The world leading group in laser remote sensing located at Lund University collects hundreds of thousands insect observation per day from airborne insects using entomological lidar measurements. The measurements, where the signals are of time-varying character and suffer from the influence of varying noise and other disturbances, are used for classification of species, sex and age groups. Exploitation relies on robust estimation of the parameters of the time-varying signals and especially the fundamental tone. In this master thesis we suggest an investigation of a novel technique, matched reassignment, which is a mathematical method that relies on the phase of the Fourier transform to reassign the power of the spectrogram. The method has been shown to outperform other time-frequency techniques and it gives accurate estimates of the time- as well as the frequency locations.
Supervisors: Maria Sandsten, firstname.lastname@example.org; Mikkel Brydegaard and Samuel Jansson, Dept. Physics.
Prerequisites: FMSF10/MASC04, FMSN35/MASM26
Resistivity and time domain induced polarization
(DCIP) measurements is a great tool for mapping
properties of the subsurface. This master thesis project invloves analysis of
existiving DCIP data to find suitable machine learning
or signal processing apporaches to deal with non-stationary
disturbances from trams and metro.
Prerequisites: FMSF10/MASC04, (FMS051/MASM17), (FMSN35/MASM26)
Software development for an audio recognition and matching tool -- a chance to develop an application together with the end-user! You will be able to lead a project and develop a tool in close cooperation with the end user and the education team. The intent of the tool is to be able to allow the user to speak the correct number in Swedish.
The objective of this project is to develop an application for audio recognition and matching. The application needs to check "intended" target for speech versus the pronunciation in the area of numbers in Swedish. The purpose is to be able to detect which number the user pronounces in Swedish.
• Research on the state of the art systems in this area, Deep learning and similar approaches.
• Investigate how little user input the system must require in order to yield a good result.
• Evaluation of the system based on precision and ease of use.
Qualifications regarding this opening:
• Experienced with programming
• Experienced with audio signal processing
• Experienced with machine learning
Centre for Mathematical Sciences
Box 118, 221 00 LUND
Phone: 046-222 00 00 (växel)