Optical 3-D metric measurements of local vocal fold deformation characteristics in an in vitro setup

Understanding vocal fold dynamics presents an essential part in treating voice disorders as it is the prerequisite to appropriate medical therapy. Various physical and numerical models exist for simulation purposes, all relying on simplified material parameters. To improve current approaches, data of realistic tissue behavior, i.e., in natural surroundings, have to be considered in model development. An in vitro setup was proposed for tensile tests combined with an optical method for precise, local and metrical 3-D measurements of distinctive surface points. Compared to previous 3-D reconstruction methods, the accuracy was improved tenfold. Vertically applied forces versus resulting deformation were measured for ten porcine vocal folds. Deformation characteristics of mucosa and the two-layer structure of mucosa and muscle (MM) were investigated at three distinctive locations along the vocal fold edge. The spring rates were represented by an exponential function. For equal deflections, an increasing spring rate from posterior to anterior for MM was measured. For solely mucosa, the spring rate decreased from the posterior to the middle and subsequently increased again. The MM-layer presented a stiffer deformation behavior than mucosa. For deformations higher than 1.5 mm, the spring rates for MM were more than twice as high as for mucosa. The investigations display the importance of considering both multilayers and local differences for the improvement of vocal fold models.     

Identifying healthy and pathologically affected voice signals [Lecture notes]

Diagnosing vocal tract pathologies is a task that demands considerable investigation due to the diversity of possible problems and the lack of standards among speech pathologists. Current invasive clinical tools, such as indirect laryngoscopy, videolaryngoscopy, and stroboscopic light, provide quantitative analyses. On the other hand, all diagnoses based on a professional's hearing require subjective identification of problems in the larynx and vocal folds, resulting in a qualitative assessment of the structures. In recent years, there has been a significant motivation to develop software techniques for the noninvasive diagnosis of abnormal functioning of the vocal apparatus by studying the patients' voice signals. Many researchers have proposed larynx models that provide insight into the dynamic behavior of human phonation. This lecture note presents some mathematical model of the larynx that could possibly predict and classify pathologies in the vocal apparatus.     

Automatic tracing of vocal-fold motion from high-speed digital images

Defining characteristics of the phonatory vocal fold vibration is essential for studies that aim to understand the mechanism of voice production and for clinical diagnosis of voice disorders. The application of high-speed digital imaging techniques to these studies makes it possible to capture sequences of images of the vibrating vocal folds at a frequency that can resolve the actual vocal fold vibrations of a patient. The objective of this study is to introduce a new approach for automatic tracing of vocal fold motion from image sequences acquired from high-speed digital imaging of the larynx. The approach involves three process steps. 1) Global thresholding--the threshold value is selected on the basis of the histogram of the image, which is assumed to follow Rayleigh distribution; 2) applying a morphology operator to remove the isolated object regions; 3) using region-growing to delineate the object, or the vocal fold opening region, and to obtain the area of the glottis; the segmented object obtained after global threshold and the morphological operation is used as a seed region for the final region-growing operation. The performance, effectiveness and validation of our approach is demonstrated using representative, high-speed imaging recordings of subjects having normal and pathological voices.     

Classification of unilateral vocal fold paralysis by endoscopic digital high-speed recordings and inversion of a biomechanical model

Hoarseness in unilateral vocal fold paralysis is mainly due to irregular vocal fold vibrations caused by asymmetries within the larynx physiology. By means of a digital high-speed camera vocal fold oscillations can be observed in real-time. It is possible to extract the irregular vocal fold oscillations from the high-speed recordings using appropriate image processing techniques. An inversion procedure is developed which adjusts the parameters of a biomechanical model of the vocal folds to reproduce the irregular vocal fold oscillations. Within the inversion procedure a first parameter approximation is achieved through a knowledge-based algorithm. The final parameter optimization is performed using a genetic algorithm. The performance of the inversion procedure is evaluated using 430 synthetically generated data sets. The evaluation results comprise an error estimation of the inversion procedure and show the reliability of the algorithm. The inversion procedure is applied to 15 healthy voice subjects and 15 subjects suffering from unilateral vocal fold paralysis. The optimized parameter sets allow a classification of pathologic and healthy vocal fold oscillations. The classification may serve as a basis for therapy selection and quantification of therapy outcome in case of unilateral vocal fold paralysis.     

Vibration parameter extraction from endoscopic image series of the vocal folds

An approach is given to extract parameters affecting phonation based upon digital high-speed recordings of vocal fold vibrations and a biomechanical model. The main parameters which affect oscillation are vibrating masses, vocal fold tension, and subglottal air pressure. By combining digital high-speed observations with the two-mass-model by Ishizaka and Flanagan (1972) as modified by Steinecke and Herzel (1995), an inversion procedure has been developed which allows the identification and quantization of laryngeal asymmetries. The problem is regarded as an optimization procedure with a nonconvex objective function. For this kind of problem, the choice of appropriate initial values is important. This optimization procedure is based on spectral features of vocal fold movements. The applicability of the inversion procedure is first demonstrated in simulated vocal fold curves. Then, inversion results are presented for a healthy voice and a hoarse voice as a case of functional dysphonia caused by laryngeal asymmetry.     

