High Frequency Emphasis Research Articles

Timbral brightness perception investigated through multimodal interference

Brightness is among the most studied aspects of timbre perception. Psychoacoustically, sounds described as “bright” versus “dark” typically exhibit a high versus low frequency emphasis in the spectrum. However, relatively little is known about the neurocognitive mechanisms that facilitate these metaphors we listen with. Do they originate in universal magnitude representations common to more than one sensory modality? Triangulating three different interaction paradigms, we investigated using speeded classification whether intramodal, crossmodal, and amodal interference occurs when timbral brightness, as modeled by the centroid of the spectral envelope, and pitch height/visual brightness/numerical value processing are semantically congruent and incongruent. In four online experiments varying in priming strategy, onset timing, and response deadline, 189 total participants were presented with a baseline stimulus (a pitch, gray square, or numeral) then asked to quickly identify a target stimulus that is higher/lower, brighter/darker, or greater/less than the baseline after being primed with a bright or dark synthetic harmonic tone. Results suggest that timbral brightness modulates the perception of pitch and possibly visual brightness, but not numerical value. Semantically incongruent pitch height-timbral brightness shifts produced significantly slower reaction time (RT) and higher error compared to congruent pairs. In the visual task, incongruent pairings of gray squares and tones elicited slower RTs than congruent pairings (in two experiments). No interference was observed in the number comparison task. These findings shed light on the embodied and multimodal nature of experiencing timbre.

Learning Spatial-Frequency Transformer for Visual Object Tracking

Recently, some researchers have begun to adopt the Transformer to combine or replace the widely used ResNet as their new backbone network. As the Transformer captures the long-range relations between pixels well using the self-attention scheme, which complements the issues caused by the limited receptive field of CNN. Although their trackers work well in regular scenarios, they simply flatten the 2D features into a sequence to better match the Transformer. We believe these operations ignore the spatial prior of the target object, which may lead to sub-optimal results only. In addition, many works demonstrate that self-attention is actually a low-pass filter, which is independent of input features or keys/queries. That is to say, it may suppress the high-frequency component of the input features and preserve or even amplify the low-frequency information. To handle these issues, in this paper, we propose a unified Spatial-Frequency Transformer that models the Gaussian spatial Prior and High-frequency emphasis Attention (GPHA) simultaneously. To be specific, Gaussian spatial prior is generated using dual Multi-Layer Perceptrons (MLPs) and injected into the similarity matrix produced by multiplying Query and Key features in self-attention. The output will be fed into a softmax layer and then decomposed into two components, i.e., the direct and high-frequency signal. The low- and high-pass branches are rescaled and combined to achieve all-pass, therefore, the high-frequency features will be protected well in stacked self-attention layers. We further integrate the Spatial-Frequency Transformer into the Siamese tracking framework and propose a novel tracking algorithm termed SFTransT. The cross-scale fusion based SwinTransformer is adopted as the backbone, and also a multi-head cross-attention module is used to boost the interaction between search and template features. The output will be fed into the tracking head for target localization. Extensive experiments on short-term and long-term tracking benchmarks all demonstrate the effectiveness of our proposed framework. Source code will be released at https://github.com/Tchuanm/SFTransT.git.

Construction of fuzzy radial basis function neural network model for diagnosing prostate cancer

In this paper, we propose a construction of fuzzy radial basis function neural network model for diagnosing prostate cancer. A fuzzy radial basis function neural network (fuzzy RBFNN) is a hybrid model of logical fuzzy and neural network. The fuzzy membership function of the fuzzy RBFNN model input is developed using the triangle function. The fuzzy C-means method is applied to estimate the center and the width parameters of the radial basis function. The weight estimation is performed by various ways to gain the most accurate model. A singular value decomposition (SVD) is exploited to address this process. As a comparison, we perform other ways including back propagation and global ridge regression. The study also promotes image preprocessing using high frequency emphasis filter (HFEF) and histogram equalization (HE) to enhance the quality of the prostate radiograph. The features of the textural image are extracted using the gray level co-occurrence matrix (GLCM) and gray level run length matrix (GLRLM). The experiment results of fuzzy RBFNN are compared to those of RBFNN model. Generally, the performances of fuzzy RBFNN surpass the RBFNN in all accuracy calculation. In addition, the fuzzy RBFNN-SVD demonstrates the most accurate model for prostate cancer diagnosis.

