Tadeusz Wszolek

Recognition of defects in high voltage transmission lines using the acoustic signal of corona effect

The paper deals with the analysis of the possible application of neural networks to the recognition of typical damage of UHV transmission lines. The acoustic signal generated as a result of corona effects is used as a damage symptom, as its intensity is usually increased after damage occurrence or after contamination of the surface of a conductor or an insulator string. The primary problem in the diagnostic process is the distinguishing between signals generated as results of damage and contamination. The problem is not solved by methods based on the RF signal interference or by the classical methods of acoustic signal analysis. The construction and verification of the assumed diagnostic model have been carried out by experimental studies in laboratory conditions, where typical damage and contamination of the transmission line elements have been simulated.

Processing and classification of deformed speech using neural networks

In many problems of medical diagnosis as well as therapy and rehabilitation, evaluation of the deformed speech signal quality is required. In problems of distorted speech diagnosis the regular methods of speech signal preprocessing and classification, used in speech or voice recognition, totally fail. Also the standard speech signal parametrization techniques (e.g. LPC or cepstral coefficients) cannot satisfactorily describe pathological speech because of its dissimilar phonetic and acoustic structure compared with correct speech, and also because the aim of the recognition process is totally different. In the paper a new method for processing and classification of pathologically deformed speech, based on neural networks techniques, is presented and discussed.

Uncertainty of airborne sound insulation index measurement in laboratory conditions

In buildings, airborne sound insulation is used to define the acoustic quality of walls between rooms. However the evaluation of sound insulation is sometimes difficult or even ambiguous, both in field and laboratory measurements, in spite of the fact that there are some unified measurement procedures specified in the ISO 140 standards. There are problems with the reproducibility and repeatability of the measured results. Some difficulties may be caused by non‐diffuse acoustic fields, non uniform reverberation time or large spread of the reverberation time measurements especially in low frequency band. Some minor problems are also posed by flanking transmission and the S/N ratio. In the present work partial uncertainty analysis has been carried out for all the above mentioned factors and their influence has been evaluated on the combined uncertainty in 1/3 octave bands and the Rw index, using the uncertainty propagation law

Accuracy of Assessing the Level of Impulse Sound From Distant Sources

Impulse sound events are characterised by ultra high pressures and low frequencies. Lower frequency sounds are generally less attenuated over a given distance in the atmosphere than higher frequencies. Thus, impulse sounds can be heard over greater distances and will be more affected by the environment. To calculate a longterm average immission level it is necessary to apply weighting factors like the probability of the occurrence of each weather condition during the relevant time period. This means that when measuring impulse noise at a long distance it is necessary to follow environmental parameters in many points along the way sound travels and also to have a database of sound transfer functions in the long term. The paper analyses the uncertainty of immission measurement results of impulse sound from cladding and destroying explosive materials. The influence of environmental conditions on the way sound travels is the focus of this paper.

The analysis of the environmental influence of drilling installation

Drilling work, due to its character, settlement, and whole‐day service, has potential acoustic danger for the environment. The degree of influence depends on many factors, e.g., the type of drilling equipment, applied acoustic screens, localization of drilling and its surroundings, the type of drilled‐trough rocks, and applied drilling technology. The main sources of acoustic noise are motors, pumps, and current generators. The distinction can be made between some procedures that are more noisy than the normal drilling process, for example, tripping. Usually the drilling rig is poorly equipped with noise reduction installation. Experiments were carried out on eight types of drilling assemblies. The received results show that different drilling equipment has an individual influence on the environment. The evident proof of that is the azimuthal acoustic characteristic and additionally the noise level dependence from the cycle of drilling work. The level of LAeq in the surroundings of the examined drilling r...

The methods of pathological speech visualization [using Kohonen neural networks]

In tasks related to the analysis and recognition of pathological speech it is often more important to provide the respective person (e.g. physician) with guidelines for a qualitative evaluation of this speech than to achieve a very accurate automated recognition. By ear it is easy to judge whether the speech is regular or deformed, but any attempt of a quantitative evaluation is not satisfactory. If the speech is transformed to a graphic form, by a proper visualization method, it is easier for a person to estimate its deformation degree by comparing the respective graphical patterns. The new visualization method proposed is based on the results obtained by application of Kohonen neural networks.

Automatic Understanding of Speech Pathology

The paper presents selected aspects of research concerning a new concept in application of computer technology to the analysis of pathological speech. This new concept assumes, that during the analysis of pathological speech the study is not focused on determining some or other signal parameters, neither it is focused on the signal classification, but it is supposed to lead to an automated understanding of the origins of the deformation, which can be revealed in analyzed signal. The presented concept therefore assumes a replacement of the well-known process of recognition of the pathological speech signal by a more advanced method of its analysis, consisting of comparison of the features revealed during the signal processing with a set of expected features resulting from application of the system’s knowledge database, provided that a specific, selected medical hypothesis is true, and concerning the pathological factors affecting the form of that speech signal. In the paper the basic elements of the proposed method are described. The examples, showing the essence of the method, have been taken from applications to selected problems of larynx pathology analyses.