One of the most frequently diagnosed neurodegenerative disorders, along with Alzheimer's disease, is Parkinson's disease. It is a slowly progressing disease of the central nervous system that affects parts of the brain which are responsible for one's motor functions. Despite the frequency of its occurrence among the elderly population, there has not yet been established a universal approach towards its certain diagnostics ante mortem. The study presents a pilot experiment regarding the assessment of the usefulness of simultaneous processing and analysis of speech signal and hand tremor accelerations for patient's screening and monitoring of the progress in healing, using the data acquired with a mid-range Android smartphone. During the study, a mobile device of this kind was used to record the patients of the Department of Neurology, University Hospital of the Jagiellonian University in Kraków and a control group of healthy persons over the age of 50. The samples were then analysed and an attempt towards classification was made using statistical methods and machine learning techniques (PCA, SVM, LDA). It was shown that even for a limited population, the classifier reaches about 85% accuracy. Another topic discussed in the study is the possibility of implementing a fully automated mobile system for the monitoring of the disease's progression. Propositions of further research were also drawn.
Temporomandibular joints are part of the stomatognathic system and play an important role in chewing, swallowing and speech articulating and expressing emotions. Unfortunately, they often do not work properly. Occasional disorder, postural defects, increased muscle tone bearing down due to stress deprivation through such parafunctions as clenching and grinding teeth, long-term chewing gum, nail biting or chewing lips and cheeks can lead to the appearance of dysfunctions in the temporomandibular joints. Analysis of vibrations caused by dysfunctions enables a more accurate diagnosis and an objective way of monitoring the treatment process. The article presents the results of pilot studies carried out in this area by Authors on a group of 13 people (9 women and 4 men) suffering from various diseases within the stomatognathic system. Particular attention was paid to the problems associated with vibroacoustic registration of temporomandibular joint cracks that occurred during the determination of the test methodology.
Nasal blockage belongs to the most common symptoms of nasal diseases in vocal tract area. At the same frequency there appear acoustic symptoms, existing as the change of human voice color. Vocal and articulation disorders of the ear, nose ane throat are usually observed in the form of closed rhinolalia and this observation can be performed both by patients and other listeners as well. Nasal polyps and nasal septum deviation are frequent reason of nasal blockage connected in consequence with decreased nasal ventilation. One of the main principles of the surgical treatment performed in mentioned situations is the restoration of nasal patency. The evaluation of the influence of nasal surgery on intensification of acoustic symptoms depends on verification of parameters of the human speech signal, so it was necessary to apply objective methods. That allowed to combine results of acoustic analysis with patient's subjective feeling and rhinomanometric evaluation of nasal patency. The main purpose of this research was to objectively evaluate the influence of surgical treatment improving nasal patency on deformation of the voice of operated patients.
With the present development of digital registration and methods for processing speech it is possible to make effective objective acoustic diagnostics for medical purposes. These methods are useful as all pathologies and diseases of the human vocal tract influence the quality of a patient's speech signal. Diagnostics of the voice organ can be defined as an unambiguous recognition of the current condition of a specific voice source. Such recognition is based on an evaluation of essential acoustic parameters of the speech signal. This requires creating a vibroacoustic model of selected deformations of Polish speech in relation to specific human larynx diseases. An analysis of speech and parameter mapping in 29-dimensional space is reviewed in this study. Speech parameters were extracted in time, frequency and cepstral (quefrency) domains resulting in diagrams that qualified symptoms and conditions of selected human larynx diseases. The paper presents graphically selected human larynx diseases.speech analysis pathological speech surgical treatment
Developing eective methods for automatic identication of noise sources is currently one of the most important tasks in long-term acoustical climate monitoring of the environment. Manual verication of recorded data, when it comes to proper determination of noise levels, is time-consuming and costly. A possible solution is to use pattern recognition techniques for acoustic signal recorded by a monitoring station. This paper presents usefulness of special directed measurement techniques, acoustic signal processing, and classication methods using articial intelligence (the Sammon mapping) and learning systems methods (Support Vector Machines) in the recognition of corona audible noise from ultra-high voltage AC transmission lines.
