Localization and Rendering of Sound Sources in Acoustic Fields

Disertační práce se zabývá lokalizací zdrojů zvuku a akustickým zoomem. Hlavním cílem této práce je navrhnout systém s akustickým zoomem, který přiblíží zvuk jednoho mluvčího mezi skupinou mluvčích, a to i když mluví současně. Tento systém je kompatibilní s technikou prostorového zvuku. Hlavní přínosy disertační práce jsou následující: 1. Návrh metody pro odhad více směrů přicházejícího zvuku. 2. Návrh metody pro akustické zoomování pomocí DirAC. 3. Návrh kombinovaného systému pomocí předchozích kroků, který může být použit v telekonferencích.This doctoral thesis deals with sound source localization and acoustic zooming. The primary goal of this dissertation is to design an acoustic zooming system, which can zoom the sound of one speaker among multiple speakers even when they speak simultaneously. The system is compatible with surround sound techniques. In particular, the main contributions of the doctoral thesis are as follows: 1. Design of a method for multiple sound directions estimations. 2. Proposing a method for acoustic zooming using DirAC. 3. Design a combined system using the previous mentioned steps, which can be used in teleconferencing.

A Novel Combined System of Direction Estimation and Sound Zooming of Multiple Speakers

This article presents a new system for estimation the direction of multiple speakers and zooming the sound of one of them at a time. The proposed system is a combination of two levels; namely, sound source direction estimation, and acoustic zooming. The sound source direction estimation uses so-called the energetic analysis method for estimation the direction of multiple speakers, whereas the acoustic zooming is based on modifying the parameters of the directional audio coding (DirAC) in order to zoom the sound of a selected speaker among the others. Both listening tests and objective assessments are performed to evaluate this system using different time-frequency transforms

ECG Signal Analysis: Enhancement and R-Peak Detection

The project has been inspired by the need to find an efficient method for ECG Signal Analysis which is simple and has good accuracy and less computation time. The initial task for efficient analysis is the removal of noise. It actually involves the extraction of the required cardiac components by rejecting the background noise. Enhancement of signal is achieved by the use of Empirical Mode Decomposition method. The use of EMD was inspired by its adaptive nature. The second task is that of R peak detection which is achieved by the use of Continuous Wavelet Transform. Efficiency of the method is measured in terms of detection error rate. Various other methods of R peak detection like Hilbert Transform and Difference Operation Method are implemented and the results when compared with the Continuous Wavelet Transform prove that CWT is a better method. The simulation is done in MATLAB environment. The experiments are carried out on MIT-BIH database. The results show that our proposed method is very effective and an efficient method for fast computation of R peak detection

Wavelet Decomposition for the Detection and Diagnosis of Faults in Rolling Element Bearings

Condition monitoring and fault diagnosis of equipment and processes are of great concern in industries. Early fault detection in machineries can save millions of dollars in emergency maintenance costs. This paper presents a wavelet-based analysis technique for the diagnosis of faults in rotating machinery from its mechanical vibrations. The choice between the discrete wavelet transform and the discrete wavelet packet transform is discussed, along with the choice of the mother wavelet and some of the common extracted features. It was found that the peak locations in spectrum of the vibration signal could also be efficiently used in the detection of a fault in ball bearings. For the identification of fault location and its size, best results were obtained with the root mean square extracted from the terminal nodes of a wavelet tree of Symlet basis fed to Bayesian classier

Time-Frequency Analysis of Rocket Nozzle Wall Pressures During Start-up Transients

Surveys of the fluctuating wall pressure were conducted on a sub-scale, thrust- optimized parabolic nozzle in order to develop a physical intuition for its Fourier-azimuthal mode behavior during fixed and transient start-up conditions. These unsteady signatures are driven by shock wave turbulent boundary layer interactions which depend on the nozzle pressure ratio and nozzle geometry. The focus however, is on the degree of similarity between the spectral footprints of these modes obtained from transient start-ups as opposed to a sequence of fixed nozzle pressure ratio conditions. For the latter, statistically converged spectra are computed using conventional Fourier analyses techniques, whereas the former are investigated by way of time-frequency analysis. The findings suggest that at low nozzle pressure ratios -- where the flow resides in a Free Shock Separation state -- strong spectral similarities occur between fixed and transient conditions. Conversely, at higher nozzle pressure ratios -- where the flow resides in Restricted Shock Separation -- stark differences are observed between the fixed and transient conditions and depends greatly on the ramping rate of the transient period. And so, it appears that an understanding of the dynamics during transient start-up conditions cannot be furnished by a way of fixed flow analysis