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Human recognition systems based on biometrics are much in demand due to increasing concerns of security and privacy. The human ear is unique and useful for recognition. It offers numerous advantages over popular biometrics traits face, iris, and fingerprints. A lot of work has been attributed to ear biometric, and the existing methods have achieved remarkable success over constrained databases. However, in unconstrained environment, a significant level of difficulty is observed as the images experience various challenges. In this paper, we first have provided a comprehensive survey on ear biometric using a novel taxonomy. The survey includes in-depth details of databases, performance evaluation parameters, and existing approaches. We have introduced a new database, NITJEW, for evaluation of unconstrained ear detection and recognition. A modified deep learning models Faster-RCNN and VGG-19 are used for ear detection and ear recognition tasks, respectively. The benchmark comparative assessment of our database is performed with six existing popular databases. Lastly, we have provided insight into open-ended research problems worth examining in the near future. We hope that our work will be a stepping stone for new researchers in ear biometrics and helpful for further development.

Biometric is one of the growing fields used in security, forensic and surveillance applications. Various types of physiological and behavioral biometrics are available today. Human ear is a passive physiological biometric. Ear is an important biometric trait due to many advantages over other biometric modalities. Because of its complex structure, face image detection is very challenging. Detection deals with finding or localizing the position of ear in the given profile face image. Various methods like manual, semiautomatic and automatic techniques are used for ear detection. Automatic ear localization is a complex process compared to manual ear cropping. This paper presents an empirical study and evaluation of four different existing ear detection techniques with our proposed method based on banana wavelets and circular Hough transform. A comparative analysis of the five algorithms in terms of detection accuracy is presented. The detection accuracy was calculated by means of manual as well as automatic verification.

The human ear is highly sensitive and accessible, making it especially suitable for being used as an interface for interacting with smart earpieces or augmented glasses. However, previous works on ear-based input mainly address gesture sensing technology and researcher-designed gestures. This paper aims to bring more understandings of gesture design. Thus, for a user elicitation study, we recruited 28 participants, each of whom designed gestures for 31 smart device-related tasks. This resulted in a total of 868 gestures generated. Upon the basis of these gestures, we compiled a taxonomy and concluded the considerations underlying the participants' designs that also offer insights into their design rationales and preferences. Thereafter, based on these study results, we propose a set of user-defined gestures and share interesting findings. We hope this work can shed some light on not only sensing technologies of ear-based input, but also the interface design of future wearable interfaces.

In recent years, more and more people are applying Convolutional Neural Networks to the study of sound signals. The main reason is the translational invariance of convolution in time and space. Thereby the diversity of the sound signal can be overcome. However, in terms of sound direction recognition, there are also problems such as a microphone matrix being too large, and feature selection. This paper proposes a sound direction recognition using a simulated human head with microphones at both ears. Theoretically, the two microphones cannot distinguish the front and rear directions. However, we use the original data of the two channels as the input of the convolutional neural network, and the resolution effect can reach more than 0.9. For comparison, we also chose the delay feature (GCC) for sound direction recognition. Finally, we also conducted experiments that used probability distributions to identify more directions.

This article is about the structure of the human ear, the method of hearing, the different types of hearing loss, and treatment of hearing loss with different types of hearing aids and implants. It also discusses the selection process of hearing aids, including preliminary examinations, the types, elements, functions and the operating principle of hearing aids. Furthermore it discusses the implantation process and programming of cochlear implants.

The inspiration for this model ware possibilities of the human ear to distinguish the frequency of sounds and a diffraction grating. Detection takes place after max. 15 length of the wave (arbitrary choice). The range of frequencies to detect for tests – 800-3200 Hz: detection every 5 Hz in the range 800-1600 Hz and 10 Hz in the range 1600-3200 Hz (arbitrary choice). It can explain the residual hearing effect (missing tone f is heard when harmonic tones 2f, 3f and 4f are played). The algorithm can be used as an alternative for FFT. Model uses only memory for delay line end for results, and adding operation, so it should be fast and cheep, and can work on-line in real-time. Testing program was written in Perl.

Power transformers are the key equipment and the main sources of noise in smart substations. Noise is an important indicator that reflects the health and safety of transformers. Monitoring it is of great significance to the safe and stable operation of transformers. At the same time, clear noise data can help reduce public complaints about substation noise. This paper mainly analyses the causes of noise in smart substations from the core magnetostrictive phenomenon of transformers and obtains the basis for the design of noise monitoring devices in smart substations through the analysis of the causes. By analysing the principle of the noise monitoring terminal instrument, the A-weighting network for data correction is applied to obtain data that is relatively consistent with the human ear. To get higher accuracy, a method to overcome the overflow of data is proposed. Finally, sensors and analogue-to-digital converters are selected to design a noise monitoring terminal instrument. Thus, a noise monitoring system that is much more convenient than the existing monitoring system is established. The system was applied to a Smart Substation of 110kV level, and the sensitivity and accuracy have been verified through actual results.

