Search

The invention of photography in the 19th century changed our perception of reality forever. An image could be recorded within minutes, representing ‘unfiltered’ reality. The requirement to enlist an illustrator or painter to interpret an event or location, creating an artistic interpretation, was no longer a necessity. In addition to the scientific challenge of how to capture light and make the image permanent, the distribution of the image material became a combined scientific and commercial endeavour. Inventions designed to capture photographic images, make them permanent and distribute them to an eager audience were numerous. Most of them have been forgotten, often because they were too complicated or they were replaced by more reliable and cheaper methods. Two of these – Wet-collodion photography and Woodburytype—are still in use by modern experts because of their outstanding image quality. In March 1851, Frederick Scott Archer published in the magazine Chemist (under the chapter heading II. Chemical Manufacturers and Agricultural Chemistry), a small abstract on his findings titled On the Use of Collodion in Photography 1. Archer’s formula for capturing and preserving an image permanently onto a surface has become known as Wet-collodion or Wetplate. He gave to the world a technique that to this day offers the highest resolution and the most beautiful continuous tone of any photographic process. When combined with the most impeccable of continuous tone printing techniques ever invented—the Woodburytype—the result is something close to a perfect union, never since equalled.

The purpose of this study is to determine the influence of Color Managed Workflow (CMW) on digital color photo printing. In a CMW, a device profile represents the color characteristics of a device (monitor, scanner, and printer) to be used in the printing workflow. The experiment analyzed the effect of device profiles on scanned and printed continuous-tone color images. The guiding objectives of this study allowed testing of an accepted color management practice to gain a better understanding of the presumptions associated with the application of device profiles. The experiment examined two groups of images [scanned (K1) and printed K2 (K = 2)]. The conclusions of this study are based upon an analysis of colorimetric data, visual assessment, and associated findings. The data from the experiment revealed that very minor color differences were found between the two groups of photos. The colorimetric data suggests that integration of device profiles is important in a CMW as it allows the process to obtain accurate output colors for a desired purpose.

The digital halftoning is widely adopted in printing applications to obtain a high-quality printing output with optimal ink usage. The technique comprises of transforming a continuous tone grayscale image into its approximate binary image. On the other hand, image quality assessment deals with estimating the perceptual quality of the degraded image. The paper deals with exploitation of ordered dithering technique of the halftoning, to improve the performance of the existing image quality indices. The widely used full reference image quality metrics such as Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) are considered for this paper. The main objective aims to improvise the correlation coefficient between the widely used IQM and Human Visual System (HVS). Moreover, the computation demand of the existing indices is also reduced significantly. The well-known LIVE image quality database is used to validate the performance of the proposed approach. To perform correlation analysis, Pearson, Spearman and Kendall correlation coefficients are used. From experimental analysis, it can be validated that the proposed IQM performs with improved accuracy and less computational demand.

Illusions give intriguing insights into perceptual and neural dynamics. In the auditory continuity illusion, two brief tones separated by a silent gap may be heard as one continuous tone if a noise burst with appropriate characteristics fills the gap. This illusion probes the conditions under which listeners link related sounds across time and maintain perceptual continuity in the face of sudden changes in sound mixtures. Conceptual explanations of this illusion have been proposed, but its neural basis is still being investigated. In this work we provide a dynamical systems framework, grounded in principles of neural dynamics, to explain the continuity illusion. We construct an idealized firing rate model of a neural population and analyze the conditions under which firing rate responses persist during the interruption between the two tones. First, we show that sustained inputs and hysteresis dynamics (a mismatch between tone levels needed to activate and inactivate the population) can produce continuous responses. Second, we show that transient inputs and bistable dynamics (coexistence of two stable firing rate levels) can also produce continuous responses. Finally, we combine these input types together to obtain neural dynamics consistent with two requirements for the continuity illusion as articulated in a well-known theory of auditory scene analysis: sustained responses occur if noise provides sufficient evidence that the tone continues and if there is no evidence of discontinuities between the tones and noise. By grounding these notions in a quantitative model that incorporates elements of neural circuits (recurrent excitation, and mutual inhibition, specifically), we identify plausible mechanisms for the continuity illusion. Our findings can help guide future studies of neural correlate of this illusion and inform development of more biophysically-based models of the auditory continuity illusion.