Survey on Cardiomegaly Detection with Enhanced X-Ray Using Deep Learning

Cardiomegaly is an augmented (enlarged) heart. It is not a disease merely a sign of another condition. Cardiomegaly in the early stages, which is less severe, is called mild Cardiomegaly. These complications may give rise to conditions like Blood clots, Cardiac Arrest and sudden death, Heart failure, Heart murmur. Hence, detecting these kinds of states in the early stages helps in improving medications and reduce complications. In this paper, we will present various approaches available to detect this development using Deep learning and offer an automated system to detect the presence of Cardiomegaly in a patient. For the computerized system, we are using deep learning concepts such as U-Net and VGG16 and image enhancement (image prepossessing is required) methods like Unsharp Masking, Contrast Limited Adaptive Histogram, High-Frequency Emphasis Filtering. Accurate measure of CTR (cardiothoracic ratio) calculations can effectively diagnose the presence of Cardiomegaly in a patient.

Single-image de-raining using joint filter and multi-scale deep alternate-connection dense network

This paper aims at developing an effective approach to remove rain layer from an individual image. The proposed is a two-stage method named JF-MADN consisting of a Joint Filter (JF) extracting high frequency details from rainy image and a Multi-scale deep Alternate-connection Dense Network (MADN) separating rain streaks in a coarse-to-fine manner. In the first stage, rainy image is enhanced first through a high frequency emphasis filter. Next, a gradient operator is deduced based on trigonometric function and Laplacian operator to generate rain gradient prior. By combining it with rain dark channel prior, a rain-prior weighted statistic order filter could be constructed to separate high and low frequency components of rainy image. Then, the result image through a guided filter followed by would be subtracted by the original low frequency component to obtain residuals for final operation with high frequency component to generate high quality initial rain layer. In the second stage, by adopting multi-scale mechanism, this study develops two new alternate-connected fully convolutional DenseNets in different scale, WNet-129 and NNet-96, to implement de-raining. By combining element-wise addition in our alternate-connection structure with original concatenation in fully convolutional DenseNet, features could be constantly connected in our network. Experimental results demonstrate that the proposed framework outperforms existing single-image de-raining algorithms on testing rainy images. Code available at: http://www.imagetech-polynomials.com/derain.html.

Development of an 80-word clinical version of the modified rhyme test (MRT80).

The modified rhyme test [MRT; House, Williams, Hecker, and Kryter. (1965). J. Acoust. Soc. Am. 37, 158-166] is a widely used test for measuring the intelligibility of communication systems [ANSI (2009). S3.2 (American National Standards Institute, New York)] but has never gained widespread acceptance as a clinical test of speech intelligibility for listeners who are hearing impaired (HI). In this study, a clinical version of the MRT consisting of two 80-word lists was developed and tested on 2394 service members with varying levels of hearing loss. The test used a factorial design incorporating two speech levels [70 and 78 dB sound pressure level (SPL)], two signal-to-noise ratios (+4 and -4 dB), and two binaural conditions (diotic and binaural). High-frequency emphasis reduced the impact of audibility for HI listeners, focusing the test on the distortion component of hearing loss. The results show that listeners with normal hearing (NH) obtained an average score of 80% correct on the MRT80 test. Listeners with a moderate hearing impairment scored an average of 70% correct. The overall level had little impact on performance for either NH or HI listeners. The results demonstrate that the MRT80 test could be a useful test to assess the distortion effects of hearing loss on speech intelligibility, particularly in cases where it is desirable to use a closed-set test for automatic administration.

Image Enhancement for Tuberculosis Detection Using Deep Learning

The latest World Health Organization’s (WHO) study on 2018 is showing that about 1.5 million people died and around 10 million people are infected with tuberculosis (TB) each year. Moreover, more than 4,000 people die every day from TB. A number of those deaths could have been stopped if the disease was identified sooner. In the recent literature, important work can be found on automating the diagnosis by applying techniques of deep learning (DL) to the medical images. While DL has yielded promising results in many areas, comprehensive TB diagnostic studies remain limited. DL requires a large number of high-quality training samples to yield better performance. Due to the low contrast of TB chest x-ray (CXR) images, they are often is in poor quality. This work assesses the effect of image enhancement on performance of DL technique to address this problem. The employed image enhancement algorithm was able to highlight the overall or local characteristics of the images, including some interesting features. Specifically, three image enhancement algorithms called Unsharp Masking (UM), High-Frequency Emphasis Filtering (HEF) and Contrast Limited Adaptive Histogram Equalization (CLAHE), were evaluated. The enhanced image samples were then fed to the pre-trained ResNet and EfficientNet models for transfer learning. In a TB image dataset, we achieved 89.92% and 94.8% of classification accuracy and AUC (Area Under Curve) scores, respectively. All the results are obtained using Shenzhen dataset, which are available in the public domain.

