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Abstract

The pump performance and occurrence of cavitation directly depends on different operating conditions. To cover a wide range of operation conditions for detecting cavitation in this work, investigations on the effect of various suction valve openings on cavitation in the pump were carried out. In order to analyse various levels of cavitation in different operation conditions, the effect of the decrease in the inlet suction pressure of the centrifugal pump by controlling the inlet suction valve opening was investigated using this experimental setup. Hence, the acoustic and pressure signals under different inlet valve openings and different flow rates, namely, 103, 200, 302 l/min were collected for this purpose. A detailed analysis of the results obtained from the acoustic signal was carried out to predict cavitation in the pump under different operating conditions. Also, the acoustic signal was investigated in time domain through the use of the same statistical features. The FFT technique was used to analyse the acoustic signal in the frequency domain. In addition, in this work an attempt was made to find a relationship between the cavitation and noise characteristics using the acoustic technique for identifying cavitation within a pump.
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Abstract

Speech emotion recognition is an important part of human-machine interaction studies. The acoustic analysis method is used for emotion recognition through speech. An emotion does not cause changes on all acoustic parameters. Rather, the acoustic parameters affected by emotion vary depending on the emotion type. In this context, the emotion-based variability of acoustic parameters is still a current field of study. The purpose of this study is to investigate the acoustic parameters that fear affects and the extent of their influence. For this purpose, various acoustic parameters were obtained from speech records containing fear and neutral emotions. The change according to the emotional states of these parameters was analyzed using statistical methods, and the parameters and the degree of influence that the fear emotion affected were determined. According to the results obtained, the majority of acoustic parameters that fear affects vary according to the used data. However, it has been demonstrated that formant frequencies, mel-frequency cepstral coefficients, and jitter parameters can define the fear emotion independent of the data used.
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Abstract

Although the emotions and learning based on emotional reaction are individual-specific, the main features are consistent among all people. Depending on the emotional states of the persons, various physical and physiological changes can be observed in pulse and breathing, blood flow velocity, hormonal balance, sound properties, face expression and hand movements. The diversity, size and grade of these changes are shaped by different emotional states. Acoustic analysis, which is an objective evaluation method, is used to determine the emotional state of people’s voice characteristics. In this study, the reflection of anxiety disorder in people’s voices was investigated through acoustic parameters. The study is a case-control study in cross-sectional quality. Voice recordings were obtained from healthy people and patients. With acoustic analysis, 122 acoustic parameters were obtained from these voice recordings. The relation of these parameters to anxious state was investigated statistically. According to the results obtained, 42 acoustic parameters are variable in the anxious state. In the anxious state, the subglottic pressure increases and the vocalization of the vowels decreases. The MFCC parameter, which changes in the anxious state, indicates that people can perceive this situation while listening to the speech. It has also been shown that text reading is also effective in triggering the emotions. These findings show that there is a change in the voice in the anxious state and that the acoustic parameters are influenced by the anxious state. For this reason, acoustic analysis can be used as an expert decision support system for the diagnosis of anxiety.
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Abstract

Particle Image Velocimetry is getting more and more often the method of choice not only for visualization of turbulent mass flows in fluid mechanics, but also in linear and non-linear acoustics for non-intrusive visualization of acoustic particle velocity. Particle Image Velocimetry with low sampling rate (about 15Hz) can be applied to visualize the acoustic field using the acquisition synchronized to the excitation signal. Such phase-locked PIV technique is described and used in experiments presented in the paper. The main goal of research was to propose a model of PIV systematic error due to non-zero time interval between acquisitions of two images of the examined sound field seeded with tracer particles, what affects the measurement of complex acoustic signals. Usefulness of the presented model is confirmed experimentally. The correction procedure, based on the proposed model, applied to measurement data increases the accuracy of acoustic particle velocity field visualization and creates new possibilities in observation of sound fields excited with multi-tonal or band-limited noise signals.
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