Powder-based 3DP was the first technique to print full color 3D objects with continuous tone. However, limited by the size of the printing platform, large 3D objects can hardly be printed efficiently which has affected the spread of this technology. In consideration of the above issues, this paper discussed the relationship between printing time and model height, and a algorithm for optimizing printing time was presented. Ten typical models had been printed to verify the effectiveness of XYX rotation method. The results showed that the maximum time reduction is 65.7% less than the modeling time before optimizing.

Halftoning is a method of quantifying a continuous tone image into a binary image. Halftone methods can be categorized into dithering, error diffusion and iterative method. We present a new method different from traditional halftoning algorithms for learning the mapping between continuous images and halftone images using conditional generative adversarial networks (conditional GANs). We regard halftoning and inverse halftoning as the process of image-to-image translation, and use the classic pix2pixHD network. In this work, we use multi-scale generator and discriminator architectures to perform both halftoning and its structural reconstruction. The experimental results show that this method can better fit some classical halftoning algorithms to realize halftoning and inverse halftoning. Compared with the existing methods, our method can better realize reconstruction of halftone images.

The reconstructed image of probabilistic visual cryptography (VC) is inevitably accompanied by the loss of secret image information and degradation of visual quality. Since halftone technique can realistically simulate grayscale continuous tone image with a binary image, in this paper, by combining VC sharing with grayscale image halftone technique, we propose a probabilistic VC scheme for grayscale image using EDBS (Efficient Direct Binary Search) to improve the quality of the reconstructed image. The probabilistic VC sharing is adopted into the EDBS halftone process, and the reconstructed image is taken as the optimization object. Through local optimizations and global iterations, the optimal reconstructed image is finally achieved under the established optimization strategy. Theoretically the proposed scheme possesses the same security strength as the traditional VC and experimentally the comparative analysis verifies the effectiveness of this scheme.

The complex f0 variations in continuous speech make it rather difficult to perform automatic recognition of tones in a language like Mandarin Chinese. In this study, we tested the use of target approximation model (TAM) for continuous tone recognition on two datasets. TAM simulates f0 production from the articulatory point of view and so allow to discover the underlying pitch targets from the surface f0 contour. The f0 contour of each tone represented by 30 equidistant points in the first dataset was simulated by the TAM model. Using a support vector machine (SVM) to classify tones showed that, compared to the representation by 30 f0 values, the estimated three-dimensional TAM parameters had a comparable performance in characterizing tone patterns. The TAM model was further tested on the second dataset containing more complex tonal variations. With equal or a fewer number of features, the TAM parameters provided better performance than the coefficients of the cosine transform and a slightly worse performance than the statistical f0 parameters for tone recognition. Furthermore, we investigated bidirectional LSTM neural network for modelling the sequential tonal variations, which proved to be more powerful than the SVM classifier. The BLSTM system incorporating TAM and statistical f0 parameters achieved the best accuracy of 87.56%.

Typical ILT goes through a continuous tone mask to define a greyscale mask for the best process window, followed by a conversion into actual mask geometries, which are typically Manahttanized to be compatible with printing on existing mask writers. On mask, however, the features to be printed are not Manhattan, and we demonstrate that, by not taking into account the actual mask shapes, current Manhattan mask 3D (M3D) approximations using width, shape, and corner libraries, give rise to poor predictions for the final aerial image. Now that curvilinear ILT is possible to manufacture, we introduce a fully curvilinear mask 3D approximations, compatible with ILT masks, that predict the aerial image significantly better than before.

Occlusion-effect and acoustic feedback are common complaints of the hearing aid user. The occlusion effect is described as an annoying quality of the user’s own voice that sounds hollow or boomy, while feedback instability results in an unpleasant loud continuous tone. Despite the availability of high performance feedback cancellers, severe to profound losses require large amplification and, as a result, certain degree of occlusion to minimize feedback. The smaller the vent, the greater is the feedback-path magnitude attenuation as well as the increase in the occlusion effect. This work presents a numerical simulation investigation about the concurrent use of the prediction-error-method feedback canceller and the feedforward-occlusion canceller in hearing aid applications. Evidences about mutual performance impact on both cancellers are pursued. The studied scenario takes into consideration three sizes of the ventilation opening of the hearing aids. Simulation results indicate that the individual performance of both cancellers is not affected when the stability is preserved. This finding can be of interest for hearing aid designers when setting up the canceller parameters.

