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The acoustical efficiency of machines varies in the range of 10−9 to 10−5. This means even high power machines generate sound powers of a few Watts only. Due to the high sensitivity of the human ear however, such low sound powers create close to the machine loudnesses higher than 100 phon (64 sone). Consequently, the assessment of machinery noise emission requires relations to these subjective properties.

We propose a generalized data driven constraint for support vector machines exemplified by classification of paired observations in general and specifically on the human ear canal. This is particularly interesting in dynamic cases such as tissue movement or pathologies developing over time. Assuming that two observations of the same subject in different states span a vector, we hypothesise that such structure of the data contains implicit information which can aid the classification, thus the name data driven constraints. We derive a constraint based on the data which allow for the use of the l1-norm on the constraint while still allowing for the application of kernels. We specialize the proposed constraint to orthogonality of the vectors between paired observations and the estimated hyperplane. We show that imposing the constraint of orthogonality on the paired data yields a more robust classifier solution, compared to the SVM i.e. reduces variance and improves classification rates. We present a quantitative measure of the information level contained in the pairing and test the method on simulated as well as a high-dimensional paired data set of ear-canal surfaces.

Korotkoff sound sensors (KSS) are used for arterial blood pressure (AP) measurement following Korotkoff’s method [29]. The method consists of acoustic signal correlation: Korotkoff sounds, arising in a patient’s tissues when the artery is squeezed by the cuff, with the corresponding value of pneumatic pressure in this cuff. The moments of Korotkoff sound occurrence and termination characterize upper (systolic) and lower (diastolic) arterial pressure (AP) respectively. The method has a number of methods of technical implementation. The elementary implementation consists of sound registration by a mechanical phonendoscope. Important drawbacks of this implementation are low phonendoscope sensitivity, and also the fact that the pulse signals spectrum lies in a low-frequency area in which the human ear has minimum sensitivity.

Modeling the musical notes as fuzzy sets provides a flexible framework which better explains musicians’ daily practices. Taking into account one of the characteristics of the sound: the pitch (the frequency of a sound as perceived by human ear), a similarity relation between two notes can be defined. We call this relation compatibility. In the present work, we propose a method to asses the compatibility between musicians based on the compatibility of their interpretations of a given composition. In order to aggregate the compatibilities between the notes offered and then obtain the compatibility between musicians, we make use of an OWA operator. We illustrate our approach with a numerical experiment.

There are always two main problems in identification of human beings through their ear images: 1- If distances of the individual from camera changes, the sizes of ears in the saved images are varied in proportion to this distance. 2- If head of people in taken images is tilted upwards or downwards, this causes ear images of these people rotate in proportion to saved ear images in database. In both of these cases, all identification systems do not work properly. In this article, we proposed a new method for normalizing human ear images by detecting the rotation and scaling variation, and normalizing the ear images accordingly. Our proposed method works well on all ear databases and all ear images (either left or right) which have been taken from front side of the ears. Our method provides high performance to the biometric identification systems to identify human being, even when the images of human ears are taken from long distance with small scale.

Many hearing aid users experience physical discomfort when wearing their device. The main contributor to this problem is believed to be deformation of the ear and ear canal caused by movement of the mandible. Physical discomfort results from added pressure on soft tissue areas in the ear. Identifying features that can predict potential deformation is therefore important for identifying problematic cases in advance. A study on the physical deformation of the human ear and canal due to movement of the mandible is presented. The study is based on laser scannings of 30 pairs of ear impressions from 9 female and 21 male subjects. Two impressions have been taken from each subject, one with open mouth, and one with the mouth closed. All impressions are registered using non-rigid surface registration and a shape model is built. From each pair of impressions a deformation field is generated and propagated to the shape model, enabling the building of a deformation model in the reference frame of the shape model. A relationship between the two models is established, showing that the shape variation can explain approximately 50% of the variation in the deformation model. An hypothesis test for significance of the deformations for each deformation field reveals that all subjects have significant deformation at Tragus and in the canal. Furthermore, a relation between the magnitude of the deformation and the gender of the subject is demonstrated. The results are successfully validated by comparing the outcome to the anatomy by using a single set of high resolution histological sectionings of the region of interest.

