The Statistical Signal Processing Group (SSPG) focusses on statistical statistical signal processing of both discrete-time and continuous-time signals, especially spectral analysis and time-frequency analysis of stationary and non-stationary processes, detection theory, parameter estimation and particle filters using sequential Monte Carlo methods. The group has an interest in a wide range of applications, such as in the remote sensing of concealed explosives and narcotics, medical signal processing of EEG, HRV, and ultrasonic signals, compression and analysis of speech and audio signals, as well as speaker recognition.
An application example: classification tool for bird singing
A long-term study of the Great Reed Warbler (Acrocephalus arundinaceus) population in Sweden is ongoing. Among other studies, the bird's song is recorded where the main aim is to understand the role of the song in an ecological and evolutionary context.
A bird's song is used as an identification tool, serving as a recognition signal to indicate the individual, the kinship, and the species. The song is also often used in male-male competition and may play a role into the mate choice of a female.
The studies so far are however impaired by the lack of methods which would automatically and more objectively analyze the song structure. The properties in the song are still an unexplored field, and call for modern program tools. In a pre-study, we have shown that time-frequency analysis is a promising tool in the classification of syllables and identification of unique elements of the song. A first evaluation, showing the reliability of the methods in comparing repertoire size has also been made. The project will focus on the construction of a reliable program tool for analysis and classification of syllables.