Inverse halftoning as a classic problem has been investigated in the last two decades, however, it is still a challenge to recover the continuous version with accurate details from halftone images. In this paper, we present a statistic learning based method to address it, leveraging Convolutional Neural Network (CNN) as a nonlinear mapping function. To exploit features as completely as possible, we propose a Progressively Residual Learning (PRL) network that synthesizes the global tone and subtle details from the halftone images in a progressive manner. Particularly, it contains two modules: Content Aggregation that removes the halftone patterns and reconstructs the continuous tone firstly, and Detail Enhancement that boosts the subtle structures incrementally via learning a residual image. Benefiting from this efficient architecture, the proposed network is superior to all the candidate networks employed in our experiments for inverse halftoning. Also, our approach outperforms the state of the art with a large margin.

Dithering creates an illusion of continuous tone output for a binary device. Error diffusion-based dithering or halftoning is an efficient technique that is primarily getting used in printing. In this paper error diffusion halftoning has been used to achieve image dithering based on complex wavelets. Similar to the wavelet-based dithering, Floyd-Steinberg error diffusion has been incorporated, but in addition a new set of sub band amplification factors is coined in the proposed method to further enhance the contrast in a dithered image. Experimental results show that the proposed method is superior to state-of-the-art methods in terms of subjective and objective assessments.

To restore high quality continuous tone images from each class of halftone images, halftone image fine classification is the key problem. In this paper, a novel feature learning method is proposed for classifying 14 kinds of halftone images produced by the most well-known halftoning algorithms. This study employs the stacked sparse auto-encoders (SAE) trained with unsupervised learning for extracting features of halftone images, and then uses softmax regression with supervised learning for fine-tuning the deep neural network and classifying halftone images. In order to reduce the run-time of deep neural network and improve the image correct classification rate, we propose an effective patch extraction method for testing halftone images by measuring the mean and variance of local entropy in a patch. Halftone image classification is determined by the classification results of all effective patches inside an image via majority voting (MV). The experimental results demonstrate that our proposed method achieves an average correct classification rate (ACCR) of over 99.44% for 14 kinds of halftone images on two public image sets. Compared with state-of-the-art LMS-Bayes and M 10 - ML methods, the proposed SAE-MV method can distinguish the most categories of halftone images and achieve competitive ACCR, meanwhile, demonstrate better generalization performance. HighlightsA halftone image classification algorithm is proposed by using deep neural network.The intrinsic features of halftone images are extracted by the sparse auto-encoders.The effective patch extraction saves time cost and improves classification accuracy.The algorithm has superior classification accuracy and generalization performance.

Driving is a safety-critical task that predominantly relies on vision. However, visual information from the environment is sometimes degraded or absent. In other cases, visual information is available, but the driver fails to use it due to distraction or impairment. Providing drivers with real-time auditory feedback about the state of the vehicle in relation to the environment may be an appropriate means of support when visual information is compromised. In this study, we explored whether driving can be performed solely by means of artificial auditory feedback. We focused on lane keeping, a task that is vital for safe driving. Three auditory parameter sets were tested: (1) predictor time, where the volume of a continuous tone was a linear function of the predicted lateral error from the lane centre 0 s, 1 s, 2 s, or 3 s into the future; (2) feedback mode (volume feedback vs. beep-frequency feedback) and mapping (linear vs. exponential relationship between predicted error and volume/beep frequency); and (3) corner support, in which in addition to volume feedback, a beep was offered upon entering/leaving a corner, or alternatively when crossing the lane centre while driving in a corner. A dead-zone was used, whereby the volume/beep-frequency feedback was provided only when the vehicle deviated more than 0.5 m from the centre of the lane. An experiment was conducted in which participants (N = 2) steered along a track with sharp 90-degree corners in a simulator with the visual projection shut down. Results showed that without predictor feedback (i.e., 0 s prediction), participants were more likely to depart the road compared to with predictor feedback. Moreover, volume feedback resulted in fewer road departures than beep-frequency feedback. The results of this study may be used in the design of in-vehicle auditory displays. Specifically, we recommend that feedback be based on anticipated error rather than current error.

