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Psychoacoustic Characteristics of Tinnitus in Relation to Audiometric Profile

Ristovska Lidija, Jachova Zora, Stojcheska Vase

Archives of Acoustics

2019

Vol. 44, No. 3

419--428

The aim of the study was to examine the relationship between tinnitus pitch and maximum heating loss, frequency range of hearing loss, and the edge frequency of the audiogram, as well as, to analyze tinnitus loudness at tinnitus frequency and normal hearing frequency. The study included 212 patients, aged between 21 to 75 years (mean age of 54.4 ± 13.5 years) with chronic subjective tinnitus and sensorineural hearing loss. For the statistical data analysis we used Chisquare test and Fisher’s exact test with level of significance p < 0.05. Tinnitus pitch corresponding to the frequency range of hearing loss, maximum hearing loss and the edge frequency was found in 70.8%, 37.3%, and 16.5% of the patients, respectively. The majority of patients had tinnitus pitch from 3000 to 8000 Hz corresponding to the range of hearing loss (p < 0.001). The mean tinnitus pitch was 3545 Hz ± 2482. The majority (66%) of patients had tinnitus loudness 4-7 dB SL. The mean sensation level at tinnitus frequency was 4.9 dB SL ± 1.9, and 13 dB SL ± 2.9 at normal heating frequency. Tinnitus pitch corresponded to the frequency range of hearing loss in majority of patients. There was no relationship between tinnitus pitch and the edge frequency of the audiogram. Loudness matching outside the tinnitus frequency showed higher sensation level than loudness matching at tinnitus frequency.

Machine Vision Analysis for Textile Texture Identification

Kandi S G

Fibres & Textiles in Eastern Europe

2011

Vol. 19, no. 6 (89)

53--57

Texture identification and matching a sample fabric within a known collection of produced fabrics is a time-consuming and difficult process as a human activity. In this study, a computational method for textile texture identification is introduced using an image analysis technique. For this purpose, images of fabrics were captured by a digital flat scanner. Texture features were extracted using the Edge frequency and Gray Level Co-occurrence Matrix (GLCM) methods. In this way, a library of texture features was collected. To match a new texture with library samples, the closest texture feature based on Euclidian distance was identified as the fabric texture. Experimental results for 33 different textures showed the successful identification of textures with both methods. However, the edge frequency method is more feasible and acceptable due to its computational simplicity and lower processing time. In addition, it was shown that the edge frequency method is extremely insensitive to the colour and scanning direction of the fabric.

Identyfikacja struktury powierzchni tkanin jest zajęciem pracochłonnym. Opracowano metodę identyfikacji struktury wykorzystując metodę automatycznej analizy obrazu. W celu identyfikacji wykonuje się zdjęcia skaningowe powierzchni materiałów, które następnie ulegają obróbce z wykorzystaniem metody wykrywania krawędzi i metody macierzy zależności poziomów szarości. W ten sposób opracowane informacje o poszczególnych strukturach zostają wprowadzone do komputerowej bazy danych, gdzie są porównywane z odpowiednimi wzorcami. Wprowadzenie 33 obrazów różnych struktur wykazało prawidłowe działanie układu. Niemniej należy zaznaczyć, ze metoda z wykrywaniem krawędzi jest bardziej użyteczna ze względu na prostszy sposób przetwarzania i krótszy czas obróbki. W dodatku stwierdzono, ze metoda ta jest niewrażliwa na kolor materiału i kierunek detekcji.