Phonocardiogram is a concept that is used for recording heart sound signals and murmurs. This acoustic recording helps to reveal important information that human ear cannot recognize easily. A phonocardiogram signal, in the healthy case, consists of two fundamental sounds \(s_1\) and \(s_2\) which are derived from the mechanical functioning of the heart. Actually any change, even small, in the heart sounds might indicate heart valve problems, and hence the need of correctly analyzing and characterizing phonocardiogram signals. Recently, the analysis of phonocardiogram signals becomes an interesting field of research. There are several tools that have been studied and presented in the literature review. The majority of these studies are based on time-frequency and partially exploiting the periodic character of phonocardiogram signal due to the heart functioning. The objective of this research is to propose a coherent mathematical model and an analytical framework based on cyclostationarity. This allows the use of cyclostationary tools for the characterization and the analysis of phonocardiogram signals which are analyzed and discussed in details over synthetic and experimental datasets. The simulation shows promising results that can help with the early detection of some heart diseases.

In this paper a modified Mel scaled filter bank-based approach to discriminate people suffering from Parkinson disease (PD) in their early stages from healthy people using speech samples is proposed. Parkinson’s disease not only affects the muscular activities of the human body but also affects the speech of the diseased. So, the speech features of Parkinson affected people tend to vary and hence differ from the speech features of healthy people. In this paper, the speech feature used for discriminating the two groups is the Mel frequency cepstral coefficients (MFCC) extracted from speech samples of both the PD and healthy people. The traditional way of computing the MFCC coefficients involves the design of the Mel filter bank. These filters are usually designed according to the auditory or acoustic system of human ear which follows the Mel scale. In this study, modification to this Mel scaled bank of filters is done by varying its bandwidth in the region of interest to compute the feature, MFCC and its performance is then compared with the conventionally designed MFCC filter bank for the said application. The performance is compared in terms of classification accuracy using radial basis network classifier. The results show an improvement of 6.3% in the classification accuracy obtained using the proposed method.

The Biospeckle Laser (BSL) induced phenomenon is recorded as light scattered from the surface of illuminated test material, and the methodology is established as a tool for the non-destructive assessment of biological tissues. The output of BSL-based recording is normally numeric data that are processed to generate graphical and visual information. However, BSL outputs could presented in a qualitative audible format as a rapid means by which a test-tissues physiological states may be classified. Here we report a new BSL-data processing methodology that was able to discriminate viable and dead beans (seeds) of coffee (Coffea arabica L.) using single- and multi-frequency wavelength models. BSL-data was rapidly converted from the traditional numeric (graphical) output into forms (‘biospeckle index’) audible to the human ear. The utility and significance of this novel BSL-audio approach is discussed.

Now-a-days, securing ones information has become a greatest threat. The advancement in the technology has paved way for many biometrics systems. These biometrics using finger prints have been highly hacked these days. This paper suggests a technique using the human ear as the perfect source of person identification as it has its own unique features. A few image processing algorithms were applied to segment the ear but some lacked in perfect. The proposed system emphasizes on developing a biometrics system that would detect and recognize a person using the features of their ear where the ear is segmented using appropriate segmentation techniques. The ear recognition is then carried out by using feature matching technique.

This paper is based on Human ear recognition in a new biometric technology by attracting the interest of biometrics community. Biometrics involves the programmed investigation of strategies in order to understand individual’s dependent on physical or social characteristics. Finding a decent biometric strategy, it has been extensively investigated in an ongoing year. The ear is a steady organ, it does not change with age and feelings, among a few biometric highlights.

Auricular acupressure therapy is a simple and effective means of health care and has gradually gained popularity. As an important prerequisite for auricular acupressure therapy, auricular point localization is difficult for general public. Most auricular points are distributed within eight regions, including helix, scapha, fossa triangularis, antihelix, concha, antitragus, tragus, and earlobes, which are divided according to the auricle’s anatomical structure. In this paper, an improved YOLACT method is applied to segment the eight regions of the auricle automatically, and the accuracy is up to 93.2% on the ear dataset of 1000 images. Achieving segmentation of the auricular region, which can greatly narrow the localization area of auricular point, will provide an important foundation for the automatic localization of auricular point, favoring nonexperts to increase their understanding of auricular acupressure therapy or the development of an intelligent instrument related to auricular acupressure therapy.