Digital halftoning is a technique to represent the binary approximation of continuous tone image meant for printing. There are wide variety of halftone methods available based on ordered dithering, error distribution, dot diffusion and iterative approaches. This paper emphasizes on the development of a digital halftone database (DHD) consisting of 23 varieties of halftone methods. Moreover, the detailed elaboration on implementation of each technique and its comparison study is carried out. The critical halftone characteristics such as homogeneity, isotropy, directional artifacts and its impact to the pattern quality are comprehensively analyzed using the stochastic halftone statistics. Finally, a complete performance comparison based on rendered image quality, spatial and spectral statistics of stochastic process parameters are analyzed. The DHD database consisting of more than 4500 halftone images along with some of its source code is made free access for scientific study and purposes.

Image quality assessment (IQA) is a challenging task in digital image processing where the images get distorted in several situations. In recent years, various IQA measures have been developed to assess the quality of images in subjective and objective manner. Although the popular measures like mean square error (MSE) and peak signal to noise ratio (PSNR) work well for grayscale images, they fail to measure the exact difference between two color images on a pixel by pixel basis. To overcome this problem, we made a slight modification in the MSE and PSNR to present new measures mean absolute variance and fidelity ration (FR). Moreover, we formulate a constant ‘k’ of value 9.542425094 to establish a relationship between FR of color and grayscale images. The performance of the proposed metric is validated by comparing its results with state of art metrics against a same set of benchmark dataset. Though the proposed method involves simple mathematical calculations and no human visual system model is employed, the experimental analysis shows that FR is found to be highly effective and robust measure especially for continuous tone, discrete tone, bi-level and inverted images. This measure is highly useful for applications require exact IQA where a change of one-pixel value is also not desirable.

International audience; We describe an interactive system that allows for sonifying arm movements. The aim is to support stroke patients going through rehabilitation by providing them with augmented auditory feedback that reacts to their movements. The system is based on IMU sensors (Inertial Measurements Unit) attached to each arm. The movement data are streamed in real-time to a laptop computer that generates various sounds or musical interactions using a program we developed. We tested different types of auditory feedback, each of them using a specific strategy for the sound-movement mapping. The first type of movement-sound mappings is based on direct relationships between the reaching distance and either the pitch of a continuous tone, or the tempo of a regular beat pattern. The second type of mapping is music-oriented: the user movement allows for controlling the tempo of musical pieces. The third type of mapping associates the hand position to specific environmental sounds. We report here on the technical system along with preliminary results in a clinical setting with both post-stroke patients and healthy users. CCS CONCEPTS • Human-centered computing → Sound-based input / output; Auditory feedback; Gestural input; Usability testing; Interaction devices

The process of conversion of continuous tone image to discrete dots is called halftoning. The existing halftoning techniques are not suitable for all types of images. Hence, in this paper, the contrast of color halftone image is enhanced using the proposed error diffusion with varying threshold (EDVT) halftoning. This algorithm is the combination of point processing and neighborhood processing halftoning techniques. EDVT color halftoning varies the threshold for each block of pixels, and also it diffuses the quantization error to the neighboring pixels. The simulation results have been carried out for color halftone images using EDVT halftoning algorithm, and it is analyzed with the quality parameters which reveals that it has improved quality than the existing halftoning techniques.

We distinguish between discrete objects and continuous entities in categorization and language, but might we actually see such stimuli differently? Here we report the one-is-more illusion, wherein ‘objecthood’ changes what we perceive in an unexpected way. Across many variations and tasks, observers perceived a single continuous object (e.g. a rectangle) as longer than an equated set of multiple discrete objects (e.g. two shorter rectangles separated by a gap). This illusion is phenomenologically compelling, exceptionally reliable, and it extends beyond space, to time: a single continuous tone is perceived to last longer than an equated set of multiple discrete tones. Previous work has emphasized the importance of objecthood for processes such as attention and visual working memory, but these results typically require careful analyses of subtle effects. In contrast, we provide striking demonstrations of how perceived objecthood changes the perception of other properties in a way that you can readily see (and hear!) with your own eyes (and ears!).

