The paper presents the current state of knowledge concerning the sources of noise generated by wind turbines, force measurement methodology, and assessment of noise onerousness in this type of installation, on the basis of a study concerning a wind farm with five REpower MM92 wind turbines and the electric power of 2 MW and the sound power level of 104.2 dB(A) each. Particular attention was focused on the often discussed problem of presence of infrasound generated by turbines and on the requirements of the applicable reference methodologies for the measurement of wind speed to 5 m/s, while the turbine reaches its full power at speeds above 10 m/s.

The study investigates the use of speech signal to recognise speakers’ emotional states. The introduction includes the definition and categorization of emotions, including facial expressions, speech and physiological signals. For the purpose of this work, a proprietary resource of emotionally-marked speech recordings was created. The collected recordings come from the media, including live journalistic broadcasts, which show spontaneous emotional reactions to real-time stimuli. For the purpose of signal speech analysis, a specific script was written in Python. Its algorithm includes the parameterization of speech recordings and determination of features correlated with emotional content in speech. After the parametrization process, data clustering was performed to allows for the grouping of feature vectors for speakers into greater collections which imitate specific emotional states. Using the t-Student test for dependent samples, some descriptors were distinguished, which identified significant differences in the values of features between emotional states. Some potential applications for this research were proposed, as well as other development directions for future studies of the topic.