The inception of the industrial application of the ultrasound was conditional of several discoveries and inventions. The human ear cannot hear the ultrasound (it is practically its definition), therefore for the initial stimulus can be considered the discovery, there can exist the sound beyond audible frequency. After that there was a challenge to invent the device, which can produce the ultrasound. When such a device was manufactured, and when ultrasound could be modulated in different frequencies and amplitudes, the researchers could start thinking about its industrial application. The first global usage of the ultrasound in the industry was a military application in the detection of the submarines and aircraft. After World War II, there begin application of the ultrasound for peaceful purpose in the industry and medicine.

Squeal noise is normally attributed to the self-excited vibration of the wheel induced by the lateral creepage when the wheel slides laterally on the top of rail. Due to its unique tone characteristics and high sound pressure level, the human ear is very sensitive to it. With the improvement of living standards, people's tolerance for squeal noise is getting lower and lower. In order to mitigate and control squeal noise, it is necessary to analyze the sensitivity of relevant parameters. In this paper, a prediction model is developed to analyze the effect of various parameters on the sound pressure level of squeal noise. The results show that squeal noise can be significantly reduced by decreasing angle of attack and rolling speed. In addition, the sound pressure of squeal noise can be reduced by reasonably controlling the negative slope of creepage. Furthermore, this mathematical model is used to perform a sensitivity analysis. The results show that squeal noise is most sensitive to the parameter of angle of attack, and the reason for this is illustrated. In particular, the analysis shows that modifying the slope of the creepage curve is effective in mitigating squeal noise, which provides a reference for mitigating and controlling squeal noise in practice, such as the application of friction modifiers at wheel/rail interface.

Tinnitus is an unusual sensation of hearing perceived without direct sound source. Given the multiplicity of management methods, a work of schematization of the most used therapies; i.e. Tinnitus Retraining Therapy (TRT), Cognitive and behavioral therapy (CBT), and Phase-out” sound therapy (POST) is led based on the Jastreboff model. Human ear models can be used to predict therapy effectiveness. Therefore, we presented three different models (an electric model, an acoustic model and a finite element model) which we then compared by applying them to two external ear shapes. The first is considered to have an auditory canal of uniform section and the second approaches the real shape of the external ear (variable section). The finite element (FE) model gives the most accurate results seeing that it allows to approach the real dimensions of the outer ear. The acoustic model gives results with a maximum deviation of 7,2 percent compared to the FE model with reduced computation time and simpler manner. The electric model is best suited if we consider the outer ear as a tube of uniform section closed at its end.

In the field of ear shape clustering, 3D ear modeling and ear related personal product implementation, it is of great significance to retrieve the accurate position of some key physiological curves and key points. Traditional edge extraction method is very sensitive to lighting and pose variations. A method for extracting the key physiological curves of the human ear based on an improved instance segmentation network YOLACT is proposed in this paper. By labeling the human ear dataset, the YOLACT model is trained. By improving the network prediction process, it can accurately segment different regions of the human ear and extract key physiological curves. Compared with the original YOLACT and other segmentation methods, it shows higher segmentation accuracy on the test dataset and can extract more accurate human ear physiological curves. The proposed method also shows more robustness under lighting variation, pose variation and slight occlusion.

The ear, as an important part of the human head, has received much less attention compared to the human face in the area of computer vision. Inspired by previous work on monocular 3D face reconstruction using an autoencoder structure to achieve self-supervised learning, we aim to utilise such a framework to tackle the 3D ear reconstruction task, where more subtle and difficult curves and features are present on the 2D ear input images. Our Human Ear Reconstruction Autoencoder (HERA) system predicts 3D ear poses and shape parameters for 3D ear meshes, without any supervision to these parameters. To make our approach cover the variance for in-the-wild images, even grayscale images, we propose an in-the-wild ear colour model. The constructed end-to-end self-supervised model is then evaluated both with 2D landmark localisation performance and the appearance of the reconstructed 3D ears., Submitted to VISAPP

In order to make full use of the noise information of substation power equipment and to deeply explore the value of noise, a voiceprint imaging technology in substation patrol was proposed in this paper which replace the the noise monitoring by human ear. The basic principle, algorithm, array selection and other technical details of the voiceprint imaging technology were introduced in detail. The effectiveness of the technical measure in substation patrol was proved by an application case. The voiceprint imaging technology in substation patrol is safe, fast and accurate. It fills the technical gap of power equipment noise patrol and greatly improves the efficiency of substation patrol.