The present study investigated the effect of auditory feedback on planning and control of two-segment reaching movements and eye–hand coordination. In particular, it was examined whether additional auditory information indicating the progression of the initial reach (i.e., passing the midway and contacting the target) affects the performance of that reach and gaze shift to the second target at the transition between two segments. Young adults performed a rapid two-segment reaching task, in which both the first and second segments had two target sizes. One out of three auditory feedback conditions included the reach-progression information: a continuous tone was delivered at a consistent timing during the initial reach from the midway to the target contact. Conversely, the other two were control conditions: a continuous tone was delivered at a random timing in one condition or not delivered in the other. The results showed that the initial reach became more accurate with the auditory reach-progression cue compared to without any auditory cue. When that cue was available, movement time of the initial reach was decreased, which was accompanied by an increased peak velocity and a decreased time to peak velocity. These findings suggest that the auditory reach-progression feedback enhanced the preplanned control of the initial reach. Deceleration time of that reach was also decreased with auditory feedback, but it was observed regardless of whether the sound contained the reach-progression information. At the transition between the two segments, the onset latencies of both the gaze shift and reach to the second target became shorter with the auditory reach-progression cue, the effect of which was pronounced when the initial reach had a higher terminal accuracy constraint. This suggests that the reach-progression cue enhanced verification of the termination of initial reach, thereby facilitating the initiation of eye and hand movements to the second target. Taken together, the additional auditory information of reach-progression enhances the planning and control of multi-segment reaches and eye–hand coordination at the segment transition.

Compound Images are stacked with text, graphics and continuous tone contents. Screen Content Coding (SCC) improves the compression quality of images with multilayer of graphics and text that are generated by digital devices rather than camera-captured images, since these images possess repetitive patterns. We proposed an Intra-Frame Dictionary Coding based on hashing technique without modifying the HEVC syntaxes. In our proposed system the current blocks are mapped with related blocks holding the same hash value. These blocks are considered as prediction blocks and the best block is selected from the prediction blocks and encoded in a repository. Further the proposed coding efficiency can be enhanced by employing the Rate Distortion Optimization. The proposed system results when compared with the existing system displays a better performance of 95% in terms of compression ratio. The proposed algorithm is coded in MATLAB and the results were analyzed.

SH guided waves are used increasingly for non-destructive testing (NDT) applications, particularly for pipes and pipe supports using circumferentially guided wave modes. In practical implementations, it is not always straightforward to ensure single-mode operation and this requires consideration when interpreting results. During shear horizontal (SH) wave generation or SH guided wave interaction with geometrical changes or defects, multiple SH guided wave modes may be produced, depending on the shear wave speed, the frequency of operation, the thickness of the sample and the transducer characteristics. This paper discusses the interference patterns created as the multiple SH modes mix (for both continuous tone generation and short bursts), and the problems caused by the interference patterns on applications such as NDT. In particular, the patterns can lead to defects being missed during an NDT inspection using SH waves, and a way to circumvent this problem is suggested.

Currently, most electrophotographic printers use halftoning technique to print continuous tone images, so scanned images obtained from such hard copies are usually corrupted by screen like artifacts. In this paper, a new model of scanned halftone image is proposed to consider both printing distortions and halftone patterns. Based on this model, an adaptive filtering based descreening method is proposed to recover high quality contone images from the scanned images. Image redundancy based denoising algorithm is first adopted to reduce printing noise and attenuate distortions. Then, screen frequency of the scanned image and local gradient features are used for adaptive filtering. Basic contone estimate is obtained by filtering the denoised scanned image with an anisotropic Gaussian kernel, whose parameters are automatically adjusted with the screen frequency and local gradient information. Finally, an edge-preserving filter is used to further enhance the sharpness of edges to recover a high quality contone image. Experiments on real scanned images demonstrate that the proposed method can recover high quality contone images from the scanned images. Compared with the state-of-the-art methods, the proposed method produces very sharp edges and much cleaner smooth regions.

The spatial variation of cell size in a functionally graded cellular structure is achieved using error diffusion to convert a continuous tone image into binary form. Effects of two control parameters, greyscale value and resolution on the resulting cell size measures were investigated. Variation in cell edge length was greatest for the Voronoi connection scheme, particularly at certain parameter combinations. Relationships between these parameters and cell size were identified and applied to an example, where the target was to control the minimum and maximum cell size. In both cases there was an 8% underestimation of cell area for target regions.

