SPACE-COP methods also for cases in which the assumption on underlying spatial shape of alternative areas was clearly violated and true alternative areas followed arbitrary and even non-convex shapes. In summary, the derived algorithms are applicable to large-scale sensor networks to perform statistical inference and identify homogeneous regions in an observed phenomenon or field where the null hypothesis does not hold.
  • User Position-Based Loudspeaker Correction
    (2021-12-13) Lindfors, Joel
     Sähkötekniikan korkeakoulu |
    In this thesis, a novel loudspeaker correction system is presented and studied.This correction system uses the location of the user to determine the calibrationparameters. Conventional loudspeaker correction uses a static equalizer to correctfor the coloration of the loudspeaker system at all times. However, the response ofthe loudspeaker changes dynamically with the location of the user. By correctingfor the loudspeaker’s response at multiple locations and changing the calibration inreal-time based on the user’s location, we expect a less colored frequency responsecompared to no applied calibration or conventional calibration methods. Thedeveloped method,User Position-Based LoudspeakerCorrection (UC), producesa flatter frequency response than that of no applied calibration: in one of themeasurement conditions the averages of the ranges of the frequency response (thedifference between the largest and the smallest decibel value) went from 10.3 dBin the non-corrected setting to 4.7 dB in the UC setting. Further, it is shownto outperform a conventional method of correcting for the frequency responseof each point in space by using calibration derived from measurements from apredetermined listening position. The average of the ranges with the conventionalmethod of calibration in the aforementioned listening condition was 6.8 dB. Finally,by interpolating the EQ gains for the calibration from a set of measurements for thesuggested correction method, the system’s resolution could be increased with theresulting calibration still outperforming the conventional correction method. Theaverages of the ranges for the interpolation methods in the aforementioned listeningcondition were around 6.1 dB varying approximately 0.1 dB with the method.