ABSTRACT
O Método Multiparamétrico de Avaliação Vocal Objetiva Assistida (EVA) foi projetado para o estudo da maioria dos parâmetros de produção da fala. OBJETIVO: Definir as medidas médias dos parâmetros eletroglotográficos em falantes do português brasileiro para o EVA. MATERIAL E MÉTODO: Foram analisadas 40 vozes, 20 homens e 20 mulheres sem queixa vocal, extraindo-se as medidas eletroglotográficas, a fim de obter valores de referência de normalidade. Estudo de caso: estudo descritivo com corte transversal. RESULTADOS: Os valores médios de normalidade encontrados nas vozes masculinas foram: F0 = 127,77 Hz, coeficiente de variação de F0 = 2,51%, jitter absoluto = 1,707 Hz, perturbação média relativa = 0,0083, jitter factor = 1,34%, jitter ratio = 13,45%, e QF = 0,447. Para vozes femininas, foram: F0 = 204,87 Hz, coeficiente de variação de F0 = 1,58%, jitterabsoluto = 3,30 Hz, perturbação média relativa = 0,0102, jitter factor = 1,60%, jitter ratio = 16,23%, e QF = 0,443. O tipo de onda foi em 100% da amostra classificada como pulso inclinado em ambos os gêneros. CONCLUSÃO: Houve diferença estatisticamente significante em relação ao gênero para os parâmetros de média F0 e jitterabsoluto. Ao utilizar um programa de análise acústica, os usuários devem basear-se em parâmetros inerentes ao próprio programa para realizar a análise dos dados coletados.
EVA was designed to study various speech production parameters. OBJECTIVE: This paper aims to define the mean values for electroglottography tests of Brazilian Portuguese speakers on EVA. MATERIALS AND METHOD: The voices of 20 men and 20 women without voice-related complaints were analyzed through electroglottography so as to obtain reference values for normality. Case study: this is a descriptive cross-sectional study. RESULTS: The mean values for normal male voices were: F0 = 127.77 Hz; F0 coefficient of variation = 2.51%; absolute jitter = 1.707 Hz; relative average perturbation = 0.0083; jitter factor = 1.34%; jitter ratio = 13.45%; QF = 0.447. The values for female voices were: F0 = 204.87 Hz; F0 coefficient of variation = 1.58%; absolute jitter = 3.30Hz; relative average perturbation = 0.0102; jitter factor = 1.60%; jitter ratio = 16.23%; QF= 0.443. Wave type for the entire sample was categorized as tilted pulse. CONCLUSION: Statistically significant differences were found for gender on parameters average FO and absolute jitter. While using acoustic analysis software, users must be based on parameters inherent to the software program when analyzing the collected data.
Subject(s)
Adolescent , Adult , Female , Humans , Male , Middle Aged , Young Adult , Glottis/physiology , Sound Spectrography/instrumentation , Voice Quality/physiology , Brazil , Cohort Studies , Cross-Sectional Studies , Reference Values , Sound Spectrography/methodsABSTRACT
The present report describes the development of a technique for automatic wheezing recognition in digitally recorded lung sounds. This method is based on the extraction and processing of spectral information from the respiratory cycle and the use of these data for user feedback and automatic recognition. The respiratory cycle is first pre-processed, in order to normalize its spectral information, and its spectrogram is then computed. After this procedure, the spectrogram image is processed by a two-dimensional convolution filter and a half-threshold in order to increase the contrast and isolate its highest amplitude components, respectively. Thus, in order to generate more compressed data to automatic recognition, the spectral projection from the processed spectrogram is computed and stored as an array. The higher magnitude values of the array and its respective spectral values are then located and used as inputs to a multi-layer perceptron artificial neural network, which results an automatic indication about the presence of wheezes. For validation of the methodology, lung sounds recorded from three different repositories were used. The results show that the proposed technique achieves 84.82% accuracy in the detection of wheezing for an isolated respiratory cycle and 92.86% accuracy for the detection of wheezes when detection is carried out using groups of respiratory cycles obtained from the same person. Also, the system presents the original recorded sound and the post-processed spectrogram image for the user to draw his own conclusions from the data.
Subject(s)
Humans , Diagnosis, Computer-Assisted/methods , Respiratory Sounds/diagnosis , Signal Processing, Computer-Assisted , Sound Spectrography/methods , Algorithms , Reproducibility of Results , Sensitivity and Specificity , Sound Spectrography/instrumentationABSTRACT
Measurements of noise levels associated with different types of vehicles plying the roads in Delhi were made. From the data, noise level indices L(10) , L(90) and Leq were determined. In addition, spectra of noise for different vehicles at 1- octave band frequencies were also obtained. The time-averaged noise spectra reveal that the noise intensities are significantly higher in the frequency range of 0.5 kHz to 2 kHz for all types of vehicles. Perceived noise levels (PNdB) and the total noisiness measured on NOY scale indicate that rural transport vehicles (RTVs) are most annoying, followed by buses, auto-rickshaws and taxis.