In this work we detail a method that leverages the two color heads of recent low-end fused deposition modeling FDM 3D printers to produce continuous tone imagery. The challenge behind producing such two-tone imagery is how to finely interleave the two colors while minimizing the switching between print heads, making each color printed span as long and continuous as possible to avoid artifacts associated with printing short segments. The key insight behind our work is that by applying small geometric offsets, tone can be varied without the need to switch color print heads within a single layer. We can now effectively print two-tone texture mapped models capturing both geometric and color information in our output 3D prints.

Early acoustic experience changes tonal frequency tuning in the inferior colliculus (IC) and the primary auditory cortex. The contributions of IC plasticity to cortical frequency map reorganization are not entirely clear. While most cortical plasticity studies exposed animals to pulsed tones, studies of IC plasticity used either noise or a continuous tone. Here we compared the effects of repeated exposure to single-frequency tone pips on cortical and IC frequency representations in juvenile rats. We found that while tone exposure caused a long-lasting increase in cortical representations of the exposure frequency, changes to IC neurons were limited to a transient narrowing of tuning bandwidth. These results suggest that previously documented cortical frequency map reorganization does not depend on similar changes in the subcortical auditory nuclei.

Increasing evidence suggests that cognitive-control processes can be configured to optimize either persistence of information processing (by amplifying competition between decision-making alternatives and top-down biasing of this competition) or flexibility (by dampening competition and biasing). We investigated whether high-frequency binaural beats, an auditory illusion suspected to act as a cognitive enhancer, have an impact on cognitive-control configuration. We hypothesized that binaural beats in the gamma range bias the cognitive-control style towards flexibility, which in turn should increase the crosstalk between tasks in a dual-task paradigm. We replicated earlier findings that the reaction time in the first-performed task is sensitive to the compatibility between the responses in the first and the second task—an indication of crosstalk. As predicted, exposing participants to binaural beats in the gamma range increased this effect as compared to a control condition in which participants were exposed to a continuous tone of 340 Hz. These findings provide converging evidence that the cognitive-control style can be systematically biased by inducing particular internal states; that high-frequency binaural beats bias the control style towards more flexibility; and that different styles are implemented by changing the strength of local competition and top-down bias.

Image hatching (or nonphotorealistic line-art) is a technique widely used in the printing or engraving of currency. Diverse styles of brush strokes have previously been adopted for different areas of an image to create aesthetically pleasing textures and shading. Because there is no continuous tone within these types of images, a multilevel scheme is proposed, which uses different textures based on a threshold level. These textures are then applied to the different levels and are then combined to build up the final hatched image. The proposed technique allows a secret to be hidden using Visual Cryptography (VC) within the hatched images. Visual cryptography provides a very powerful means by which one secret can be distributed into two or more pieces known as shares. When the shares are superimposed exactly together, the original secret can be recovered without computation. Also provided is a comparison between the original grayscale images and the resulting hatched images that are generated by the proposed algorithm. This reinforces that the overall quality of the hatched scheme is sufficient. The Structural SIMilarity index (SSIM) is used to perform this comparison.

There are very complicated nonlinear relations between input value and presentation color of digital prepress devices, as well as in the gamut mapping among different devices. Not all the relations can be described by linear model properly. In recent years, neural network and black box theory are introduced to describe the relations. In this research, the printer gamut is divided into micro space sections, a relative colorimetric rending mapping method is proposed based on BP neural network theory to complete the gamut mapping from monitor gamut to color printer. Experimental results show that the model reduces the color error of color blocks between the two devices, and the color rendering method can be used for gamut mapping of image with less continuous tone.

Halftoning schemes are developed for reserving the quality after transforming continuous tone images to binary images under human vision, and are widely used in printing and outputting. Modern halftoning schemes focus on transforming forward and backward the two kinds of images, but basic operations for halftone images like rescaling, blurring and sharpening are still insufficient. In this paper, a novel halftone image resampling scheme based on interpolation and error-diffusion is proposed. The proposed scheme can shrink and magnify halftone images to any desired scale and does not limit the source of the halftone images. To obtain the best performance, an evaluation tool is proposed to test various combinations of interpolation techniques and error-diffusion kernels. Due to the lack of evaluation research, the evaluation metric called light invariance is developed to evaluate the consistency before and after resampling. By the proposed scheme, resampling halftone images does not need transforming to and back from continuous-tone images anymore.

