Cepstral Analysis Research Articles

Towards robust crop disease detection for complex real field background images

Most of the work done in image processing-based crop disease detection focuses on images with plain background. This paper presents a technique for crop disease detection for complex real field background images. A segmentation technique is presented to extract leaf patches from the entire image. Transform domain cepstral analysis is proposed for obtaining cepstral coefficients, to attain two level classifications. The first level classifies the crop species while the second level classifies the species into healthy leaf or leaf with specific type of disease. The work is tested on three crops Banana, Soybean and Grape and is checked on plain background laboratory images and on complex real field images. Suggested technique give species level accuracy of 94.33 %, 94.11 % and 98.44 % and disease level average accuracy of 97.75 %, 96.66 % and 97.95 % for Banana, Soybean and Grape, respectively. Comparison with standard features like texture and shape indicate that the presented technique gives the best results for both plain and complex background images suggesting its utilization in crop disease detection to reduce the agricultural and economic losses.

The Relationship between Traditional Acoustic Measures and Cepstral Analysis of Voice.

In this study, the correlations between traditional acoustic measures (TAMs) and cepstral analysis (CA) were explored in Persian. This investigation was a cross-sectional study including 179 dysphonic (n=141) and normophonic (n=38) speakers. The TAMs (jitter, shimmer, and noise-to-harmonic ratio) and CA (cepstral peak prominence, and cepstral peak prominence-smoothed) values were obtained during vowel prolongation, reading a standard sentence, and a non-standard running speech sample using Praat software. The difference of acoustic measures between normophonic and dysphonic speakers and intercorrelation among acoustic measures and correlation between the acoustic measures and perceived dysphonia levels was analyzed with independent t test, Mann-Whitney U test, Pearson, Spearman, and Kendall's Tau-b correlation tests using IBM SPSS Statistics. The findings showed that dysphonic speakers had higher TAM values and lower CA values than normophonic speakers. (P < 0.05). In dysphonic speakers, a large correlation was discovered among all acoustic measurements (r = 0.52 to 0.96; P < 0.05), while in various perceived dysphonic speakers, there was a correlation of varying strength (r = 0.25 to 0.97; P < 0.05). Ultimately, there was a significant small-to-large correlation between the acoustic measures and perceived dysphonia levels (r = 0.34-0.58; P < 0.05). This research demonstrated that Persian speakers with dysphonia experienced a rise in TAM and a corresponding reduction in CA. In the future, multi-parametric indices can be developed using both TAM and CA to include various aspects of vocal production and yield a single, comprehensive value.

Diagnosis of Schizophrenia Using EEG Sensor Data: A Novel Approach with Automated Log Energy-Based Empirical Wavelet Reconstruction and Cepstral Features.

Schizophrenia (SZ) is a severe mental disorder characterised by disruptions in cognition, behaviour, and perception, significantly impacting an individual's life. Traditional SZ diagnosis methods are labour-intensive and prone to errors. This study presents an innovative automated approach for detecting SZ acquired through electroencephalogram (EEG) sensor signals, aiming to improve diagnostic efficiency and accuracy. We utilised Fast Independent Component Analysis to remove artefacts from raw EEG sensor data. A novel Automated Log Energy-based Empirical Wavelet Reconstruction (ALEEWR) technique was introduced to reconstruct decomposed modes based on their variability, ensuring effective extraction of meaningful EEG signatures. Cepstral-based features-cepstral activity, cepstral mobility, and cepstral complexity-were used to capture the power, rate of change, and irregularity of the cepstrum of preprocessed EEG signals. ANOVA-based feature selection was applied to refine these features before classification using the K-Nearest Neighbour (KNN) algorithm. Our approach achieved an exceptional accuracy of 99.4%, significantly surpassing previous methods. The proposed ALEEWR and cepstral analysis demonstrated high precision, sensitivity, and specificity in the automated diagnosis of schizophrenia. This study introduces a highly accurate and efficient method for SZ detection using EEG technology. The proposed techniques offer significant improvements in diagnostic accuracy, with potential implications for enhancing SZ diagnosis and patient care through automated systems.