Extracting physiologically relevant parameters of vocal folds from high-speed video image series

In this paper, a new method is proposed to extract the physiologically relevant parameters of the vocal fold mathematic model including masses, spring constants and damper constants from high-speed video (HSV) image series. This method uses a genetic algorithm to optimize the model parameters until the model and the realistic vocal folds have similar dynamic behavior. Numerical experiments theoretically test the validity of the proposed parameter estimation method. Then the validated method is applied to extract the physiologically relevant parameters from the glottal area series measured by HSV in an excised larynx model. With the estimated parameters, the vocal fold model accurately describes the vibration of the observed vocal folds. Further studies show that the proposed parameter estimation method can successfully detect the increase of longitudinal tension due to the vocal fold elongation from the glottal area signal. These results imply the potential clinical application of this method in inspecting the tissue properties of vocal fold.     

Phonovibrography: mapping high-speed movies of vocal fold vibrations into 2-D diagrams for visualizing and analyzing the underlying laryngeal dynamics

Endoscopic high-speed laryngoscopy in combination with image analysis strategies is the most promising approach to investigate the interrelation between vocal fold vibrations and voice disorders. So far, due to the lack of an objective and standardized analysis procedure a unique characterization of vocal fold vibrations has not been achieved yet. We present a visualization and analysis strategy which transforms the segmented edges of vibrating vocal folds into a single 2-D image, denoted Phonovibrogram (PVG). Within a PVG the individual type of vocal fold vibration becomes uniquely characterized by specific geometric patterns. The PVG geometries give an intuitive access on the type and degree of the laryngeal asymmetry and can be quantified using an image segmentation approach. The PVG analysis was applied to 14 representative recordings derived from a high-speed database comprising normal and pathological voices. We demonstrate that PVGs are capable to differentiate and quantify different types of normal and pathological vocal fold vibrations. The objective and precise quantification of the PVG geometry may have the potential to realize a novel classification of vocal fold vibrations.     

Towards developing a voice pathologies detection system

Most voice features used in predicting the voice when a person has voted with instability in the vocal fold vibration cause problems in estimating such period; as a result of this challenge, scientists have focused on the development of powerful features independent of pitch estimation. The major goal of this paper is to study and investigate the Acoustic Voice Analysis methods (AVA) based on adaptive features. This investigation will lead to the development of a system of detection. The essential parts of this topic is related to database (described later), sampling the sounds (and satisfying) from the German database with many diseases, degenerative neurological disease (such as chronic inflammation of the larynx and vocal fold nodules). Under the supervision of the used algorithm to accomplish the above task, the Mel-Frequency Cepstral Coefficients (MFCCs with different Jitter and Shimmer), as by likely flux model mixture (GMM) are used in the AVA. MATLAB was used to simulate such a study for the extraction of features as well as making the training and testing process. The achieved results showed that with some kind of analysis, it is possible to find different sound patterns of diseases, e.g. excessive twang, where additional spectral components exist due to the increase in air flow in nasal cavities. Another focal point is some mathematical transformations both in the temporal domain or frequency. These changes can improve the capacity of some voice features voice; however, there is a need to multivariate analysis of parameters which measure the various problems in the process of phonation; after that, it is necessary to analyse the importance of finding and sorting those features that provide more information. Finally, automatic classification of pathological voices was made using any of the known techniques for this purpose. Our achieved results prove that a good classification rate needs efficient features to characterize each class, in this work, on one hand the accuracy of system increases with the number of parameters (best accuracy with 39 coefficients including Jitter & Shimmer) which means that the difference between normal and abnormal become noticeable with second derivate of MFCC and energy more than the others.     

A new type of artificial larynx using a PZT ceramics vibrator as asound source

Various speech production substitutes which aim to reconstruct speech functions have been developed and used practically, for vocalization-handicapped persons. However, current speech production substitutes have problems, and it is hoped that perfect speech production substitutes are developed. We pay attention to a PZT ceramics vibrator as a sound source of an artificial larynx. We try to produce the artificial larynx which uses a PZT ceramics vibrator, and we evaluate the performance. The power spectrum of the produced artificial larynx user's voice is similar to that of a nonhandicapped person's voice. The vocalized sound of the produced artificial larynx user shows good characteristics at formant frequency, which is important for vowel discrimination. Based on the result of listening estimation, this voice has good clarity. We hope that the proposed artificial larynx will be widely used in the future     