The key to underwater target recognition is to extract the effective features of underwater target radiation noise. This paper presents an effective method for underwater target recognition and classification by extracting Mel-Frequency Cepstral Coefficients (MFCCs) features of underwater target radiation noise. Compared with traditional spectral analysis methods, MFCC makes full use of the non-linear auditory effect of the human ear with different perception capabilities for sounds of different frequencies. In this paper, the classification experiment of the radiated noise of the three types of measured underwater targets is done, where the MFCC feature vectors of the three types of targets are extracted, and the K-Nearest Neighbor (K-NN) algorithm is used to classify and identify them. Finally, the experimental results show that the method is effective.

A 24 GHz Doppler radar based stethoscope has been modified for non-contact diagnosis of heart sounds in a high-noise environment. By adding a digital single-sideband (SSB) modulator, the very low-frequency and difficult to hear heart sounds detected by this stethoscope were shifted toward the more sensitive part of the human ear’s frequency response.In a quiet environment, the heart sounds heard by the modified radio frequency (RF) stethoscope are shown to be the same as those heard by a conventional stethoscope. However, the modified RF stethoscope has an advantage in an extremely high noise environment, where a conventional stethoscope cannot be used. Under those conditions, the quality of the RF stethoscope’s acoustic response is shown to remain substantially unchanged.

Traditional on-line detection of partial discharge based on ultrasonic method has become an important means to detect the insulation condition of electrical equipment. However, most of its detection is based on the threshold value to realize the function similar to “traffic light”, that is, to detect whether partial discharge occurs or not. Gammatone frequency cepstral coefficient (GFCC) is based on human cochlear model and are widely used in voiceprint identification. This paper proposed an extraction algorithm of the characteristic parameter GFCC, and the PD ultrasonic signals of particle discharge and floating discharge are taken as examples, the GFCC of ultrasonic signals of those discharges are extracted. A preliminary partial discharge ultrasonic identification method based on GFCC is proposed. The method can realize on-line intelligent nonintrusive detection of the insulation state of high-voltage equipment, and break the limitation of traditional detection and diagnosis relying on certain threshold or human ear identification.

Introducción y Objetivos: En este trabajo mostramos un nuevo tubo de ventilación transtimpánico cuyo diseño pretende solventar los efectos indeseables aparecidos durante la inserción de tubos de ventilación actualmente comercializados; analizamos su comportamiento mecánico en un modelo 3D del oído a fin de optimizar su comportamiento acústico. Métodos: Para el diseño del tubo se empleó un software autoCAD; El comportamiento mecánico se analizó en un modelo computadorizado dinámico 3D del oído humano basado en el método de los elementos finitos (FEM). Resultados: El nuevo tubo de ventilación posee un tamaño y una masa significativamente menores a los actualmente disponibles en el mercado lo que provoca un menor interferencia en la vibración del sistema tímpano-osicular; su diseño facilita que permanezca insertado mientras las condiciones del paciente así lo aconsejen evitando su caída hacia la caja o su precoz extrusión. Conclusiones: Las ventajas teóricas biológicas y acústicas del nuevo tubo desarrollado (con menor masa y cuyo diseño evita las complicaciones de los actuales) puede abrir una nueva posibilidad de tratamiento de la otitis media seromucosa crónica. Introduction and Objetive: We show a new trans-tympanic ventilation tube whose design and mass are intended to solve the undesirable effects that appeared during the insertion of currently commercialized ventilation tubes; We analyze its mechanical behavior in a 3D model of the ear in order to optimize its acoustic behavior. Methods: For the design of the tube an autoCAD software was used; The mechanical behavior was analyzed in a 3D dynamic computerized model of the human ear based on the finite element method (FEM). Results: The new ventilation tube has a size and mass significantly smaller than those currently available in the market, which causes less interference in the vibration of the eardrum-osicular system; Its design facilitates it to remain inserted while the patient's conditions so advise avoiding its fall into the middle ear cavity or its early extrusion. Conclusions: The biological and acoustic advantages of the new developed tube (with less mass and whose design avoids the complications of the current ones) can open a new possibility of treatment of chronic seromucous otitis media.

As the body centric communication technology develops in monitoring real-time healthcare, fitness, sports, and military, significant attention arises in regarding possible health risks. The aim of ...

Ear print is an imminent biometric modality that has been attracting increasing attention in the biometric community. However, compared with well-established modalities, such as face and fingerprints, a limited number of contributions has been offered on ear imaging. Moreover, only several studies address the aspect of ear characterization (i.e., feature design). In this respect, in this paper, we propose a novel descriptor for ear recognition. The proposed descriptor, namely, dense local phase quantization (DLPQ) is based on the phase responses, which is generated using the well-known LPQ descriptor. Furthermore, local dense histograms are extracted from the horizontal stripes of the phase maps followed by a pooling operation to address viewpoint changes and, finally, concatenated into an ear descriptor. Although the proposed DLPQ descriptor is built on the traditional LPQ, we particularly show that drastic improvements (of over 20%) are attained with respect to this latter descriptor on two benchmark data sets. Furthermore, the proposed descriptor stands out among recent ear descriptors from the literature.

