Automatic posture evaluation for professional voice users

Nowadays voice trainings are executed by therapists in the worldwide to their knowledge, experiences and skills. In these therapies, tension spots of a body are evaluated. A body is relieved with guidance of a therapist and applied physical exercises. Breathing exercises and implementations are executed. Breathing and body posture relation hybridization with voice resonance is carried out. Workouts are executed by combining breath, body posture and voice resonance. Trainings are conducted to help subjects improve their voice qualities and to feel correlation between body posture and voice. Enhancements are executed practically by talking or singing a song. In order to master the usage of muscle groups as confident as possible, breathing exercises are required. Afterwards, body locations with the lack of resonances are sought using subjective sense of a therapist and continue to treatment with a focus to chosen locations. After each therapy session, important points are highlighted and if it is needed a schedule of exercises are given to a patient. Voice recordings are saved for the assessment of voice both before and after the therapy. To sum up, weekly improvement of a patient is observed. So far, voice quality evaluation of professional voice users are carried out subjectively. However, classical voice evaluations cannot be done by an objective approach, yet done with therapists' intuition. In this thesis, a measurement system was proposed to evaluate voice quality objectively by the posture of a patient. In the first step, voice quality metrics were determined and it was defined as a human voice which propagates to significant distances without any deterioration. With respect of this, three level of voice quality hypothesis was created. In the first step, a voice which faded out in a patients' body and cannot be propagated to even close proximity points were labeled as low quality voice. In the second step, a voice which has a natural amplitude and reached to some extend distance without any deterioration was labeled as normal quality sound. In the last step, a voice which reached to great extent of distant points in big halls namely opera hall without any deterioration was labeled as high quality voice. In the first stage, in the presence of a professional sound therapist, five people were asked to voice ``aaaaa'' in all three levels. Sounds extracted by the subjective approach of the voice therapist were labeled according to the three classes identified. Thus, a sound quality data set was created. 26 different acoustic features have been extracted from the extracted audio data set. Some of the extracted features were created as features suggested by the voice therapist and voice therapist. Other attributes have been identified in the literature as attributes used to classify different sound properties. 57-dimensional feature array was created from each audio window received from the audio data using 26 attributes. The difference with the reconstructed system is that it can extract attributes in real time. It has been observed that by selecting 5 parameters which are most successful in classifying the sound quality classification of the extracted sound parameters, the feature size is reduced and the classification success is increased. In particular, it has been observed that the energy value of the mel-frequencies generated by the principle of transmitting the emitted energy values to the brain's hearing center by clustering the incoming sound of the human inner ear within certain frequency ranges and thus being audible is a very successful sound quality classifier parameter. The features extracted from voice data labeled by voice therapist were used for the training of an artificial neural network and the system was created to predict voice quality. In the second step, correlation between qualitative parameters of given voice and body postures were examined and results were obtained. In order to extract body posture data, Motion Capture Device(MOCAP) was used. It is a device that extracts angular position information of joints in body to calculate distances between neighboring joints and finds angles between them in real time. In the presence of a voice therapist, five users wearing the MOCAP device were asked to make an ``aaaa'' sound and voice and posture data were collected with eight static postures and two dynamic motions accompanying. The three-dimensional center of mass of the extracted postural data was calculated, and the correlation between each voice parameter and the center of mass of the body in terms of the distance from zero position in each dimension was inspected at and it was observed that some voice parameters are significantly related to the center of mass change of body. An interface was written on the Unity platform to simultaneously record posture data and audio data. The real time posture change at the interface can be observed using an avatar, and the sound data and posture data can be extracted and recorded to the computer at the desired time. The recorded postural data was again analyzed by a built-in function created on the Matlab platform to extract the three-dimensional center of mass deviation data of a body. In the third stage, an objectively sound focus was determined from the instantaneous changes in the body's postures. Thus, a system has been developed in which a wearer of a MOCAP device can sense, which predicts optimal posture. In order to be able to create this system, the audio features extracted during the experiments were fed into the artificial neural network model which was formed in the first stage, which determined the sound quality, and as a consequence the instantaneous sound data was labeled. In respect, the labels of the changing postural parameters were accepted as labels of the instantaneous voice data, and the changed postural parameters were trained in another artificial neural network model. Thus, by testing the changing postural parameters in the model, it is possible to predict what quality natural voice will be likely to emerge at the time of that posture. 10-fold cross-validation score of the artificial neural networks that predict the voice quality was found to be 85.19\% and the 10-fold cross-validation score of the model analyzing the possible voice quality of the postural parameters was found to be 81.33\%. In future work, the objective sound quality will be determined to have more classes than three, and a system will be developed that evaluates a closer objective sound quality to reality. By doing this, the possible sound quality of the posture that the person is in can be examined in more detail and the feedback given to the individual will be elaborated more. At this point, a subject will be able to better predict the possible optimal posture when trying different postures.