Theoretical modeling and experimental high-speed imaging of elongated vocal folds

In this paper, the role of vocal fold elongation in governing glottal movement dynamics was theoretically and experimentally investigated. A theoretical model was first proposed to incorporate vocal fold elongation into the two-mass model. This model predicted the direct and nondirect components of the glottal time series as a function of vocal fold elongation. Furthermore, high-speed digital imaging was applied in excised larynx experiments to visualize vocal fold vibrations with variable vocal fold elongation from -10% to 50% and subglottal pressures of 18- and 24-cm H(2)O. Comparison between theoretical model simulations and experimental observations showed good agreement. A relative maximum was seen in the nondirect component of glottal area, suggesting that an optimal elongation could maximize the vocal fold vibratory power. However, sufficiently large vocal fold elongations caused the nondirect component to approach zero and the direct component to approach a constant. These results showed that vocal fold elongation plays an important role in governing the dynamics of glottal area movement and validated the applicability of the proposed theoretical model and high-speed imaging to investigate laryngeal activity.     

Telephony-based voice pathology assessment using automated speech analysis

A system for remotely detecting vocal fold pathologies using telephone-quality speech is presented. The system uses a linear classifier, processing measurements of pitch perturbation, amplitude perturbation and harmonic-to-noise ratio derived from digitized speech recordings. Voice recordings from the Disordered Voice Database Model 4337 system were used to develop and validate the system. Results show that while a sustained phonation, recorded in a controlled environment, can be classified as normal or pathologic with accuracy of 89.1%, telephone-quality speech can be classified as normal or pathologic with an accuracy of 74.2%, using the same scheme. Amplitude perturbation features prove most robust for telephone-quality speech. The pathologic recordings were then subcategorized into four groups, comprising normal, neuromuscular pathologic, physical pathologic and mixed (neuromuscular with physical) pathologic. A separate classifier was developed for classifying the normal group from each pathologic subcategory. Results show that neuromuscular disorders could be detected remotely with an accuracy of 87%, physical abnormalities with an accuracy of 78% and mixed pathology voice with an accuracy of 61%. This study highlights the real possibility for remote detection and diagnosis of voice pathology.     

A multiresolution 100-GOPS 4-Gpixels/s programmable smart vision sensor for multisense imaging

This paper presents a multiresolution general-purpose high-speed machine vision sensor with on-chip image processing capabilities. The sensor comprises an innovative multiresolution sensing area, 1536 A/D converters, and a SIMD array of 1536 bit-serial processors with corresponding memory. The sensing area consists of an area part with 1536 /spl times/ 512 pixels, and a line-scan part with a set of rows with 3072 pixels each. The SIMD processor array can deliver more than 100 GOPS sustained and the on-chip pixel-analysing rate can be as high as 4Gpixels/s. The sensor is ideal for high-speed multisense imaging where, e.g., color, greyscale, internal material light scatter, and 3-D profiles are captured simultaneously. When running only 3-D laser triangulation, a data rate of more than 20 000 profiles/s can be achieved when delivering 1536 range values per profile with 8 bits of range resolution. Experimental results showing very good image characteristics and a good digital to analog noise isolation are presented.     

Automatic detection of voice impairments by means of short-term cepstral parameters and neural network based detectors

It is well known that vocal and voice diseases do not necessarily cause perceptible changes in the acoustic voice signal. Acoustic analysis is a useful tool to diagnose voice diseases being a complementary technique to other methods based on direct observation of the vocal folds by laryngoscopy. Through the present paper two neural-network based classification approaches applied to the automatic detection of voice disorders will be studied. Structures studied are multilayer perceptron and learning vector quantization fed using short-term vectors calculated accordingly to the well-known Mel Frequency Coefficient cepstral parameterization. The paper shows that these architectures allow the detection of voice disorders--including glottic cancer--under highly reliable conditions. Within this context, the Learning Vector quantization methodology demonstrated to be more reliable than the multilayer perceptron architecture yielding 96% frame accuracy under similar working conditions.     

Automated Analysis of Ultra High-Speed Laryngeal Films

This paper discusses the design and implementation of a digital image processing system which automatically processes ultra high-speed laryngeal films. The 100 ft films are exposed at a rate of nearly 5000 frames/s. The objective of this ultra high-speed cinematography is to obtain a permanent record of vocal cord motion during phonation, e. g., a sustained vowel. These films are then analyzed frame-by-frame for data relevant to the theory of vocal cord motion and to the detection of laryngeal pathologies. Because of the massive amount of data, several automated systems have been developed to extract such parameters as glottal area, length, width, etc. The system described here encompasses image digitization and display, artifact removal, contour tracing, image coding, and storage. Our system can process the films at a rate of 1 frame/min. We compare our results to previous systems developed in our laboratories and others.     