The long-distance acoustic signal has low energy, high concealment and low signal-to-noise ratio (SNR), thus is often submerged in noise and interference. The conventional method of acoustic detection cannot classify or recognize the target. A sound recognition method has been proposed to simulate human ear auditory process by making the best of sound recognition advantage of human ear. First of all, those parts which function as energy gathering, frequency-selective amplification or information transmission in human ear are selected, and the auditory model of each part is established independently. Then, the structural parameters of each model are extracted, and the influence and change rules of structural parameters on the auditory signal are studied and optimized according to the auditory law. Finally, the auditory model is reconstructed based on the parameters extracted from each component, and the integrated model is used to carry out the voice recognition experiment under low SNR. The recognition rate of low SNR (−5 dB) acoustic event is improved by 6.5%.

Over the past years, the Lecanosticta acicola fungus or brown band is seriously damaging the pine forests of northern Spain. This fungus happens to dry the leaves and branches of trees. The present article describes the equipment and methodology for assessing the severity and spread of diseases in trees quickly, easily and cheaply. The concept is based on a basic assumption: a sick tree does not sound the same when it is hit as a healthy one does. The breakthrough character of the present work comes from a basic mechanical principle: if a tree loses water its density is reduced, so the sound that it makes when it is hit, also known as its acoustic response, is higher. If the tree is healthy it will grow, so the sound will be the same or deeper. The human ear cannot distinguish these variations or quantify them, but a microphone with an acquisition system can. After initial proofs of concept on real trunks, the function that relates the frequency increase to the degree of dehydration of the tree, and therefore to the severity of the disease, has been identified. Then, the method has been tested in a real forest with sick pines and compared to visual inspection of an expert. After several months of tests, it has been concluded that the trend of the acoustic method coincides with the visual assessment, with the difference that it is objective, while visual inspection is not. It has even demonstrated to be able to foresee the healing of trees before new leaves start to sprout.

In this paper, PATRAN software was used to establish a complete 3D finite element model of human ears, and it was then combined with NASTRAN software to analyze frequency responses. This paper conducted a detailed analysis on the dynamic parameters including umbo and stapes displacements of normal human ears under sound pressures 90 dB and 105 dB. The numerically computational results were compared with experimental data. When the analyzed frequency was less than 1000 Hz, the computational result of numerical simulation was well consistent with the upper limit. When the analyzed frequency was more than 1000 Hz, the computational result of numerical simulation was well consistent with the lower limit. Therefore, the numerically computational model was reliable. In addition, based on the verified model, this paper studied vibration characteristics of spiral basilar membrane after replacing artificial auditory ossicle based on the whole hearing system, and found that vibration characteristics of spiral basilar membrane had an obvious change at low and high frequencies after replacing artificial auditory ossicle TORP. Using finite element method to analyze vibration characteristics of spiral basilar membrane can well predict the hearing recovery effect after replacing artificial auditory ossicle. Compared with normal ears, the vibration level of spiral basilar membrane after replacing artificial auditory ossicle has slowed down in 100 Hz-600 Hz, 2000 Hz-4000 Hz and 7000 Hz-10000 Hz, and has been strengthened in 600 Hz-2000 Hz and 4000 Hz-7000 Hz, which provided some help for the hearing recovery at the high-frequency band.

Audio Scene Classification (ASC) is a very important part of acoustic scene understanding. Recurrent Neural Network (RNN) models are widely adopted with proven successes in ASC. In this paper, the Gated Recurrent Unit (GRU) is introduced for ASC. The Mel-Frequency Cepstral Coefficients (MFCCs) which are closer to the auditory characteristics of the human ear are extracted as feature vectors and inputs to the classifier. The GRU is a slightly more simplified variation of the Long Short-Term Memory Unit (LSTM), and it is simpler than the standard LSTM. The Gated Recurrent Units (GRUs) are used to build a network model to implement ASC while controlling the number of parameters with respect to their performance. In these experiments, good results were achieved on the UrbanSound8Kdataset with an average accuracy of 83.8%, which has significant advantages over traditional network models.

The use of deep neural networks for speech recognition and recognizing speech commands continues to grow. This necessitates an understanding of the security risks that goes along with this technology. This paper analyzes the ability to interfere with the performance of neural networks for speech pattern recognition. With the methods proposed herein, it is a simple matter to create adversarial data by overlaying audio of a command at a fairly unnoticeable amplitude. This causes the neural network to lose around 20% accuracy and misidentify commands for other commands with an average to high confidence value. Such an attack is virtually undetectable to the human ear.