Application and research of ear recognition technology is a new subject in the field of biometrics recognition. Earlier research showed that human ear is one of the representative human biometrics with uniqueness and stability. Feasibility and characteristics of ear recognition was discussed and recent advances in 2D and 3D domain was presented. Furthermore, a proposal for future research topics was given, such as ear database generation, ear detection, ear occluding problem and multimodal biometrics with face etc.

Biometrics identification methods proved to be very efficient, more natural and easy for users than traditional methods of human identification. The future of biometrics leads to passive physiological methods based on images of such parts of human body as face and ear. The article presents a novel geometrical method of feature extraction from human ear images in order to perform human identification.

Modelling musical instruments is a particularly challenging task. For these structures, the complexity of both the continuous model and numerical formulation is mainly due to the high sensitivity of the human ear. An overview of the major difficulties are given, with regard to elastic and acoustic field equations, as well as to initial and boundary conditions. To illustrate these features, some recently obtained results in the modelling of some selected instruments are commented. A number of current advances and research perspectives for the near future are briefly described.

This paper reports the development of a full-scale instrumented model of the human ear that permits quantitative evaluation of the utility of a microsurgical assistant robot in the surgical procedure of stapedotomy.

Ultrasound and infrasound differ from “ordinary” sounds in three distinct ways that influence all the considerations of this chapter. The first and most obvious characteristic of these sound types is that, by definition, they are “extreme” frequencies that fall outside the normal response curve for the human ear (see Figure 1) and are therefore inaudible. Ultrasound, which includes biologically significant sounds ranging from 15 kHz or so up to 200 kHz, is too high in frequency. Infrasound, effectively extending downwards from about 20 to 0.1 Hz or less, is too low in frequency. In both cases, therefore, it is necessary to use special instruments merely to detect the signals, which only increases the fascination of studying them. When the appropriate technology is applied, it becomes possible to observe phenomena that may be quite common among nonhuman species, but have previously been unknown.

Mechanical radiant energy beyond the upper limit of perception by the human ear was discovered near the end of the nineteenth century. The piezoelectric effect was described by the Cyrie brothers in 1880. They found that the application of mechanical pressure to a tourmaline crystal created a potential difference across the two surfaces of the crystal and that, conversely, application of an electric potential caused deformation of the crystal. The most important advance leading to ultrasonic diagnosis was the invention of the “reflectoscope” by Firestone (1912); this was an instrument designed to make possible the artifact-free examination of material. Using a similar apparatus, Ludwig (1949) succeeded in demonstrating gallstones in vivo.

As was pointed out in Chap. I, communication by means of language supposes differences, of sound, or of written or printed figures on the expression level, and differentiated morphological, syntactical or semantic units and combinations on the content level. Communication supposes variation. The transfer medium used for communication must not be predictable to 100%. It has to contain at least some element of surprise. An unvaried and unvariable medium would be predictable both along the time axis (a monotonous sound-wave such as a sinuosidal curve with constant amplitude; Fig.7), and along the space axis (an undifferentiated visual stimulus, e.g. a straight line or an unlimited repetition of the same printed figure). As the transmission of the message in a restricted sense (physically) is made only through the minimal expression units and through combinations of these — the physical manifestations of which (as sound-waves and as articulatory movements, or as printed or written figures) are the only outside facts actually present in the passage from brain to brain (from 3–8 in our model in Chap.II) —, we shall examine in this special chapter the expression level and try to give a survey of the principal sound differences, and their physiological correlatesi, the principal task of what is traditionally called general phonetics. That is to say that we must start by examining the amount of information of a sound-wave emanating from the speech apparatus of a human being (5–6 in our model). And we do this to begin with without taking into account any differences of distribution between the possible acoustic stimuli. We shall also permit ourselves in this context to talk about our speech mechanism notwithstanding the well-known fact that, originally, man has no speech organ in the same sense as he has a breathing and a digestive apparatus. The so called organs of speech all have other primary tasks (intake of food, respiration, etc.) and have been adopted secondarily to communication needs. They all still keep their primary functions besides that of speaking. We finally have to take into consideration the reception mechanism (i.e. the human ear) and its particular characteristics.