Unsupervised machine learning and cepstral analysis with 4D-STEM for characterizing complex microstructures of metallic alloys

Four-dimensional scanning transmission electron microscopy, coupled with a wide array of data analytics, has unveiled new insights into complex materials. Here, we introduce a straightforward unsupervised machine learning approach that entails dimensionality reduction and clustering with minimal hyperparameter tuning to semi-automatically identify unique coexisting structures in metallic alloys. Applying cepstral transformation to the original diffraction dataset improves this process by effectively isolating phase information from potential signal ambiguity caused by sample tilt and thickness variations, commonly observed in electron diffraction patterns. In a case study of a NiTiHfAl shape memory alloy, conventional scanning transmission electron microscopy imaging struggles to accurately identify a low-contrast precipitate at lower magnifications, posing challenges for microscale analyses. We find that our method efficiently separates multiple coherent structures while using objective means of determining hyperparameters. Furthermore, we demonstrate how the clustering result facilitates more robust strain mapping to provide immediate and quantitative structural insights.

Cepstral Analysis of Voice in Patients With Temporomandibular Disorders.

This study aimed to assess the voice quality of patients with temporomandibular disorders (TMDs) compared with healthy subjects using cepstral analysis and investigate the relationship between the TMD severity and the values of cepstral analysis. Subjects who met the inclusion criteria completed a general health questionnaire and the Fonseca Anamnestic Index. Patients who had TMDs with FAI were subjected to an examination based on the Diagnostic Criteria for Temporomandibular Disorders. The final sample included 65 subjects, 31 TMDs patients (with a mean age ± standard deviation of 36.64 ± 13.67 years), and 34 healthy individuals in the control group (with a mean age ± standard deviation of 30.35 ± 7.78 years). Cepstral Peak Prominence (CPP) and Smoothened Cepstral Peak Prominence (CPPS) of a sustained vowel and connected speech were computed using Praat software. TMD patients indicated lower cepstral values and lower voice quality compared to the control group. Significant differences were found between TMD and control groups for all cepstral parameters (P < .001) and cepstral measurements showed a moderate to strong negative correlation with TMD severity (P < .001, rho = -0.57 to -0.88). The outcomes of the present study indicate that cepstral analysis can accurately distinguish the reduced voice quality of TMD patients from normal voice.

Applying Precession Electron Diffraction to Cepstral Analysis to Investigate Polarity in Ferroelastically Tilted Specimens

Applying Precession Electron Diffraction to Cepstral Analysis to Investigate Polarity in Ferroelastically Tilted Specimens

CPPS and Voice-Source Parameters: Objective Analysis of the Singing Voice.

In recent years cepstral analysis and specific cepstrum-based measures such as smoothed cepstral peak prominence (CPPS) has become increasingly researched and utilized in attempts to determine the extent of overall dysphonia in voice signals. Yet, few studies have extensively examined how specific voice-source parameters affect CPPS values. Using a range of synthesized tones, this exploratory study sought to systematically analyze the effect of fundamental frequency (fo), vibrato extent, source-spectrum tilt, and the amplitude of the voice-source fundamental on CPPS values. A series of scales were synthesised using the freeware Madde. Fundamental frequency, vibrato extent, source-spectrum tilt, and the amplitude of the voice-source fundamental were systematically and independently varied. The tones were analysed in PRAAT, and statistical analyses were conducted in SPSS. CPPS was significantly affected by both fo and source-spectrum tilt, independently. A nonlinear association was seen between vibrato extent and CPPS, where CPPS values increased from 0 to 0.6 semitones (ST), then rapidly decreased approaching 1.0 ST. No relationship was seen between the amplitude of the voice-source fundamental and CPPS. The large effect of fo should be taken into account when analyzing the voice, particularly in singing-voice research, when comparing pre and posttreatment data, and when comparing inter-subject CPPS data.