Abundant research has been done on the improvement of the security and trustworthiness of biometric systems. The aim of this paper is to demonstrate the image key-point extraction technique and establish its uniqueness for biometric identification. Ear features comes out to be one of the important biometric systems, which prove to have great potential, in identifying humans in the real world applications. In this work, key-point based matching and recognition is done using SIFT (Scale Invariant Feature Transform) technique. This approach extracts features from images of distinctive invariant. These images are utilized to perform consistent matching between various objects (ear). The key-points are invariant to image scale and hence can provide good matching over a wide range of images. The distinctive features have been matched correctly using the proposed technique and tested on a large database of ear images. This study helps in establishing that the experimental results show improvements in recognition accuracy.

The accuracy of existing auditory saliency calculation models was analyzed in this paper. The subjective auditory attention saliency of 20 pieces of sound materials was judged by psychoacoustic experiment. Then the objective saliency of these sound materials was calculated using the existing auditory saliency calculation models. According to the relationship between the calculation results of the saliency calculation models and the subjective auditory attention perception of the human ear, the accuracy of existing auditory saliency calculation models was analyzed. The experimental results showed that, although auditory saliency computational models could calculate and judge the attention saliency of sound on certain extent, they had own shortcomings when calculating different types of sounds. Choosing different computational models based on the characteristics of the sound could improve the prediction accuracy of the auditory saliency calculation models.

Human ear recognition has attracted more attention in the past several years. In terms of the redundant information of Histogram of Oriented Gradient (HOG), this paper proposes a human ear recognition method in which the principal component analysis (PCA) was carried out on the HOG dimension reduction, then fused with local binary pattern (LBP) features. Using minimum distance classification for classification, experiments on USTB human ear image library shows that the proposed method improves the recognition rate and speeds up the training and detection.

Immersive, stereoscopic displays may be instrumental to better interpreting 3-dimensional (3D) data. Further- more, the advent of commodity-level virtual reality (VR) hardware has made this technology accessible for meaningful applications, such as medical education. Accordingly, in the current work we present a commodity- level, immersive simulation for interacting with human ear anatomy. In the simulation, users may interact simultaneously with high resolution computed tomography (CT) scans and their corresponding, 3D anatomical structures. The simulation includes: (1) a commodity level, immersive virtual environment presented by the Oculus CV1, (2) segmented 3D models of head and ear structures generated from a CT dataset, (3) the ability to freely manipulate 2D and 3D data synchronously, and (4) a user-interface which allows for free exploration and manipulation of data using the Oculus touch controllers. The system was demonstrated to 10 otolaryngolo- gists for evaluation. Physicians were asked to supply feedback via both questionnaire and discussion in order to determine the efficacy of the current system as well as the most pertinent applications for future research.

We propose a simple and yet effective technique for shape-based ear localization. The idea is based on using a predefined binary ear template that is matched to ear contours in a given edge image. To cope with changes in ear shapes and sizes, the template is allowed to deform. Deformation is achieved by dividing the template into segments. The dynamic programming search algorithm is used to accomplish the matching process, achieving very robust localization results in various cluttered and noisy setups.

Biosimulation models are used to understand the multiple or different causative factors that cause impairment in human organs. Finite Element Method (FEM) provide a mathematical framework to simulate dynamic biological systems, with applications ranging from human ear, cardiovascular, to neurovascular research. Finite Element (FE) Biosimulation experiments produce huge amounts of numerical data. Visualising and analysing this huge numerical biosimulation data is a strenuous task. In this paper, we present a Linked Data Visualiser–called SIFEM Visualiser–to help domain-experts (experts in the field of ear mechanics) and clinical practitioners (otorhinolaryngologists) to Visualise, analyse and compare biosimulation results from heterogeneous, complex, and high volume numerical data. The SIFEM visualiser builds on conceptualising different aspects of biosimulations. In addition to the visualiser, we also propose how biosimulation numerical data can be conceptualised, such that it sustains the visualisation of large numerical data. The SIFEM Visualiser aims to help domain scientists and clinical practitioners exploring and analysing Finite Element (FE) numerical data and simulation results obtained from different aspects of inner ear (Cochlear) model — such as biological, geometrical, mathematical, and physical models. We validate the SIFEM Visualiser in both dimensions of qualitative and quantitative evaluation. This publication has emanated from research supported in part by the research grant from Science Foundation Ireland (SFI) under Grant No. SFI/12/RC/2289 and EU project SIFEM (contract number 600933). peer-reviewed

Presently there already existed a few human ear image databases, which have been very influential in advancing the research on ear recognition. However no ear video databases are available. In this paper, we introduce the construction and basic content of the Chinese Ear Video Database (CEVD) and some primary evaluation results on it. The CEVD consists of 3600 ear video segments collected from 120 subjects. All the video segments in the database were collected in specially designed environment with three principal variations of illumination condition, viewing angle and interference. Compared with other public ear database, CEVD can not only be used for image-based applications but also video-based applications. In this paper we introduce the database and describe the collecting procedure. It excels in its large-scale and variation modes and is expected to have positive impact on the development and evaluation of ear recognition algorithms. This paper also gives experiment results of improved CamShift and AdaBoost algorithm on the database.