A prospective case study of high boost, high frequency emphasis and two-way diffusion filters on MR images of glioblastoma multiforme.

Glioblastoma multiforme (GBM) appears undifferentiated and non-enhancing on magnetic resonance (MR) imagery. As MRI does not offer adequate image quality to allow visual discrimination of the boundary between GBM focus and perifocal vasogenic edema, surgical and radiotherapy planning become difficult. The presence of noise in MR images influences the computation of radiation dosage and precludes the edge based segmentation schemes in automated software for radiation treatment planning. The performance of techniques meant for simultaneous denoising and sharpening, like high boost filters, high frequency emphasize filters and two-way anisotropic diffusion is sensitive to the selection of their operational parameters. Improper selection may cause overshoot and saturation artefacts or noisy grey level transitions can be left unsuppressed. This paper is a prospective case study of the performance of high boost filters, high frequency emphasize filters and two-way anisotropic diffusion on MR images of GBM, for their ability to suppress noise from homogeneous regions and to selectively sharpen the true morphological edges. An objective method for determining the optimum value of the operational parameters of these techniques is also demonstrated. Saturation Evaluation Index (SEI), Perceptual Sharpness Index (PSI), Edge Model based Blur Metric (EMBM), Sharpness of Ridges (SOR), Structural Similarity Index Metric (SSIM), Peak Signal to Noise Ratio (PSNR) and Noise Suppression Ratio (NSR) are the objective functions used. They account for overshoot and saturation artefacts, sharpness of the image, width of salient edges (haloes), susceptibility of edge quality to noise, feature preservation and degree of noise suppression. Two-way diffusion is found to be superior to others in all these respects. The SEI, PSI, EMBM, SOR, SSIM, PSNR and NSR exhibited by two-way diffusion are 0.0016 ± 0.0012, 0.2049 ± 0.0187, 0.0905 ± 0.0408, 2.64 × 1012 ± 1.6 × 1012, 0.9955 ± 0.0024, 38.214 ± 5.2145 and 0.3547 ± 0.0069, respectively.

An Enhanced Algorithm for an Optimal High-Frequency Emphasis Filter Based on Fuzzy Logic for Chest X-Ray Images

The chest X-ray image cannot be focused in the same manner that optical lenses are and the resultant image generally tends to be slightly blurred. Therefore, the methods to improve the quality of chest X-ray image have been studied. In this paper, the inherent noises of the input images are suppressed by adding the Laplacian image to the original. First, the chest X-ray image using an Gaussian high pass filter and an optimal high frequency emphasis filter has shown improvements in the edges and contrast of flat areas. Second, using fuzzy logic_histogram equalization, each pixel of the chest X-ray image shows the normal distribution of intensities that are not overexposed. As a result, the proposed method has shown the enhanced edge and contrast of the images with the noise canceling effect.

An Optimal Method to Improve the Visual Quality of Medical Images

AbstractAs the visual quality of X-ray images is a critical reference for the accuracy of the clinical diagnosis, the methods toimprove the quality of X-ray images have been investigated. Among many existing methods, using frequency domainfilter is a very powerful method to improve the visual quality of images. In this paper, the inherent noises of the inputimages are suppressed by adding the Laplacian image to the subjected image. The medical X-ray images using the optimalhigh pass filter has shown improved edges. Further, the optimal high frequency emphasis filter has shown the improvedcontrast of flat areas by using the result image from the optimal high pass filter. Also the resulting images of the globalcontrast have improved by the histogram equalization. As a result, the proposed methods have shown enhanced contrastand edges of the images with noise canceling effect. Keywords: Laplacian Image, High Pass Filter, High Frequency Emphasis Filter 1. Introduction Even the smallest abnormality should not be over-looked in the correct X-ray diagnosis because the earlytreatment of this abnormality is critical for the recoveryof patients. To identify abnormalities in lung tissue thatare roughly spherical with round opacity and a diameterof up to approximately 30 mm

The relationship between hearing aid frequency response and acceptable noise level in patients with sensorineural hearing loss.