Cepstral Analysis of Voice in School-Aged Children

Dysphonia in school-aged children is attributed primarily to hyperfunctional use of voice. These can be identified through effective protocols using both acoustic and auditory-perceptual analyses. The current study aimed to investigate voice characteristics in school children aged 4-17years using auditory-perceptual rating and cepstral measures of voice. This is a descriptive cross-sectional observational study. Four hundred and fifty-seven recordings of sustained phonation of /a/ in children and adolescents obtained in a quiet room using Zoom h1 voice recorder were analyzed using auditory-perceptual evaluation by three speech-language pathologists using Grade of overall dysphonia, Roughness, Breathiness, Asthenia, and Strain(GRBAS) rating scale. The samples were classified based on age into five groups: 1) 4 to 6; 11 years 2) 7-8; 11 years, 3) 9-11; 11years 4) 12-13; 11years and 5) 14-16; 11 years. PRAAT software was used to extract Cepstral Peak Prominence (CPP) and Cepstral Peak Prominence Smoothed (CPPS). Inter-rater reliability was assessed for both auditory-perceptual and acoustic analysis. Auditory-perceptual analysis revealed dysphonia in 7.8% of samples with higher rate in males than females. Inter-rater reliability for auditory-perceptual rating was found to be good (Intraclass Corelation Coefficient-0.83). Independent t test revealed statistically significant difference (P<0.001) in both cepstral measures and mean values were lower in dysphonic than normal group. Gender effect was present for CPP in group 5(14-16;11 years) and CPPS in group 4 (12-13; 11years). One-way analysis of variance within groups in males (P<0.005) revealed statistical difference in both cepstral measures but not in females. Statistically significant difference was not found between ratings of both speech language pathologists for both CPP (P=0.929) and CPPS (P=0.965) values indicating the ratings to be reliable. Pediatric dysphonia has received less attention when compared to adults. Assessing school-aged children for dysphonia using both auditory-perceptual and acoustic measures would aid in identifying those at risk to make appropriate referrals and plan further intervention.

Comparative study of respiratory sounds classification methods based on cepstral analysis and artificial neural networks

In this paper, we investigated and evaluated various machine learning-based approaches for automatically detecting wheezing sounds. We conducted a comprehensive comparison of these proposed systems, assessing their classification performance through metrics such as Sensitivity, Specificity, and Accuracy. The main approach to developing a machine learning-based system for classifying respiratory sounds involved the combination of a technique for extracting features from an unknown input sound with a classification method to determine its belonging class. The characterization techniques used in this study are based on the cepstral analysis, which was extensively employed in the automatic speech recognition field. While MFCC (Mel-Frequency Cepstral Coefficients) feature extraction methods are commonly used in respiratory sounds classification, our study introduces a novelty by employing GFCC (Gammatone-Frequency Cepstral Coefficients) and BFCC (Bark-Frequency Cepstral Coefficients) for this purpose. For the classification task, we employed two types of neural networks: the MLP (Multilayer Perceptron), a feedforward neural network, and a variant of the LSTM (Long Short-Term Memory) recurrent neural network called BiLSTM (Bidirectional LSTM). The proposed classification systems are evaluated using a database consisting of 497 wheezing segments and 915 normal respiratory segments, which are recorded from individuals diagnosticated with asthma and individuals without any respiratory issues, respectively. The highest classification performance was achieved by the BFCC-BiLSTM model, which demonstrated an exceptional accuracy rate of 99.8%.

Early Assessment of Voice Problems in Post-Thyroidectomy Syndrome Using Cepstral Analysis.

Post-thyroidectomy syndrome (PTS), characterized by voice issues after thyroidectomy without recurrent laryngeal nerve injury, was investigated in this study. The Voice Fatigue Index (VFI) and cepstral analysis were employed for subjective and objective voice evaluation. Retrospective analysis involved 96 patients (37 males, 59 females) who underwent thyroidectomy without nerve injury from April 2018 to June 2022. Assessments pre- and post-thyroidectomy included the Voice Handicap Index (VHI) and VFI, along with auditory perceptual, acoustic (including cepstral), aerodynamic, and glottal vibration analyses. In females, although the GRBAS scale showed no significant change, both VHI and VFI increased post-thyroidectomy. Significant correlations were observed between the VHI and VFI in females. Acoustic analysis indicated a decrease in the cepstral peak prominence (CPP) of vowels (/a/) and sentences in females, with significant correlations between changes in the CPP/a/ and VHI/VFI. The maximum fundamental frequency (F0max) exhibited a significant decrease, correlating with the VHI and VFI changes. The VFI demonstrated effectiveness in subjective PTS voice evaluation, comparable to the VHI. The present study highlights the potential of cepstral analysis as an index reflecting subjective voice discomfort, suggesting its promise for a comprehensive PTS voice evaluation.

