Jiří Schimmel has been a doctoral student in the Department of Telecommunications of FEEC BUT since 1999. In 2006 he defends his doctoral thesis on the topic “Audio Effect Synthesis Using Non-Linear Signal Processing” and in 2016 habilitation thesis on “New Methods of Spatial Audio Coding and Rendering”. His professional scientific activity is focused on the research in the area of digital audio signal processing, on the research and development of real-time signal processing systems and multi-channel sound systems. He also cooperates with interior and foreign companies (C-Mexx, DFM, Audified).

Spatial Audio Coding Using Ambisonic

Ambisonic is a mathematically based acoustic signal processing technology that attempts to capture and reproduce information from a complete three-dimensional sound field, including the exact localization of each sound source and the environmental characteristics of the field. Basically this is a simplified solution of the wave equation for the progressive convergent spherical wave using spherical harmonic decomposition of the wave field. Theory and technologies related to ambisonic were developed already in the 1970s but its real-time use has been enabled by modern computing technologies. The output of the coding process are so-called ambisonic components whose number determines the order of the ambisonic as well as accuracy of the encoding and the subsequent reconstruction of the sound field. There are two ways how to obtain the ambisonic components – encoding sound object and capture the sound field using 3D microphone. The encoding process is based on finding weighting factors of ambisonic components according to the position of an audio object. For the 3D sound field capture a set of microphones is used that form virtual 3D microphone whose components are identical to the ambisonic components. The decoding process is based on reconstruction of the sound filed using several sound sources (loudspeakers) which supposes further simplifications. Although the sound field is mathematically fully described in ambisonic, there are still many problems that need to be addressed in its practical use.

His talk takes place on Tuesday, October 2, 2018 at 13:00 in room A113.

Petr Dokládal is a senior researcher with the Center for Mathematical Morphology, a joint research lab of Armines and MINES ParisTech, Paris, France. He graduated from the Technical University in Brno, Czech Republic, in 1994, as a telecommunication engineer, received his Ph.D. degree in 2000 from the Marne la Vallée University, France, in general computer sciences, specialized in image processing and received his habilitation from the ParisEst University in 2013. His research interests include mathematical morphology, image segmentation, object tracking and pattern recognition.

Image processing in Non-Destructive Testing

Non-destructive testing is a frequent task in industry for material control and structure inspection. There are many imaging techniques available to make defects visible. Effort is being made to automatize the process to make it repeatable, more accurate, cheaper and environment friendly. Others techniques (able to work remotely, easier to automatize) are being developed. Most of these techniques are still followed by a visual inspection performed by a qualified personnel.

In the beginning of this talk we will review a few, various inspection techniques used in industry. In the second part we will focus on the detection of cracks. From the image processing angle of view cracks are thin, curvilinear structures. They are not always easy to detect especially when surrounded by noise. We show in this talk how cracks can be detected by using path openings, an operator from mathematical morphology. Then, inspired by the a contrario approach, we will show how to choose a convenient threshold value to obtain a binary result. The a contrario approach, instead of modeling the structures to detect, models the noise to detect structures deviating from the model. In this scope, we assume noise composed of pixels that are independent random variables. Henceforth, cracks that are curvilinear and not necessarily connected sequences of bright pixels, are detected as abnormal sequences of bright pixels. In the second part, a fast approximation of the solution based on parsimonious path openings is shown.

His talk takes place on Tuesday, September 18, 2018 at 13:00 in room A113.