Background:When fitting hearing aid as a compensatory device for an impaired cochlea in a patient with sensorineural hearing loss (HL), it is needed to the effective and efficient frequency response would be selected regarding providing the patient's perfect speech perception. There is not any research about the effects of frequency modifications on speech perception in patients with HL regarding the cochlear desensitization. The effect (s) of modifications in frequency response of hearing aid amplification on the results of acceptable noise level (ANL) test is the main aim of this study.Materials and Methods:The amounts of ANL in two conditions of linear amplification (high frequency emphasis [HFE] and mid frequency emphasis [MFE]) were measured. Thirty-two male subjects who participated in this study had the moderate to severe sensorineural HL.Results:There was not any significant difference between ANL in linear amplification of hearing aid with HFE frequency response and ANL in linear amplification of hearing aid with MFE frequency response.Conclusion:The gain modification of frequency response not only does not affect the patient's performance of speech intelligibility in ANL test. This indicates that we need to note to the cochlear desensitization phenomenon when fitting hearing aid as a compensatory device for an impaired cochlea in a patient. The cochlear desensitization has not been considered properly in hearing aid fitting formula which is needed to be explored more about the bio-mechanisms of impaired cochlea.

Log-hybrid architecture for tonal correction combined with modified un-sharp masking filter algorithm for colour image enhancement

A novel colour image enhancement architecture combining a Tonal Correction algorithm and a high frequency emphasis circular symmetric filter is implemented in hardware forming a colour image contrast enhancement system of low complexity. The architecture utilizes efficient log-domain calculations, resulting in multiplier-less operations and eliminates costly hardware division. Furthermore, the modularity of the fundamental design enables its ease of incorporation into larger and more complex designs with little modification. The design can be used for both grayscale and RGB colour images by processing each channel individually and the system can serve as a post enhancement module for improved colour rendition. The unit can be configured for the enhancement of over-exposed (too bright) and under-exposed (too dark) images based on adjustable parameters. The architecture easily fits on a basic Spartan III FPGA for low-cost realization evaluation and a Virtex 5 ML505 for real-time use. Results of the implementation and simulation are provided and compared with the software version and show that the system produces images with improved colour contrast and rendition compared to well known image enhancement algorithms such as RGB colour Homomorphic filtering. It also performs better than the Homomorphic filter and the MSRCR for faded colour images.

고주파 강조필터를 이용한 의료영상의 화질향상을 위한 최적화 방법

The visual quality of medical image is an important factor for diagnosis accuracy. Therefore, the methods to improve the quality of medical image have studied. Among them, frequency domain filter is very powerful method to impove the visual quality of image. In this paper, the X-ray medical image using optimized high-pass filter was improved edges. The result image was improved edge and contrast of flat area using optimized high frequency emphasis filter. At last, the result image is to minimize the noise using the minimum mean square error(MMSE) filter. As a result, the proposed method has enhanced contrast and edge of the image in the contrast of existing filters, with the noise canceling effect.

Nuts & Bolts

Nuts & Bolts

Cochlear Dead Regions in Typical Hearing Aid Candidates: Prevalence and Implications for Use of High-Frequency Speech Cues

This study had two purposes. The first was to assess the prevalence of cochlear dead regions (DRs) among listeners with moderate to severe hearing loss that is typical of a large proportion of adult hearing aid wearers. The second was to determine whether subjects who tested positive for DRs differed from those without DRs in their ability to utilize high-frequency speech cues in a laboratory test. One hundred and seventy adults (307 ears) were tested for DRs at frequencies from 0.5 to 4 kHz using the threshold equalizing noise (HL) test. Speech recognition ability was measured for high-frequency emphasis (HFE) stimuli and for low-pass filtered HFE (HFE-LP) stimuli using the Quick Speech In Noise test. Results obtained from the HFE and HFE-LP conditions were compared to examine changes in word recognition when more speech cues were provided above 2.5 kHz. Possible effects of audiogram differences between DR-no and DR-yes groups were examined by estimating the change in audibility for the two Quick Speech In Noise conditions using calculated differences in Speech Intelligibility Index for each condition for every subject. Thirty-one percent of subjects (23% of ears) were found to have a DR at one or more test frequencies. Sixty-eight percent of subjects who tested positive for DR had DRs in one ear only. DRs were most prevalent at frequencies above 1.5 kHz. Comparison of word recognition scores obtained with the HFE and HFE-LP conditions revealed that, on average, both groups scored significantly better when more high-frequency cues were provided. The magnitude of the benefit was small for both groups, but the computed effect size was larger for listeners without DRs than for those with DRs. Further, subjects with contiguous DRs at 2 to 3 frequencies obtained less benefit than subjects with DRs at isolated frequencies. It was determined that the improved audibility of high-frequency cues in the HFE condition was significantly less for listeners with DRs, and this accounted for some, but not all, of the difference in effect sizes. Although about one-third of listeners with flat or sloping moderate to severe hearing losses tested positive for at least one DR, there was no evidence to support a proposal for reducing high-frequency gain in hearing aid fittings for these types of listeners. Making high frequencies more audible was helpful, on average, regardless of DR status. It is recommended that field trials be undertaken in which subjects with and without DRs wear hearing aids in daily life. This type of study would produce higher level evidence about best practice in hearing aid fitting for patients with flat or sloping moderate to severe hearing loss who test positive for DRs.