A Crowdsensing-Based Framework for Indirect Bridge Monitoring Using Mel-Frequency Cepstral Analysis Considering Elimination of Operational Effects

A Crowdsensing-Based Framework for Indirect Bridge Monitoring Using Mel-Frequency Cepstral Analysis Considering Elimination of Operational Effects

A spectrum-of-spectrum filtering method to extract direct and multipath arrivals from simulations and measurements

In measurements and numerical modelling of wave propagation, undesired interference between direct and multipath arrivals can be reduced using Fourier-based signal processing methods. Existing methods, such as cepstral analysis and time-signal gating, are not applicable to all cases. Here, an alternative Fourier-based signal processing method is presented, called spectrum-of-spectrum (SoS) filtering. Its main advantage over existing methods is its ability to extract single direct or multipath arrivals for relatively short propagation distances even when subsequent arrivals do not become successively weaker. The method is based on the following steps:•Apply a lowpass filter to the real and imaginary parts of an input frequency spectrum individually, using a digital finite impulse response (FIR) filter in the frequency domain.•Recombine the filtered real and imaginary parts of the frequency spectrum to get the frequency spectrum of the direct arrival.•For extraction of the first multipath arrival, subtract the filtered frequency spectrum from the input frequency spectrum and repeat the previous steps. Repeat multiple times to extract subsequent multipath arrivals.

Data processing method for magnetotelluric sounding based on cepstral analysis

Magnetotelluric (MT) signals exhibit the characteristics of being weak and having a wide frequency band. The acquired field data are susceptible to various types of noise, which poses challenges in accurate identification and processing. Currently, there exist numerous MT data processing methods; however, they lack efficiency and physical meaning. To address this issue and improve the signal-to-noise ratio of the acquired data, this study proposes a MT data processing method based on cepstral analysis. By employing cepstral analysis on the MT data, the cepstrum is obtained, and an appropriate truncation position is selected for processing. The experimental results demonstrate that this method obtains smoother and more continuous apparent resistivity curves with fewer errors. Compared with other methods, the cepstral analysis method can effectively suppress different types of MT noise, and the method is simple and efficient with clear physical significance.

Early voice problems assessment in post-thyroidectomy syndrome using vocal fatigue index and cepstral analysis

Early voice problems assessment in post-thyroidectomy syndrome using vocal fatigue index and cepstral analysis

Experimental Investigations to Study the Effectiveness of Cepstral Features to Detect Surface Fatigue Wear Development in a FZG Spur Geared System Subjected to Accelerated Tests

Gears are essential machine elements used to transmit power and motion from one unit to another under desired angular velocity ratio. Various types of gears have been developed to fulfill power transmission requirements in industrial applications. Under normal or fluctuating operating conditions, increase in fatigue load cycles, transition in lubrication regimes, fluctuating loads and speeds, etc., result in various surface fatigue wear modes which affect the performance of geared system. The severity of wear anomalies developed on gear tooth surfaces can be assessed by using vibration signals acquired from the gear box. On the other hand, reliable wear assessment is very important to perform maintenance action which depends on the sensors, data acquisition procedure, vibration signal analysis and interpretation. This paper presents results of the experimental investigations carried out to assess initiation and propagation of surface fatigue failure wear modes developed on gear tooth contact surfaces. A FZG back to back power recirculation type spur gearbox was used to conduct fatigue test experiments on spur gears under accelerated test conditions. Accelerated test conditions resulted in a rapid transition of lubrication regimes, i.e., hydrodynamic lubrication regime to boundary lubrication regime which triggered surface fatigue faults on gear tooth surfaces. A cepstral analysis method was used to assess fault severity in the geared system. The results obtained from the cepstral features were correlated to various surface fatigue faults and reduction in gear tooth stiffness. Results obtained from the experimental investigations highlighted the suitability of cepstral features to assess incipient faults developed on spur gear tooth surfaces.