In recent years, automatic ear recognition has become a popular research. Effective feature extraction is one of the most important steps in Content-based ear image retrieval applications. In this paper, the authors proposed a new vectors construction method for ear retrieval based on Block Discriminative Common Vector. According to this method, the ear image is divided into 16 blocks firstly and the features are extracted by applying DCV to the sub-images. Furthermore, Support Vector Machine is used as classifier to make decision. The experimental results show that the proposed method performs better than classical PCA+LDA, so it is an effective human ear recognition method.

Audio-based automated pulmonary symptom detection has the potential to offer accurate and continuous assessment of patients with lung disease. However, privacy preservation becomes a significant issue when it comes to continuous passive audio recording. Various techniques have been employed to obfuscate the speech within audio in these applications. However, that penalizes the accuracy of detection by affecting of-interest, non-speech audio parts. This is inevitably undesirable as it contradicts the notion of sensing. In this paper, we propose a novel algorithm to achieve the goal by employing a machine-learning-based vowel detection algorithm. The algorithm is implemented in a configurable manner to address many different scenarios of data collection. Logistic regression has been utilized to make the algorithm feasible for on-device implementation. We have shown that our algorithm achieves the goals in that the obfuscated speech is unrecognizable while cough sounds are identifiable by symptom detection models as well as a human ear.

We aim at building and studying artificial hair cells (AHC) based on MEMS technology to understand the extraordinary sound perception of the human ear and build a sensor system with similar properties. These perception properties, i.e. detecting six orders of sound pressure level and simultaneously frequency differences of only 3-5 Hz, are obtained mainly due to the sophisticated biological sensors in the inner ear, called hair cells, which convert the acoustic waves into electric signals. They amplify weak inputs and compress larger ones, known as compressive nonlinearity, thus enabling this impressive dynamic range, typically not captured by current engineering solutions. We tackle this demand by building artificial hair cells on the basis of smart, self-actuated and self-sensing mechanical resonator beams with suitable actuation feedback. Thereby, we take advantage of the fact that the compressive nonlinearity arises naturally in dynamical systems tuned to a bifurcation point. This tuning is achieved by an appropriate feedback loop inspired by physiological models. Initial results on the detection properties of a single AHC will be shown demonstrating amplification and a decreased width of the resonance peak.

Ultrasound is composed of mechanical sound waves that originate from molecular movements that oscillate in a propagation medium. The waves have a very high frequency, equal to approximately 20 kHz, are divided into two categories (i.e., low-intensity and high-intensity waves) and cannot be perceived by the human ear. Nature has created the first ultrasound applications. Bats use ultrasound to navigate in the dark, and many cetaceans use echolocation to detect prey or obstacles using ultrasound produced by their vocal system. Ultrasound is commonly associated with the biomedical field. Today, ultrasound-based methods and equipment are available to detect organs, motion, tumour masses, and pre/post-natal handicaps, and for kidney stone removal, physiotherapy, and aesthetic cures. However, ultrasound has found multiple applications in many other fields as well. In particular, ultrasound has recently been used in the food industry to develop various effective and reliable food processing applications. Therefore, this review summarizes the major applications of ultrasound in the food industry. The most common applications in the food industry include cell destruction and extraction of intracellular material. Depending on its intensity, ultrasound is used for the activation or deactivation of enzymes, mixing and homogenization, emulsification, dispersion, preservation, stabilization, dissolution and crystallization, hydrogenation, tenderization of meat, ripening, ageing and oxidation, and as an adjuvant for solid-liquid extraction for maceration to accelerate and to improve the extraction of active ingredients from different matrices, as well as the degassing and atomization of food preparations.