A Fog-Removing Treatment Based on Combining High-Frequency Emphasis Filtering and Histogram Equalization

Fog may cause the impairment of image as well as the decrease in the distinguish ability. The present paper is to get rid of the weather’s influence from the impaired image. According to Retinex theory and exponential relationship between the degradation of the image and the depths of the scene points, it puts forward a fog-removing treatment based on combining high-frequency emphasis filtering and histogram equalization .Firstly, obtain the padding parameters and fill it. Secondly, filter the impaired image using Butterworth highpass filter of order 2. Through the padding parameters, Highpass filtering is not overly sensitive to the value of cutoff frequencies, as long as the radius of the filter is not so small that frequencies near the origin of the transform are passed. In which the gray-level tonality due to the low-frequency components was retained. Lastly, histogram balanced the image gotten last step. The simulation result based on Matlab shows his algorithm can effectively improve the visual effect scene under the condition of mist.

Frequency Domain Pseudo-color to Enhance Ultrasound Images

In digital image processing, image enhancement is employed to give a better look to an image. Color is one of the best ways to visually enhance an image. Pseudo-color refers to coloring an image by mapping gray scale values to a three dimensional color space. In this paper we used a pseudo-color technique in frequency domain to enhance ultrasound images. We used three different types of transforms to do this. These are the Fourier transform, Discrete Cosine transform and Walsh- Hadamard transform. After obtaining these pseudo-color images, we applied a high frequency emphasis filter or histogram stretch as a post process. In this paper we used a subjective study to compare images. First we compared pseudo-color images to their original monochrome images. Secondly, we compared all the three different types of transforms. Lastly, we compared the post processing techniques.

Digital sound system for motion pictures with analog sound track emulation

A signal conditioner (116) is provided for the output of a digital sound source (110) in a dual digital/analog motion picture theater sound system so that the digitally derived audio signal emulates the analog sound track (58) signal. This allows the digitial source to be easily integrated into the overall sound system, without displacing the existing analog sound track system. The theater sound playback mechanism (124) is provided with the conditioned audio signal when the digital sound source is actuated, and receives the analog sound track signal at other times. The conditioning circuit includes an adjustable impedance (P1, P2) to match the current from the digital sound source to the current from the analog sound track reader (102), a high frequency variable signal adjuster (146) that is complementary to a high frequency emphasis built into the theater playback system, and an optional subwoof output (118).

Method and means for processing speech

First Page

Speech perception through various signal processings

Modified rhyme test (MRT) and the City University of New York nonsense syllables test (CUNY) were recorded through various speech processings: (1) amplitude compression for a 10:1 compression ratio (CR) with attack and release times 10 and 90 ms, respectively; (2) amplitude compression plus two types of peak clippings (CLIP); (3) amplitude compression plus high‐frequency emphasis (HFE) with different attack and release times; and (4) three‐channel multiband compression (MBC) in which the CR in each channel was adjusted on the basis of the subject's dynamic range. The compression threshold was set 25 dB below the average maximum RMS level of the vowels of test items in the MRT or CUNY. The processed signal was amplified with individually adjusted frequency responses and delivered to the listeners via a circumaural earphone. The presentation of speech was always at the comfortable level for each individual listener. All the testings were done in quiet. The MRT mean scores in percent correct did not differ a...