The Study of Vocal Function in Patients With Early Laryngeal Carcinoma After Transoral Laser Microsurgery

Objective Today transoral laser microsurgery is considered as one of the first options to control early laryngeal cancer, and voice disorder is one of the inevitable complications of this therapeutic component. This study aimed to compare the vocal function in patients with early-stage laryngeal cancer following laser surgery with healthy individuals with normal voice quality using acoustic analysis and the voice handicap index and to examine the correlation between the results of these two evaluations in each group. Materials &amp; Methods The current research is a descriptive-correlational and comparative study that was cross-sectioned using convenience sampling into two groups consisting of 60 men (Mean±SD of age 59.18±5.170 years) with early laryngeal cancer undergoing laser surgery as a patient group and 60 men (Mean±SD of age 56.68±5.491 years) with normal voice quality, and without any history of voice disorder as a control. The vocal function of participants was assessed using acoustic parameters (including jitter, shimmer, harmonic to noise ratio, fundamental frequency, smoothed cepstral peak prominence) and a 30-item Persian version of the voice handicap index questionnaire (including overall score and scores of physical, emotional and functional subscale). Then, data were analyzed using SPSS software, version 20, descriptive statistics, Kolmogorov-Smirnov, independent t-test, and Pearson correlation coefficient at the P&lt;0.05. Results The results show that the acoustic parameters and voice handicap index in the group undergoing laser surgery are significantly different from the control group (P&lt;0.001). Also, a significant direct correlation between jitter and shimmer with total score and physical subscale of VHI, a significant inverse correlation between the harmonic-to-noise ratio and total score, functional and physical subscales of VHI, as well as between smoothed cepstral peak prominence with total score and all subscales of VHI (P&lt;0.05). In addition, in the control group, there is no significant correlation between the acoustic parameters and the subscales of the voice handicap index (P&lt;0.05). Conclusion The findings of the acoustic analysis and the voice handicap index show impairment in vocal function and a decrease in voice-related quality of life (QoL) in patients with early laryngeal cancer after laser surgery. Furthermore, a significant correlation between the scores of the voice handicap index and acoustic analysis parameters, especially cepstral analysis indicates the necessity to pay attention to frequency-based analysis. Therefore the results of the present study emphasize the need for a comprehensive assessment of vocal function, providing voice therapy programs, and attention to psychological problems in patients with early laryngeal cancer after laser surgery.

Quality Control Metrics for Cepstral Analysis with Homomorphic Deconvolution

ABSTRACT In this study we describe an improved cepstral method that can be used for estimating the depth (&amp;lt;2 km) of very shallow earthquakes and explosions. Knowing the depth is important, especially for explosions, for which the best depth indicator, the seismic depth phase (pP) is often not easily discernible from the first arrival (P). Unlike the previous cepstral studies, our method utilizes the power and complex complex cepstrum, and homomorphic deconvolution (HD). The use of quality control metrics increases analyst confidence that a plausible signal–echo delay pair was identified. The metrics quantify liftering adequacy and success of the HD. The most important criteria for successful HD shows good signal–echo delay lag recovery, and a plausible deconvolved signal and echo with similar shape to the initial waveform. The successful application of the method to a few near-ideal cases of high signal-to-noise ratio events warrants testing the proposed metrics on a larger number of well-located events.