Every year billions of birds migrate between their breeding and wintering areas. As birds are an important indicator in nature conservation, migratory bird studies have been conducted for many decades, mostly by bird-ringing programmes and direct observation. However, most birds migrate at night, and therefore much information about their migration is lost. Novel methods have been developed to overcome this difficulty; including thermal imaging, radar, geolocation techniques, and acoustic recognition of bird calls. Many bird species are detected by their characteristic sounds. This method of identification occurs more often than by direct observation, and therefore recordings are widely used in avian research. The commonly used approach is to record the birds automatically, and to manually study the bird sounds in the recordings afterwards (Furnas and Callas 2015, Frommolt 2017). However, the tagging of recordings is a tedious and time-consuming process that requires expert knowledge, and, as a result, automatic detection of flight calls is in high demand. The first experiments towards this used energy thresholds or template matching (Bardeli et al. 2010, Towsey et al. 2012), and later on the machine and deep learning methods were applied (Stowell et al. 2018). Nevertheless, not many studies have focused specifically on night flight calls (Salamon et al. 2016, Lostanlen et al. 2018). Such acoustic monitoring could complement daytime avian research, especially when the field recording station is close to the bird-ringing station, as it is in our project. In this study, we present the initial results of a long-term bird audio monitoring project using automatic methods for bird detection. Passive acoustic recorders were deployed at a narrow spit between a lake and the Baltic sea in Dąbkowice, West Pomeranian Voivodeship, Poland . We recorded bird calls nightly from sunset till sunrise during the passerine autumn migration for 3 seasons. As a result, we collected over 3000 hours of recordings each season. We annotated a subset of over 50 hours, from different nights with various weather conditions. As avian flight calls are sporadic and short, we created a balanced set for training - recordings were divided into partially overlapping 500-ms clips, and we retained all clips containing calls and created about the same number of clips without bird sounds. Different signal representations were then examined (e.g. mel-spectrograms and multitaper). Afterwards, various convolutional neural networks were checked and their performance was compared using the area under the receiver operating characteristic curve (AUC) measure. Moreover, an initial attempt was made to take advantage of the transfer learning from image classification models. The results obtained by the deep learning methods are promising (AUC exceeding 80%), but higher bird detection accuracy is still needed. For a chosen bird species – Song thrush (Turdus philomelos) – we observed a correlation between calls recorded at night and birds caught in the nets during the day. This fact, as well as the promising results from the detection of calls from long-term recordings, indicate that acoustic monitoring of nocturnal birds has great potential and could be used to supplement the research of the phenomenon of seasonal bird migration.
The stomatognathic system represents an important element of human physiology, constituting a part of the digestive, respiratory, and sensory systems. One of the signs of temporomandibular joint disorders (TMD) can be the formation of vibroacoustic and electromyographic (sEMG) phenomena. The aim of the study was to evaluate the effectiveness of temporomandibular joint rehabilitation in patients suffering from locking of the temporomandibular joint (TMJ) articular disc by analysis of vibrations, sEMG registration of masseter muscles, and hypertension of masticatory muscles. In this paper, a new system for the diagnosis of TMD during rehabilitation is proposed, based on the use of vibration and sEMG signals. The operation of the system was illustrated in a case study, a 27-year-old woman with articular dysfunction of the TMJ. The first results of TMD diagnostics using the k-nearest neighbors method are also presented on a group of fifteen people (ten women and five men). Vibroacoustic registration of temporomandibular joints, sEMG registration of masseter muscles, and functional manual analysis of the TMJ were simultaneously assessed before employing splint therapy with stomatognathic physiotherapy. Analysis of vibrations with the monitoring of sEMG in dysfunctions of the TMJ can lead to improve differential diagnosis and can be an objective way of monitoring the rehabilitation process of TMD.
Undertaking long-term acoustic measurements on sites located near an airport is related to a problem of large quantities of recorded data which very often represents information not related to ight operations. In such areas, usually dened as zones of limited use, other sources of noise often exist such as roads or railway lines treated in such context as an acoustic background. Manual verication of such recorded data is a costly and timeconsuming process. Automatic dierentiation of the tested noise source from background and precise recognition of quantitative impact of aircraft noise on the acoustic climate in a particular area is an important task. This paper presents the idea of a method that can be used for identifying aircraft operations (ights, take-os, landings) supported by experimental studies carried out with the use of 3D Microown sound intensity probe and SoundField ST350 ambisonic microphone. The proposed method is based on determining the spatial sound intensity vector in the tested acoustic eld during a monitoring timespan. On the grounds of this information, aircraft operations are marked in a continuous record of noise events.
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