声带振动产生的声场的近场分析

针对声带振动在喉内所产生的声场难以分析的问题,该文根据声带的发声机制,提出了模拟声带振动的一种新模型,并对模型中对角线上的声压变化规律及微小振元的数目对声带振动所产生的近声场的影响进行了研究.运用Matlab对取得的变化规律进行仿真,结果表明,在新模型中,微元法可实现对近声场的理论研究;从而为进一步研究声带振动及其产生...     

Dimensionality reduction of a pathological voice quality assessment system based on Gaussian mixture models and short-term cepstral parameters

Voice diseases have been increasing dramatically in recent times due mainly to unhealthy social habits and voice abuse. These diseases must be diagnosed and treated at an early stage, especially in the case of larynx cancer. It is widely recognized that vocal and voice diseases do not necessarily cause changes in voice quality as perceived by a listener. Acoustic analysis could be a useful tool to diagnose this type of disease. Preliminary research has shown that the detection of voice alterations can be carried out by means of Gaussian mixture models and short-term mel cepstral parameters complemented by frame energy together with first and second derivatives. This paper, using the F-Ratio and Fisher's discriminant ratio, will demonstrate that the detection of voice impairments can be performed using both mel cesptral vectors and their first derivative, ignoring the second derivative.     

Topology-graph Directed Separating Boundary Surfaces Approximation of Nonmanifold Neuroanatomical Structures: Application to Mouse Brain Olfactory Bulb

Boundary surface approximation of 3-D neuroanatomical regions from sparse 2-D images (e.g., mouse brain olfactory bulb structures from a 2-D brain atlas) has proven to be difficult due to the presence of abutting, shared boundary surfaces that are not handled by traditional boundary-representation data structures and surfaces-from-contours algorithms. We describe a data structure and an algorithm to reconstruct separating surfaces among multiple regions from sparse cross-sectional contours. We define a topology graph for each region, that describes the topological skeleton of the region's boundary surface and that shows between which contours the surface patches should be generated. We provide a graph-directed triangulation algorithm to reconstruct surface patches between contours. We combine our graph-directed triangulation algorithm together with a piecewise parametric curve fitting technique to ensure that abutting or shared surface patches are precisely coincident. We show that our method overcomes limitations in 1) traditional contours-from-surfaces algorithms that assume binary, not multiple, regionalization of space, and in 2) few existing separating surfaces algorithms that assume conversion of input into a regular volumetric grid, which is not possible with sparse interplanar resolution.      

Reconstruction of coronary arteries from a single rotational X-ray projection sequence

Cardiovascular diseases remain the primary cause of death in developed countries. In most cases, exploration of possibly underlying coronary artery pathologies is performed using X-ray coronary angiography. Current clinical routine in coronary angiography is directly conducted in two-dimensional projection images from several static viewing angles. However, for diagnosis and treatment purposes, coronary artery reconstruction is highly suitable. The purpose of this study is to provide physicians with a three-dimensional (3-D) model of coronary arteries, e.g., for absolute 3-D measures for lesion assessment, instead of direct projective measures deduced from the images, which are highly dependent on the viewing angle. In this paper, we propose a novel method to reconstruct coronary arteries from one single rotational X-ray projection sequence. As a side result, we also obtain an estimation of the coronary artery motion. Our method consists of three main consecutive steps: 1) 3-D reconstruction of coronary artery centerlines, including respiratory motion compensation; 2) coronary artery four-dimensional motion computation; 3) 3-D tomographic reconstruction of coronary arteries, involving compensation for respiratory and cardiac motions. We present some experiments on clinical datasets, and the feasibility of a true 3-D Quantitative Coronary Analysis is demonstrated.     

瞬态三维温度场的莫尔偏折层析

本文讨论了莫尔偏折法层析三维温度场的原理;提出了一种多通道(多方向)莫尔偏折的方法来获得多方向光线经过温度场后偏折的数据,然后利用计算机层析再现三维温度扬的方法;并分析了最佳通道采样角间隔的选取问题,得到了角间隔与角频率之间的关系。采用YAG脉冲激光光源,测量了非对称火焰的三维空间温度分布,并与干涉法测量的结果比较,得到一致的结果。     

Novel measurement scheme for injection-locking experiments

A novel experimental setup for injection-locking experiments is presented. The single-mode-fiber-based configuration allows one to precisely control the power and the polarization state of the light injected from the master laser into the slave laser cavity. Different behaviors typical for injection locking with single-mode semiconductor lasers (e.g., stable injection locking, undamped relaxation oscillations, nearly degenerate four-wave mixing, period doubling, chaotic behavior) are experimentally observed and theoretically verified using a rate-equation-based model. Measurements and calculations are entirely linked analytically and thoroughly compared by means of the corresponding power spectra. The good quantitative agreement between measurements and model validates the model, the analytical approach, and the experimental setup