Spatial location from running binaural signals using cepstral analysis and logarithmic filtering

This paper proposes an acoustic model for predicting the acoustical room characteristics from a running binaural signal. This is accomplished via training a convolutional neural network on a precedence effect model to extract the spatial locations of the direct sound source and its early reflections. The precedence effect model extends and modifies the BICAM algorithm with cepstral analysis [Tyler, J., Si, M., \&amp; Braasch, J. Acoust. Soc. Am. 151] and a logarithmic filter. The logarithmic filter takes human perception into account and provides better separation at higher frequencies. A synthetic dataset of binaural signals was generated using anechoic orchestral recordings with added reflections and reverberations. The binaural model generates binaural activity maps from binaural input signals, which are then used to train a convolutional neural network. The ability to predict the traits of a direct sound source and its reflections has applications in academic areas like perceptual modeling and room acoustical analysis. It can also be applied to industrial areas such as television and movies, video games, and augmented and virtual reality, to name a few. [Work supported by the National Science Foundation: HCC-1909229.]

Relationship Between Aerodynamic Measurement of Maximum Phonation Time With Acoustic Analysis and the Effects of Sex and Dysphonia Type.

This study set out to uncover the correlation between maximum phonation time (MPT) with acoustic and cepstral analysis in the dysphonic and control groups, considering the effects of sex and dysphonia type. For this cross-sectional study, a sample of 179 attendees (141 dysphonic and 38 control) were randomly selected and requested to sustain the vowel /a/ as long as they could with their habitual pitch and loudness. Reading standard sentences and conversational connected speech tasks were obtained too. Using Praat, the MPT, jitter, shimmer, noise-to-harmonic ratio, cepstral peak prominence (CPP), and smoothed cepstral peak prominence (CPPS) were calculated in the target vocal tasks. There was a very low to low significant correlation (r=0.00-0.50) between MPT amounts and acoustic analysis in the dysphonic group (P < 0.05), except for between MPT with shimmer (P > 0.05). In contrast, findings showed no significant correlation between MPT and acoustic analysis in the control group, not even separated by sex (P > 0.05). There was a very low to low correlation between MPT amounts and acoustic analysis in the male dysphonic group (P < 0.05), except for the MPT with shimmer (P > 0.05). There was no significant correlation between MPT and acoustic analysis in the female dysphonic group (P > 0.05), except for MPT with CPP (sustained vowel) (P < 0.05). Finally, very low to high correlations between MPT and some of the acoustic analysis in all the different dysphonia types were observed (P < 0.05). MPT contains some information about the acoustic features of the dysphonic voice, specifically the CPP and smoothed cepstral peak prominence. The data suggested that the observed relationship between MPT and the acoustic analysis has the capacity to be considered for the development of new multiparametric tests of voice assessment in dysphonia, regarding the sex and dysphonia type.

Is Cepstral Peak Prominence a Measure of Vocal Fatigue in Temple Priests: A Pilot Study

Considering cepstral analysis of voice as a measure of overall severity of dysphonia, we tried to investigate if these measures could be considered as a metric of vocal fatigue as well. Since voice quality changes are seen as a result of vocal fatigue, we wanted to find out if there were any correlations between the cepstral measures, vocal fatigue symptoms, and auditory perceptual evaluation of voice in professional voice users. The pilot study was conducted on 10 temple priests belonging to the Krishna Consciousness Movement. We conducted a pre-post voice evaluation, which included recording voices before the beginning of any temple preaching in the morning and after all the preaching sessions in the evening. The priests also filled in the Vocal Fatigue Index (VFI) questionnaire twice (morning and evening), and all the voice samples were analyzed for GRBAS (Grade, Roughness, Breathiness, Asthenia, and Strain voice quality) rating by speech language pathologists with voice expertise. Correlations were obtained between the acoustic measures, VFI responses, and auditory perceptual evaluations. The findings of our pilot study didn't show any correlations between the cepstral measures and the questionnaire responses or with the perceptual ratings. However, the cepstral measures were slightly higher for evening recordings than the morning recordings. Our participants did not experience or perceive any voice symptoms or vocal fatigue. Despite more than 10 hours of voice use per day for over 10 years, our participants did not experience any voice symptoms or vocal fatigue. This finding indicates that there may be diverse reasonings and opinions about the occurrence of voice problems in various professional voice users. This is particularly because the participants' responses to vocal fatigue symptoms had more of a psychological explanation (faith, self-power, etc.) rather than any physiological changes in the vocal apparatus.