Pure-tone testing is the primary audiological test procedure for the differential diagnosis of hearing loss and hearing disorders in school-aged children. No research is currently available internationally for children's responses to continuous versus pulsed pure-tones. The aim of this exploratory investigation was to compare the performance of a group of school-aged children on continuous versus pulsed pure-tone audiometry. The aims were to determine whether a threshold difference existed between continuous versus pulsed pure-tones and to record whether a listener preference existed between continuous versus pulsed tones for the frequency range of 125 to 8 000 Hz. Eighteen children (36 ears) aged between 8-12 years, participated in a hearing evaluation as well as in a brief three-question interview. Descriptive statistics viz. average threshold, mean difference and standard deviation of thresholds were used to analyse data. Listeners' perceived preferences were calculated in percentages and reasons for preferring one signal over another was analysed qualitatively. Although the automatically pulsed tone threshold (averaged a cross the frequencies tested) was lower than for the continuous tone, the difference was only 0.2 dB in the left ear and 0.5 dB in the right ear. This small difference is not important in clinical applications for which 5dB in crements are used in pure-tone audiometry. Where a listener preference was indicated, however, the continuous tones w ere preferred over pulsed tones by 56% percent of subjects. These findings differ from similar studies involving adults. This revealed that children may yield different preferences during pure-tone testing than adults and that these preferences should be taken into consideration during testing.

Word segmentation, that is, discovering the boundaries between words that are embedded in a continuous speech stream, is an important faculty for language learners; humans solve this task partly by calculating transitional probabilities between sounds. Behavioral and ERP studies suggest that detection of sequential probabilities (statistical learning) plays an important role in the process of word segmentation. To identify the brain regions that are engaged during statistical segmentation of tone sequences, we measured changes in blood oxygenation using multichannel near-infrared spectroscopy (NIRS) while participants were exposed to continuous, nonlinguistic auditory sequences, the elements of which were organized into fixed "tone-words," but sequenced in random order. We habituated the participants to the tone-words by presenting the stimuli in a training phase prior to the presentation of the continuous tone sequences. After training, the statistical sequences, which included six tone-words, and the random sequences, which included 11 tones in random order, were presented alternately during NIRS recording. A large increase in the oxygenated hemoglobin concentration was observed in the left inferior frontal cortex (IFC) during the statistical sequence condition, but not during the random sequence condition. This suggests that the left IFC plays an important role in statistical segmentation of tone sequences, during which participants deploy the knowledge obtained in the training phase on the subsequent continuous sequence.

Tone recognition has been a basic but important task for speech recognition and assessment of tonal languages, such as Mandarin Chinese. Most previously proposed approaches adopt a two-step approach where syllables within an utterance are identified via forced alignment first, and tone recognition using a variety of classifiers---such as neural networks, Gaussian mixture models (GMM), hidden Markov models (HMM), support vector machines (SVM)---is then performed on each segmented syllable to predict its tone. However, forced alignment does not always generate accurate syllable boundaries, leading to unstable voiced-unvoiced detection and deteriorating performance in tone recognition. Aiming to alleviate this problem, we propose a robust approach called Tone Recognition Using Extended Segments (TRUES) for HMM-based continuous tone recognition. The proposed approach extracts an unbroken pitch contour from a given utterance based on dynamic programming over time-domain acoustic features of average magnitude difference function (AMDF). The pitch contour of each syllable is then extended for tri-tone HMM modeling, such that the influence from inaccurate syllable boundaries is lessened. Our experimental results demonstrate that the proposed TRUES achieves 49.13% relative error rate reduction over that of the recently proposed supratone modeling, which is deemed the state of the art of tone recognition that outperforms several previously proposed approaches. The encouraging improvement demonstrates the effectiveness and robustness of the proposed TRUES, as well as the corresponding pitch determination algorithm which produces unbroken pitch contours.

A hybrid neural network based method for halftoning and inverse halftoning of digital images is presented. The halftone image is performed by single-layer perceptron neural network (SLPNN), and its corresponding continuous-tone image is reconstructed by radial-basis function neural network (RBFNN). The combined training procedure produces halftone images and the corresponding continuous tone images at the same time. The PSNR performance and visual image quality of these contone images achieved is comparable to the well-known inverse halftoning methods. The resultant halftone images compared with the error diffusion halftone are visually good, too. Furthermore, we apply different kinds of halftone images to a bi-level image compression method, called Block Arithmetic Coding for Image Compression (BACIC), which is better than the current facsimile methods.