Recently, intensive research efforts are conducted on the human ear as a promising biometric modality for identity recognition. However, one of the main challenges facing ear recognition systems is to find robust representation for the image information that is invariant to different imaging variations. Recent studies indicate that using the distribution of local intensity gradients or edge directions can better discriminate the shape and appearance of objects. Moreover, gradient-based features are robust to global and local intensity variations as well as noise and geometric transformation of images. This paper presents an ear biometric recognition approach based on the gradient-based features. To this end, four local feature extractors are investigated, namely: Histogram of Oriented Gradients (HOG), Weber Local Descriptor (WLD), Local Directional Patterns (LDP), and Local Optimal Oriented Patterns (LOOP). Extensive experiments are conducted for both identification and verification using the publicly available IIT Delhi-I, IIT Delhi-II, and AMI ear databases. The obtained results are encouraging, where the LOOP features excel in all cases achieving recognition rates of approximately 97%.

Hearing damage induced by blast exposure is a common injury in military personnel involved in most operation activities. Personal hearing protection devices such as earplugs come as a standard issue for Service members; however, it is not clear how to accurately evaluate the protection mechanisms of different hearing protection devices for blast overpressures (BOP). This paper reports a recent study on characterization of earplugs’ protective function to BOP using human cadaver ears and 3D finite element (FE) model of the human ear. The cadaver ear mounted with pressure sensors near the eardrum (P1) and inside the middle ear (P2) and with an earplug inserted was exposed to BOP in the blast test chamber. P1, P2, and BOP at the ear canal entrance (P0) were simultaneously recorded. The measured P0 waveform was then applied at the ear canal entrance in the FE model and the P1 and P2 pressures were derived from the model. Both experiments and FE modeling resulted in the P1 reduction which represents the effective protection function of the earplug. Different earplugs showed variations in pressure waveforms transmitted to the eardrum, which determine the protection level of earplugs.

Recognizing identity from morphological shape of the human ear using one sample image per person in training-set (i.e. only one model of the individual to be identified is registered in the database and available for the task of identification), with insufficient and incomplete training data, dealing with strong person-specificity can be very challenging. In addition, most encountered testing-images in real world applications are not in high quality due to their acquisitions in difficult conditions (ex, video-surveillance) which cause more challenges like: rotated images or images with low resolution. In continuation to our previous works on ear recognition, we present in this paper an experimental and comparative study on the effects of rotation and scaling of ear images using only one sample image per person in training-set which are considered as problems largely encountered in real world applications. Several local color texture descriptors are tested and compared under several color spaces. Support Vector Machine (SVM) is used as a classifier. We experiment with USTB-1 ear database. The experiments show very acceptable and interesting results in comparison to those reported in literature.

A Thai government's law prohibiting the cutting down of trees for making Thai xylophones (Ranā d xek) has had a major effect on musical instrument makers. It is necessary for these instrument makers to now synthesize or find a new replacement material. The original instrument must be used as a benchmark in terms of sound. An objective measurement procedure, beside the human ear, is needed to compare sounds of instruments made of the original wood and new materials. In this experimental project, audio measurements of the original Thai xylophone's sound were collected and statistically analyze, focusing on the inharmonic structure of its spectrum of partials. The result is shown as numerical data. This data can now be used as a benchmark and compared to possible replacement materials in further research.

In the current years, many kinds of prostheses have been developed to replace parts with deformity or absent from the human body. Facial prosthesis production methods have undergone little change in the last 40 years. Today, manual techniques are used to produce prostheses, which are usually cast in wax over a replica of the patient’s anatomy. Silicone is the most appropriate structural material used in the production of atrial prostheses due to its mechanical and chemical characteristics. Recently, new technologies, such as 3D scanning and additive manufacturing techniques are being applied in the production of auricular prostheses with better cost/quality relation when compared to the manual manufacturing process. In this research, four methods to acquire the structure of a human ear were investigated (photogrammetry, 3D scanning, 3D reconstruction of computed tomography images and parameterized 3D modeling) to produce auricular prosthesis molds by additive manufacture using the fused deposition modeling (FDM) technique. The best silicone ear prosthesis produced shows an excellent esthetic result with 0.1–3% of dimensional error. Mechanical analysis of the silicone (tensile strength and hardness tests) shows that the prostheses produced have excellent mechanical resistance that is not altered by the pigmentation process. The research demonstrates the feasibility of an accessible methodology to produce ear prostheses using free software, technologies and supplies available in the market.

Morphable models are useful shape priors for biometric recognition tasks. Here we present an iterative process of refinement for a 3D Morphable Model (3DMM) of the human ear that employs data augmentation. The process employs the following stages 1) landmark-based 3DMM fitting; 2) 3D template deformation to overcome noisy over-fitting; 3) 3D mesh editing, to improve the fit to manual 2D landmarks. These processes are wrapped in an iterative procedure that is able to bootstrap a weak, approximate model into a significantly better model. Evaluations using several performance metrics verify the improvement of our model using the proposed algorithm. We use this new 3DMM model-booting algorithm to generate a refined 3D morphable model of the human ear, and we make this new model and our augmented training dataset public.