Cepstral Signal Research Articles

Mean texture depth measurement with an acoustical-based apparatus using cepstral signal processing and support vector machine

In this study, an acoustical-based macrotexture measurement method that uses the friction-induced mechanism of tire/road noise has been proposed. This mechanism influences the frequencies above 1 KHz and is the dominant mechanism for frequencies above 3 KHz. The airflow due to this mechanism passes through the macrotexture which can be represented using convolution in the time-domain of the airflow, which is viewed as the input signal, and macrotexture, which is considered as the transmission channel. Using the Cepstral processing method, the related features to macrotexture airflow can be extracted and the macrotexture can be evaluated. To investigate the proposed method, interaction noise was measured for six uniform non-porous asphaltic test tracks with different macrotextures. The tire/road noise signal is passed through a bandpass filter (3–5 KHz). By liftering the cepstrum components in the range of 2–50, the feature vector has been obtained and then used as input to a multiclass SVM, resulting in a precision error of 4%.

Dense-graded asphalt pavement macrotexture measurement using tire/road noise monitoring

Data collection and the associated automated methods are one of the main building blocks of pavement management systems. One of the main problems associated with automated data collection methods is the high cost of the equipment involved. The aim of this research is to develop a cost-effective system for dense-graded asphalt pavement macrotexture monitoring. To this end, a system based on the tire/road noise, utilizing microphones and employing cepstral signal processing has been developed. The proposed method is compared with the current state of the art PCA based method and is shown that the precision error of the proposed method is about 7%, which outperforms the previous state of the art. To enhance the precision of the cepstral method for the vehicle speed variation amongst the collected dataset, the combination of the cepstral signal processing with Gaussian mixture models is proposed which results in the final precision error of 8%.

Detection and Identification System of Bacteria and Bacterial Endotoxin Based on Raman Spectroscopy

Sepsis is a global health problem that causes risk of death. In the developing world, about 60 to 80 % of death cases are caused by Sepsis. Rapid methods for detecting its causes, represent one of the major factors that may reduce Sepsis risks. Such methods can provide microbial detection and identification which is critical to determine the right treatment for the patient. Microbial and Pyrogen detection is important for quality control system to ensure the absence of pathogens and Pyrogens in the manufacturing of both medical and food products. Raman spectroscopes represent a q uick and accurate identification and detection method, for bacteria and bacterial endotoxin, which this plays an important role in delivering high quality biomedical products using the power of Raman spectroscopy. It is a rapid method for chemical structure detection that can be used in identifying and classifying bacteria and bacterial endotoxin. Such a method acts as a solution for time and cost effective quality control procedures. This work presents an automatic system based on Raman spectroscopy to detect and identify bacteria and bacterial endotoxin. It uses the frequency properties of Raman scattering through the interaction between organic materials and electromagnetic waves. The scattered intensities are measured and wave number converted into frequency, then the cepstral coefficients are extracted for both the detection and identification. The methodology depends on normalization of Fourier transformed cepstral signal to extract their classification features. Experiments’ results proved effective identification and detection of bacteria and bacterial endotoxin even with concentrations as low as 0.0003 Endotoxin unit (EU)/ml and 1 Colony Forming Unit (CFU)/ml using signal processing based enhancement technique.

Aerodynamic and Acoustic Features of Vocal Effort

The purpose of this study was to determine the aerodynamic and acoustic features of speech produced at comfortable, maximal and minimal levels of vocal effort. Prospective, quasi-experimental research design. Eighteen healthy participants with normal voice were included in this study. After task training, participants produced repeated syllable combinations at comfortable, maximal and minimal levels of vocal effort. A pneumotachometer and vented (Rothenberg) mask were used to record aerodynamic data, with simultaneous recording of the acoustic signal for subsequent analysis. Aerodynamic measures of subglottal pressure, translaryngeal airflow, maximum flow declination rate (MFDR), and laryngeal resistance were analyzed, along with acoustic measures of cepstral peak prominence (CPP) and its standard deviation (SD). Participants produced significantly greater subglottal pressure, translaryngeal airflow, and MFDR during maximal effort speech as compared with comfortable vocal effort. When producing speech at minimal vocal effort, participants lowered subglottal pressure, MFDR, and laryngeal resistance. Acoustic changes associated with changes in vocal effort included significantly higher CPP during maximal effort speech and significantly lower CPP SD during minimal effort speech, when each was compared with comfortable effort. For healthy speakers without voice disorders, subglottal pressure, translaryngeal airflow, and MFDR may be important factors that contribute to an increased sense of vocal effort. Changes in the cepstral signal also occur under conditions of increased or decreased vocal effort relative to comfortable effort.

Measuring the Fundamental Tone of Voice Signal

Measurements of the fundamental tone of voice signal were considered. A modification of the cepstral signal analysis, the method of determination of the fundamental tone through the linear high-frequency filtering of the logarithmic spectrum with subsequent nonlinear processing, was proposed.

HF multipath dispersion measurements using cepstral signal processing

A technique for measuring ionospheric echo arrival times and strengths is described. The technique uses ordinary HF‐SSB transceivers and an easy to generate probe signal. It is based on the cepstral signal processing tool for deconvolution. Identification of ionospheric echoes in a received sky wave signal was viewed as a deconvolution problem, and the cepstral transformation was used to obtain information about the multipath profile of the channel. Field tests were carried out where multipath characteristics of the ionospheric channel was measured using this technique.

Estimation of mean scatterer size from sparse, random distributions of polystyrene spheres in solution via Mie scattering and cepstral signal processing: Theory and experimental results

Four solutions of narrowly distributed polystyrene spheres of one diameter, either 41, 136, 271, or 381 μm, sparsely suspended in degassed water, were imaged using multiple 5‐ and 7.5‐MHz medical ultrasonic transducers. Radio‐frequency backscattering data from the 136‐, 271‐, and 381‐μm solutions were processed using the data from the 41‐μm solution as a volumetric reference to correct for transducer diffraction effects and acquisition system gain vibrations. The power cepstrum of the mean log difference spectra averaged over all overlapping data segments was computed and corrected for the effects of attenuation and Rayleigh scattering in the reference. Abcissas of the cepstral peaks were plotted versus scatterer diameter. The zero‐intercept, linear regression line for the longer‐lag peaks yielded a slope of 3.4 μs/mm, r2 = 0.997, corresponding to a Mie‐region periodicity in backscattered cross section of ka = 0.6. This value is in excellent agreement with calculations based on the scattering theory of Faran, which includes shear waves and yields a periodicity of ka = 0.66.